username, display name, profile image, location, description
EXTRACTIVE
username, display name, profile image, location and description
EXTRACTIVE
We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively. The username is a unique handle or screen name associated with each twitter user that appears on the profile URL and is used to communicate with each other on Twitter. Some username examples are $@narendramodi$, $@RahulGandhi$ etc. The display name on the other hand, is merely a personal identifier that is displayed on the profile page of a user, e.g. `Narendra Modi', `Rahul Gandhi', etc. The description is a string to describe the account, while the location is user-defined profile location. We considered the 5 profile attributes as stated above since these attributes are key elements of a users identity and saliently define users likes and values BIBREF2.
We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively. The username is a unique handle or screen name associated with each twitter user that appears on the profile URL and is used to communicate with each other on Twitter. Some username examples are $@narendramodi$, $@RahulGandhi$ etc. The display name on the other hand, is merely a personal identifier that is displayed on the profile page of a user, e.g. `Narendra Modi', `Rahul Gandhi', etc. The description is a string to describe the account, while the location is user-defined profile location. We considered the 5 profile attributes as stated above since these attributes are key elements of a users identity and saliently define users likes and values BIBREF2.
1909.10012
Introduction </s> User Ġprofiles Ġon Ġsocial Ġmedia Ġplatforms Ġserve Ġas Ġa Ġvirtual Ġintroduction Ġof Ġthe Ġusers . ĠPeople Ġoften Ġmaintain Ġtheir Ġonline Ġprofile Ġspace Ġto Ġreflect Ġtheir Ġlikes Ġand Ġvalues . ĠFurther , Ġhow Ġusers Ġmaintain Ġtheir Ġprofile Ġhelps Ġthem Ġdevelop Ġrelationship Ġwith Ġcoveted Ġaudience ĠB IB REF 0 . ĠA Ġuser Ġprofile Ġon ĠTwitter Ġis Ġcomposed Ġof Ġseveral Ġattributes Ġwith Ġsome Ġof Ġthe Ġmost Ġprominent Ġones Ġbeing Ġthe Ġprofile Ġname , Ġscreen Ġname , Ġprofile Ġimage , Ġlocation , Ġdescription , Ġfollowers Ġcount , Ġand Ġfriend Ġcount . ĠWhile Ġthe Ġscreen Ġname , Ġdisplay Ġname , Ġprofile Ġimage , Ġand Ġdescription Ġidentify Ġthe Ġuser , Ġthe Ġfollower Ġand Ġfriend Ġcounts Ġrepresent Ġthe Ġuser 's Ġsocial Ġconnectivity . ĠProfile Ġchanges Ġmight Ġrepresent Ġidentity Ġchoice Ġat Ġa Ġsmall Ġlevel . ĠHowever , Ġprevious Ġstudies Ġhave Ġshown Ġthat Ġon Ġa Ġbroader Ġlevel , Ġprofile Ġchanges Ġmay Ġbe Ġan Ġindication Ġof Ġa Ġrise Ġin Ġa Ġsocial Ġmovement ĠB IB REF 1 , ĠB IB REF 2 . </s> In Ġthis Ġwork , Ġwe Ġconduct Ġa Ġlarge - scale Ġstudy Ġof Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġusers Ġon ĠTwitter Ġduring Ġthe Ġ2019 Ġgeneral Ġelections Ġin ĠIndia . ĠThese Ġelections Ġare Ġof Ġinterest Ġfrom Ġthe Ġperspective Ġof Ġsocial Ġmedia Ġanalysis Ġfor Ġmany Ġreasons . ĠFirstly , Ġthe Ġgeneral Ġelections Ġin ĠIndia Ġwere Ġheld Ġin Ġ7 Ġphases Ġfrom Ġ11 th ĠApril , Ġ2019 Ġto Ġ19 th ĠMay , Ġ2019 . ĠThus , Ġthe Ġelections Ġserve Ġas Ġa Ġrich Ġsource Ġof Ġdata Ġfor Ġsocial Ġmovements Ġand Ġchanging Ġpolitical Ġalign ments Ġthroughout Ġthe Ġtwo Ġmonths . ĠSecondly , Ġthe Ġincrease Ġin Ġthe ĠInternet Ġuser - base Ġand Ġwider Ġadoption Ġof Ġsmartphones Ġhas Ġmade Ġsocial Ġmedia Ġoutreach Ġan Ġessential Ġpart Ġof Ġthe Ġbroader Ġelection Ġcampaign . ĠThus , Ġthere Ġexists Ġa Ġconsiderable Ġvolume Ġof Ġpolitical Ġdiscourse Ġdata Ġthat Ġcan Ġbe Ġmined Ġto Ġgain Ġinteresting Ġinsights . ĠTwitter Ġwas Ġwidely Ġused Ġfor Ġpolitical Ġdiscourse Ġeven Ġduring Ġthe Ġ2014 Ġgeneral Ġelections ĠB IB REF 3 Ġand Ġthe Ġnumber Ġwas Ġbound Ġto Ġincrease Ġthis Ġyear . ĠA Ġpiece Ġof Ġevidence Ġin Ġfavor Ġof Ġthis Ġargument Ġis Ġthe Ġenormous Ġ49 Ġmillion Ġfollowers Ġthat ĠNarendra ĠModi , Ġthe ĠPrime ĠMinister Ġof ĠIndia Ġhas Ġon ĠTwitter . </s> We Ġbelieve Ġprofile Ġattributes Ġhave Ġsignificant Ġpotential Ġto Ġunderstand Ġhow Ġsocial Ġmedia Ġplayed Ġa Ġvital Ġrole Ġin Ġthe Ġelection Ġcampaign Ġby Ġpolitical Ġparties Ġas Ġwell Ġas Ġthe Ġsupporters Ġduring Ġthe Ġ# L ok Sab ha Elect ions 2019 . ĠA Ġprominent Ġexample Ġof Ġthe Ġuse Ġof Ġprofile Ġchanges Ġfor Ġpolitical Ġcampaigning Ġis Ġthe Ġ# Main B hi Ch ow kid ar Ġcampaign Ġlaunched Ġby Ġthe ĠBh art iya ĠJ anta ĠParty Ġ( B JP ) Ġduring Ġthe Ġ# L ok Sab ha Elect ions 2019 . ĠIn ĠFigure ĠFIG REF 2 , Ġwe Ġshow Ġthe Ġfirst Ġoccurrence Ġof Ġ# Main B hi Ch ow kid ar Ġcampaign Ġon ĠTwitter Ġwhich Ġwas Ġlaunched Ġin Ġresponse Ġto Ġan Ġopposition Ġcampaign Ġcalled ĠâĢ ľ Ch ow kid ar Ġch or Ġha i âĢ Ŀ Ġ( The Ġwatch man Ġis Ġa Ġthief ). ĠOn Ġ17 th ĠMarch Ġ2019 , Ġthe ĠIndian ĠPrime ĠMinister ĠNarendra ĠModi Ġstepped Ġup Ġthis Ġcampaign Ġand Ġadded ĠChow kid ar Ġ( Watch man ) Ġto Ġhis Ġdisplay Ġname . ĠThis Ġspurred Ġoff Ġa Ġsocial Ġmovement Ġon ĠTwitter Ġwith Ġusers Ġfrom Ġacross Ġthe Ġnation Ġand Ġpolitical Ġspectrum Ġupdating Ġtheir Ġprofiles Ġon ĠTwitter Ġby Ġprefix ing ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġto Ġtheir Ġdisplay Ġname ĠB IB REF 4 . ĠWe Ġare Ġthus Ġinterested Ġin Ġstudying Ġthe Ġdifferent Ġfacets Ġof Ġthis Ġcampaign , Ġin Ġparticular , Ġthe Ġname Ġchanges Ġin Ġthe Ġdisplay Ġname Ġof Ġaccounts , Ġverified , Ġand Ġothers . </s> In ĠFigure ĠFIG REF 3 , Ġwe Ġcompare Ġthe ĠTwitter Ġprofiles Ġof Ġthe Ġtwo Ġleaders Ġof Ġnational Ġparties Ġduring ĠLok ĠSabha ĠElection , Ġ2019 . ĠThe Ġtop Ġprofile Ġbelongs Ġto Ġ@ n are nd ram odi Ġ( N are ndra ĠModi ), ĠPM Ġof ĠIndia Ġand Ġmember Ġof Ġthe Ġruling Ġparty ĠBhar ati ya ĠJan ata ĠParty Ġ( B JP ), Ġwhile Ġthe Ġbottom Ġprofile Ġbelongs Ġto Ġ@ Rah ul G and hi Ġ( Rah ul ĠGandhi ), Ġpresident Ġof ĠIndian ĠNational ĠCongress Ġ( INC ). ĠFigure ĠFIG REF 3 Ġshows Ġa Ġsnapshot Ġof Ġboth Ġleaders Ġbefore , Ġduring , Ġand Ġafter Ġthe ĠLok ĠSabha ĠElections Ġin Ġ2019 . ĠThe Ġruling Ġleader Ġshowed Ġbre vity Ġin Ġthe Ġuse Ġof Ġhis Ġprofile Ġinformation Ġfor Ġpolitical Ġcampaigning Ġand Ġchanged Ġhis Ġprofile Ġinformation Ġduring Ġand Ġafter Ġthe Ġelections , Ġwhich Ġwas Ġnot Ġthe Ġcase Ġwith Ġopposition . </s> Int ending Ġto Ġunderstand Ġthe Ġpolitical Ġdiscourse Ġfrom Ġthe Ġlens Ġof Ġprofile Ġattributes Ġon Ġsocial Ġmedia , Ġwe Ġaddress Ġthe Ġfollowing Ġquestions . </s> Introduction Ġ: :: ĠResearch ĠQuestions </s> To Ġanalyze Ġthe Ġimportance Ġof Ġuser - prof iles Ġin Ġelections , Ġwe Ġneed Ġto Ġdistinguish Ġbetween Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġpolitical Ġaccounts Ġand Ġfollower Ġaccounts . ĠThis Ġbrings Ġus Ġto Ġthe Ġfirst Ġresearch Ġquestion : </s> R Q 1 Ġ[ Compar ison ]: ĠHow Ġoften Ġdo Ġpolitical Ġaccounts Ġand Ġcommon Ġuser Ġaccounts Ġchange Ġtheir Ġprofile Ġinformation Ġand Ġwhich Ġprofile Ġattributes Ġare Ġchanged Ġthe Ġmost . </s> The Ġpolitical Ġinclination Ġof ĠTwitter Ġusers Ġcan Ġbe Ġclassified Ġbased Ġon Ġthe Ġcontent Ġof Ġtheir Ġpast Ġtweets Ġand Ġcommunity Ġdetection Ġmethods ĠB IB REF 5 . ĠHowever , Ġthe Ġsame Ġanalysis Ġhas Ġnot Ġbeen Ġextended Ġto Ġthe Ġstudy Ġof Ġuser - prof iles Ġin Ġdetail . ĠWe Ġthus , Ġstudy Ġthe Ġfollowing : </s> R Q 2 Ġ[ Political ĠIn cl ination ]: ĠDo Ġusers Ġreveal Ġtheir Ġpolitical Ġinclination Ġon ĠTwitter Ġdirectly / ind irect ly Ġusing Ġtheir Ġuser Ġprofiles Ġonly ? </s> Beh avior Ġcontag ion Ġis Ġa Ġphenomenon Ġwhere Ġindividuals Ġadopt Ġa Ġbehavior Ġif Ġone Ġof Ġtheir Ġopinion Ġleaders Ġadop ts Ġit ĠB IB REF 6 . ĠWe Ġanalyze Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġaccounts Ġbased Ġon Ġthe ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġmovement Ġwith Ġthe Ġintent Ġto Ġfind Ġbehavior Ġcontag ion Ġof Ġan Ġopinion Ġleader . ĠThus , Ġour Ġthird Ġresearch Ġquestion Ġis : </s> R Q 3 Ġ[ Beh avior ĠCont ag ion ]: ĠCan Ġprofile Ġchange Ġbehavior Ġbe Ġc atered Ġto Ġbehavior Ġcontag ion Ġeffect Ġwhere Ġfollowers Ġtend Ġto Ġadopt Ġan Ġopinion Ġas Ġtheir Ġopinion Ġleader Ġadop ts Ġit ? </s> We Ġare Ġinterested Ġin Ġknowing Ġthe Ġcommon Ġattributes Ġof Ġthe Ġpeople Ġwho Ġtook Ġpart Ġin Ġthe Ġch ow kid ar Ġmovement . ĠTo Ġcharacterize Ġthese Ġusers , Ġwe Ġneed Ġto Ġanalyze Ġthe Ġmajor Ġtopic Ġpatterns Ġthat Ġthese Ġusers Ġengage Ġin . ĠThus , Ġwe Ġask Ġthe Ġquestion : </s> R Q 4 Ġ[ Topic ĠMod elling ]: ĠWhat Ġare Ġthe Ġmost Ġdiscussed Ġtopics Ġamongst Ġthe Ġusers Ġthat Ġwere Ġpart Ġof Ġthe ĠChow kid ar Ġcampaign ? </s> Introduction Ġ: :: ĠContributions </s> In Ġsummary , Ġour Ġmain Ġcontributions Ġare : </s> We Ġcollect Ġdaily Ġand Ġweekly Ġprofile Ġsnapshots Ġof Ġ$ 1 , 293 $ Ġpolitical Ġhandles Ġon ĠTwitter Ġand Ġover Ġ55 Ġmillion Ġof Ġtheir Ġfollowers Ġrespectively Ġover Ġa Ġperiod Ġof Ġtwo Ġmonths Ġduring Ġand Ġafter Ġthe Ġ# L ok Sab ha Elect ions 2019 . ĠWe Ġwill Ġmake Ġthe Ġdata Ġand Ġcode Ġavailable Ġon Ġour Ġwebsite . </s> We Ġanalyze Ġthe Ġpolitical Ġand Ġfollower Ġhandles Ġfor Ġprofile Ġchanges Ġover Ġthe Ġsnapshots Ġand Ġshow Ġthat Ġthe Ġpolitical Ġhandles Ġengage Ġin Ġmore Ġprofile Ġchange Ġbehavior . ĠWe Ġalso Ġshow Ġthat Ġcertain Ġprofile Ġattributes Ġare Ġchanged Ġmore Ġfrequently Ġthan Ġothers Ġwith Ġsubtle Ġdifferences Ġin Ġthe Ġbehavior Ġof Ġpolitical Ġand Ġfollower Ġaccounts . </s> We Ġshow Ġusers Ġdisplay Ġsupport Ġfor Ġa Ġpolitical Ġparty Ġon ĠTwitter Ġthrough Ġboth Ġorganic Ġmeans Ġlike Ġparty Ġname Ġmentions Ġon Ġtheir Ġprofile , Ġand Ġin organic Ġmeans Ġsuch Ġas Ġshowing Ġsupport Ġfor Ġa Ġmovement Ġlike Ġ# Main B hi Ch ow kid ar . ĠWe Ġanalyze Ġthe ĠChow kid ar Ġmovement Ġin Ġdetail Ġand Ġdemonstrate Ġthat Ġit Ġillustrates Ġa Ġcontag ion Ġbehavior . </s> We Ġperform Ġtopic Ġmodelling Ġof Ġthe Ġusers Ġwho Ġtook Ġpart Ġin Ġthe ĠChow kid ar Ġmovement Ġand Ġshow Ġthat Ġthey Ġwere Ġmost Ġlikely ĠNarendra ĠModi Ġsupporters Ġand Ġmostly Ġdiscussed Ġpolitical Ġtopics Ġon ĠTwitter . </s> Dat as et ĠCollection Ġand ĠDescription </s> Our Ġdata Ġcollection Ġprocess Ġis Ġdivided Ġinto Ġtwo Ġstages . ĠWe Ġfirst Ġmanually Ġcur ate Ġa Ġset Ġof Ġpolitical Ġaccounts Ġon ĠTwitter . ĠWe Ġuse Ġthis Ġset Ġof Ġusers Ġas Ġa Ġseed Ġset Ġand Ġthen Ġcollect Ġthe Ġprofile Ġinformation Ġof Ġall Ġthe Ġfollowers Ġof Ġthese Ġhandles . </s> S eed Ġset : ĠTo Ġanswer Ġthe ĠR Q 1 , Ġwe Ġfirst Ġmanually Ġcur ate Ġa Ġset Ġof Ġ$ 2 , 577 $ ĠTwitter Ġhandles Ġof Ġpoliticians Ġand Ġofficial / un official Ġparty Ġhandles Ġinvolved Ġin ĠLok ĠSabha ĠElections Ġ2019 . ĠLet Ġthis Ġset Ġof Ġusers Ġbe Ġcalled Ġ$\ math bf Ġ{ U } $. ĠOf Ġthe Ġ$ 2 , 577 $ Ġcurated Ġhandles , Ġonly Ġ$ 1 , 293 $ Ġwere Ġverified Ġat Ġthe Ġbeginning Ġof ĠApril . ĠLet Ġthis Ġset Ġof Ġverified Ġhandles Ġbe Ġcalled Ġ$\ math bf Ġ{ P } $. ĠWe Ġcollect Ġa Ġsnapshot Ġof Ġthe Ġprofile Ġinformation Ġof Ġ$\ math bf Ġ{ P } $ Ġdaily Ġfor Ġtwo Ġmonths Ġfrom Ġ5 th ĠApril Ġ- Ġ5 th ĠJune Ġ2019 . </s> Tr acked Ġset : ĠWe Ġuse Ġthe Ġset Ġ$ P $ Ġof Ġverified ĠTwitter Ġhandles Ġas Ġa Ġseed Ġset Ġand Ġcollect Ġthe Ġprofile Ġinformation Ġof Ġall Ġtheir Ġfollowers . ĠWe Ġhypothes ize Ġthat Ġthe Ġusers Ġwho Ġfollow Ġone Ġor Ġmore Ġpolitical Ġhandles Ġare Ġmore Ġlikely Ġto Ġexpress Ġtheir Ġpolitical Ġorientation Ġon ĠTwitter . ĠThus , Ġwe Ġconsider Ġonly Ġthe Ġfollowers Ġof Ġall Ġthe Ġpolitical Ġhandles Ġin Ġour Ġwork . ĠThe Ġtotal Ġnumber Ġof Ġfollowers Ġof Ġall Ġthe Ġhandles Ġin Ġset Ġ$\ math bf Ġ{ P } $ Ġare Ġ600 Ġmillion , Ġof Ġwhich Ġonly Ġ$ 55 , 830 , 8 44 $ Ġusers Ġare Ġunique . ĠO wing Ġto ĠTwitter ĠAPI Ġconstraints , Ġwe Ġcollect Ġsnapshots Ġof Ġuser - prof iles Ġfor Ġthese Ġhandles Ġonly Ġonce Ġa Ġweek Ġover Ġthe Ġtwo Ġmonths Ġof ĠApril Ġ5 Ġ- ĠJune Ġ5 , Ġ2019 . ĠThere Ġwere Ġa Ġtotal Ġof Ġ9 Ġsnapshots , Ġof Ġwhich Ġ7 Ġcoincided Ġwith Ġthe Ġphases Ġof Ġthe Ġelection . ĠThe Ġexact Ġdates Ġon Ġwhich Ġthe Ġsnapshots Ġwere Ġtaken Ġhas Ġbeen Ġmentioned Ġin ĠTable ĠT AB REF 10 . ĠAs Ġwe Ġcollect Ġsnapshots Ġover Ġthe Ġof Ġtwo Ġmonths , Ġthe Ġnumber Ġof Ġfollowers Ġof Ġpolitical Ġhandles Ġin Ġset Ġ$ P $ Ġcan Ġincrease Ġsignificantly . ĠSimilarly , Ġa Ġsmall Ġsubset Ġof Ġfollowers Ġof Ġhandles Ġin Ġ$ P $ Ġcould Ġalso Ġhave Ġbeen Ġsuspended / de leted . ĠTo Ġmaintain Ġconsistency Ġin Ġour Ġanalysis , Ġwe Ġonly Ġuse Ġthose Ġhandles Ġfor Ġour Ġanalysis Ġthat Ġare Ġpresent Ġin Ġall Ġthe Ġ9 Ġsnapshots . ĠWe Ġcall Ġthis Ġset Ġof Ġusers Ġas Ġ$\ math bf Ġ{ S } $. ĠThe Ġset Ġ$\ math bf Ġ{ S } $ Ġconsists Ġof Ġ49 , 433 , 640 Ġunique Ġaccounts . </s> Dat as et : ĠOur Ġdataset Ġconsists Ġof Ġdaily Ġand Ġweekly Ġsnapshots Ġof Ġprofile Ġinformation Ġof Ġusers Ġin Ġset Ġ$\ math bf Ġ{ S } $ Ġand Ġ$\ math bf Ġ{ P } $ Ġrespectively . ĠWe Ġutilize Ġthe ĠTwitter ĠAPI Ġto Ġcollect Ġthe Ġprofile Ġinformation Ġof Ġusers Ġin Ġboth Ġthe Ġabove Ġsets . ĠTwitter ĠAPI Ġreturns Ġa Ġuser Ġobject Ġthat Ġconsists Ġof Ġa Ġtotal Ġof Ġ34 Ġattributes Ġlike Ġname , Ġscreen Ġname , Ġdescription , Ġprofile Ġimage , Ġlocation , Ġand Ġfollowers . ĠWe Ġfurther Ġpre - process Ġthe Ġdataset Ġto Ġremove Ġall Ġthe Ġhandles Ġthat Ġwere Ġinactive Ġfor Ġa Ġlong Ġtime Ġbefore Ġproceeding Ġwith Ġfurther Ġanalysis . </s> In Ġset Ġ$\ math bf Ġ{ S } $, Ġmore Ġthan Ġ15 Ġmillion Ġout Ġof Ġthe Ġtotal Ġ49 Ġmillion Ġusers Ġhave Ġmade Ġno Ġtweets Ġand Ġhave Ġmarked Ġno Ġfavourites Ġas Ġwell . ĠWe Ġcall Ġthe Ġremaining Ġ34 Ġmillion Ġusers Ġas ĠActive Ġusers . ĠOf Ġthe ĠActive Ġusers , Ġonly Ġ5 Ġmillion Ġusers Ġhave Ġmade Ġa Ġtweet Ġin Ġyear Ġ2019 . ĠThe Ġdistribution Ġof Ġthe Ġlast Ġtweet Ġtime Ġof Ġall Ġusers Ġin Ġset Ġ$ S $ Ġis Ġshown Ġin ĠFigure ĠFIG REF 14 . ĠThe Ġ5 Ġmillion Ġtwitter Ġusers Ġsince Ġthe Ġstart Ġof Ġ2019 Ġfall Ġin Ġfavor Ġof Ġthe Ġargument Ġthat Ġa Ġminority Ġof Ġusers Ġare Ġonly Ġresponsible Ġfor Ġmost Ġof Ġthe Ġpolitical Ġopinions ĠB IB REF 7 . ĠWe Ġbelieve Ġthere Ġis Ġa Ġcorrelation Ġbetween ĠActive Ġusers Ġand Ġusers Ġwho Ġmake Ġchanges Ġin Ġprofile Ġattribute . ĠWe , Ġtherefore Ġuse Ġthe Ġset Ġof Ġ34 Ġmillion Ġusers ( Active ĠUsers Ġof Ġset Ġ$\ math bf Ġ{ S } $ Ġ) Ġfor Ġour Ġfurther Ġanalysis . </s> Profile ĠAtt ribute ĠAnalysis </s> In Ġthis Ġsection Ġwe Ġcharacterize Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġpolitical Ġaccounts Ġ( Set Ġ$ P $ ) Ġand Ġfollower Ġaccounts Ġ( Set Ġ$ S $ ). </s> Profile ĠAtt ribute ĠAnalysis Ġ: :: ĠPolitical ĠHand les </s> Increase Ġin Ġthe ĠInternet Ġuser - base Ġand Ġwidespread Ġadoption Ġof Ġsmartphones Ġin Ġthe Ġcountry Ġhas Ġled Ġto Ġan Ġincrease Ġin Ġthe Ġimportance Ġof Ġsocial Ġmedia Ġcampaigns Ġduring Ġthe Ġelections . ĠThis Ġis Ġevident Ġby Ġthe Ġfact Ġthat Ġin Ġour Ġdataset Ġ( Set Ġ$ U $ ), Ġwe Ġfound Ġthat Ġa Ġtotal Ġof Ġ54 Ġhandles Ġgot Ġverified Ġthroughout Ġ61 Ġsnapshots . ĠMoreover Ġthe Ġaverage Ġnumber Ġof Ġfollowers Ġof Ġhandles Ġin Ġset Ġ$ P $ Ġincreased Ġfrom Ġ$ 3 , 08 , 7 16 . 84 $ Ġto Ġ$ 3 , 19 , 88 1 . 09 $ Ġover Ġthe Ġ61 Ġsnapshots . ĠGiven Ġthat Ġthe Ġaverage Ġfollowers Ġof Ġset Ġ$ P $ Ġincreased Ġby Ġapproximately Ġ$ 3 \ % $ Ġand Ġ54 Ġprofile Ġhandles Ġgot Ġverified Ġin Ġthe Ġspan Ġof Ġ61 Ġdays , Ġwe Ġbelieve Ġthat Ġefforts Ġof Ġleaders Ġon Ġsocial Ġmedia Ġcampaign Ġcan 't Ġbe Ġdenied . </s> We Ġconsider Ġ5 Ġmajor Ġprofile Ġattributes Ġfor Ġfurther Ġanalysis . ĠThese Ġattributes Ġare Ġusername , Ġdisplay Ġname , Ġprofile Ġimage , Ġlocation Ġand Ġdescription Ġrespectively . ĠThe Ġusername Ġis Ġa Ġunique Ġhandle Ġor Ġscreen Ġname Ġassociated Ġwith Ġeach Ġtwitter Ġuser Ġthat Ġappears Ġon Ġthe Ġprofile ĠURL Ġand Ġis Ġused Ġto Ġcommunicate Ġwith Ġeach Ġother Ġon ĠTwitter . ĠSome Ġusername Ġexamples Ġare Ġ$ @ n are nd ram odi $, Ġ$ @ Rah ul G and hi $ Ġetc . ĠThe Ġdisplay Ġname Ġon Ġthe Ġother Ġhand , Ġis Ġmerely Ġa Ġpersonal Ġidentifier Ġthat Ġis Ġdisplayed Ġon Ġthe Ġprofile Ġpage Ġof Ġa Ġuser , Ġe . g . Ġ` N are ndra ĠModi ', Ġ` Rah ul ĠGandhi ', Ġetc . ĠThe Ġdescription Ġis Ġa Ġstring Ġto Ġdescribe Ġthe Ġaccount , Ġwhile Ġthe Ġlocation Ġis Ġuser - defined Ġprofile Ġlocation . ĠWe Ġconsidered Ġthe Ġ5 Ġprofile Ġattributes Ġas Ġstated Ġabove Ġsince Ġthese Ġattributes Ġare Ġkey Ġelements Ġof Ġa Ġusers Ġidentity Ġand Ġsalient ly Ġdefine Ġusers Ġlikes Ġand Ġvalues ĠB IB REF 2 . </s> For Ġthe Ġset Ġof Ġusers Ġin Ġ$ P $, Ġthe Ġtotal Ġnumber Ġof Ġchanges Ġin Ġthese Ġ5 Ġprofile Ġattributes Ġover Ġ61 Ġsnapshots Ġis Ġ9 74 . ĠFigure ĠFIG REF 12 Ġshows Ġthe Ġdistribution Ġof Ġprofile Ġchanges Ġfor Ġspecific Ġattributes Ġover Ġall Ġthe Ġ61 Ġsnapshots . ĠThe Ġmost Ġchanged Ġattribute Ġamong Ġthe Ġpolitical Ġhandles Ġis Ġthe Ġprofile Ġimage Ġfollowed Ġby Ġdisplay Ġname . ĠIn Ġthe Ġsame ĠFigure , Ġthere Ġis Ġa Ġsharp Ġincrease Ġin Ġthe Ġnumber Ġof Ġchanges Ġto Ġthe Ġdisplay Ġname Ġattribute Ġon Ġ23 rd ĠMay , Ġ2019 . ĠThe Ġincrease Ġis Ġbecause Ġ@ n are nd ram odi Ġremoved ĠChow kid ar Ġfrom Ġthe Ġdisplay Ġname Ġof Ġhis ĠTwitter Ġhandle Ġwhich Ġresulted Ġin Ġmany Ġother Ġpolitical Ġhandles Ġdoing Ġthe Ġsame . ĠAnother Ġinteresting Ġtrend Ġis Ġthat Ġnone Ġof Ġthe Ġhandles Ġchanged Ġtheir Ġusername Ġover Ġall Ġthe Ġsnapshots . ĠThis Ġcould Ġbe Ġattributed Ġto Ġthe Ġfact Ġthat ĠTwitter Ġdiscour ages Ġa Ġchange Ġof Ġus ern ames Ġfor Ġverified Ġaccounts Ġas Ġthe Ġverified Ġstatus Ġmight Ġnot Ġremain Ġintact Ġafter Ġthe Ġchange ĠB IB REF 8 . </s> Profile ĠAtt ribute ĠAnalysis Ġ: :: ĠF ollower ĠHand les </s> To Ġcharacterize Ġthe Ġprofile Ġchanging Ġbehavior Ġamongst Ġthe Ġfollowers Ġ, Ġwe Ġstudy Ġthe Ġprofile Ġsnapshots Ġof Ġall Ġthe Ġusers Ġin Ġset Ġ$ S $ Ġover Ġ9 Ġsnapshots . </s> Of Ġall Ġthe Ġactive Ġuser Ġaccounts , Ġ$ 1 , 363 , 499 $ Ġusers Ġmade Ġat Ġleast Ġone Ġchange Ġto Ġtheir Ġprofile Ġattributes Ġover Ġall Ġthe Ġsnapshots . ĠThus , Ġover Ġ$ 3 \ % $ Ġof Ġall Ġthe Ġactive Ġusers Ġengaged Ġin Ġsome Ġform Ġof Ġprofile Ġchange Ġactivity Ġover Ġthe Ġ9 Ġsnapshots . ĠOf Ġthese Ġ$ 1 , 363 , 499 $ Ġusers , Ġmore Ġthan Ġ$ 95 \ % $ Ġof Ġthe Ġusers Ġmade Ġonly Ġ1 Ġchange Ġto Ġtheir Ġprofile , Ġand Ġaround Ġ275 Ġusers Ġmade Ġmore Ġthan Ġ20 Ġchanges . ĠOn Ġan Ġaverage , Ġeach Ġone Ġof Ġthese Ġ$ 1 , 363 , 499 $ Ġusers Ġmade Ġabout Ġ2 Ġchanges Ġto Ġtheir Ġprofiles Ġwith Ġa Ġstandard Ġdeviation Ġof Ġ$ 1 . 42 $. </s> In Ġorder Ġto Ġcompare Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġpolitical Ġaccounts Ġand Ġfollower Ġaccounts , Ġwe Ġanalyzed Ġthe Ġprofile Ġchanges Ġof Ġset Ġ$\ math bf Ġ{ P } $ Ġover Ġthe Ġ9 Ġsnapshots Ġonly . ĠFor Ġthe Ġsame Ġ9 Ġsnapshots , Ġ$ 54 . 9 \ % $ Ġof Ġall Ġusers Ġin Ġset Ġ$\ math bf Ġ{ P } $ Ġmade Ġat Ġleast Ġone Ġchange Ġto Ġtheir Ġprofile Ġattributes . ĠThis Ġis Ġin Ġsharp Ġcontrast Ġto Ġthe Ġthe Ġusers Ġof Ġset Ġ$\ math bf Ġ{ S } $, Ġwhere Ġonly Ġ$ 3 \ % $ Ġof Ġusers Ġmade Ġany Ġchanges Ġto Ġtheir Ġprofile Ġattributes . ĠSimilarly , Ġusers Ġof Ġset Ġ$\ math bf Ġ{ P } $ Ġmade Ġ$ 75 . 32 \ % $ Ġchanges Ġto Ġtheir Ġprofiles Ġon Ġan Ġaverage , Ġwhich Ġis Ġ25 Ġtimes Ġmore Ġthan Ġthe Ġset Ġ$\ math bf Ġ{ S } $. </s> INS IGHT Ġ1 : ĠPolitical Ġhandles Ġare Ġmore Ġlikely Ġto Ġengage Ġin Ġprofile Ġchanging Ġbehavior Ġas Ġcompared Ġto Ġtheir Ġfollowers . </s> In ĠFigure ĠFIG REF 15 , Ġwe Ġplot Ġthe Ġnumber Ġof Ġchanges Ġin Ġgiven Ġattributes Ġover Ġall Ġthe Ġsnapshots Ġfor Ġthe Ġusers Ġin Ġset Ġ$ S $. ĠFrom Ġthis Ġplot , Ġwe Ġfind Ġthat Ġnot Ġall Ġthe Ġattributes Ġare Ġmodified Ġat Ġan Ġequal Ġrate . ĠProfile Ġimage Ġand ĠLocation Ġare Ġthe Ġmost Ġchanged Ġprofile Ġattributes Ġand Ġaccount Ġfor Ġnearly Ġ$ 34 \ % $ Ġand Ġ$ 25 \ % $ Ġrespectively Ġof Ġthe Ġtotal Ġprofile Ġchanges Ġin Ġour Ġdataset . ĠWe Ġanalyze Ġthe Ġtrends Ġin ĠFigure ĠFIG REF 12 Ġand Ġfind Ġthat Ġthe Ġpolitical Ġhandles Ġdo Ġnot Ġchange Ġtheir Ġus ern ames Ġat Ġall . ĠThis Ġis Ġin Ġcontrast Ġto Ġthe Ġtrend Ġin ĠFigure ĠFIG REF 15 Ġwhere Ġwe Ġsee Ġthat Ġthere Ġare Ġa Ġlot Ġof Ġhandles Ġthat Ġchange Ġtheir Ġus ern ames Ġmultiple Ġtimes . ĠThe Ġmost Ġlikely Ġreason Ġfor Ġthe Ġsame Ġis Ġthat Ġmost Ġof Ġthe Ġfollower Ġhandles Ġare Ġnot Ġverified Ġand Ġwould Ġnot Ġloose Ġtheir Ġverified Ġstatus Ġon Ġchanging Ġtheir Ġusername . </s> The Ġfollower Ġhandles Ġalso Ġuse Ġtheir Ġus ern ames Ġto Ġshow Ġsupport Ġto Ġa Ġmovement Ġor Ġperson Ġand Ġrevert Ġback Ġto Ġtheir Ġoriginal Ġusername Ġlater . ĠOut Ġof Ġthe Ġ$ 79 , 99 1 $ Ġusername Ġchanges , Ġ$ 1 , 7 11 $ Ġusers Ġof Ġset Ġ${ S } $ Ġwent Ġback Ġand Ġforth Ġto Ġthe Ġsame Ġname Ġwhich Ġincluded Ġadding Ġof ĠElection Ġrelated Ġkeywords . ĠExamples Ġof Ġfew Ġsuch Ġcases Ġin Ġour Ġdataset Ġare Ġshown Ġin ĠTable ĠT AB REF 16 . </s> INS IGHT Ġ2 : ĠUsers Ġdo Ġnot Ġchange Ġall Ġprofile Ġattributes Ġequally , Ġand Ġthe Ġprofile Ġattributes Ġthat Ġpolitical Ġand Ġfollower Ġhandles Ġfocus Ġon Ġare Ġdifferent . </s> Profile ĠAtt ribute ĠAnalysis Ġ: :: ĠSimilar ity Ġanalysis </s> Change Ġin Ġan Ġattribute Ġin Ġthe Ġprofile Ġinvolves Ġreplacing Ġan Ġexisting Ġattribute Ġwith Ġa Ġnew Ġone . ĠWe Ġanalyze Ġthe Ġsimilarity Ġbetween Ġthe Ġcurrent , Ġnew Ġdisplay Ġnames Ġand Ġus ern ames Ġused Ġby Ġusers Ġin Ġset Ġ$\ math bf Ġ{ P } $ Ġand Ġ$\ math bf Ġ{ S } $ Ġrespectively . ĠFor Ġthis Ġanalysis , Ġwe Ġconsider Ġonly Ġthe Ġusers Ġwho Ġhave Ġchanged Ġtheir Ġusername Ġor Ġdisplay Ġname Ġat Ġleast Ġonce Ġin Ġall Ġ9 Ġsnapshots . ĠWe Ġuse Ġthe ĠLong est ĠCommon ĠSub sequence Ġ( LCS ) Ġto Ġcompute Ġthe Ġsimilarity Ġbetween Ġtwo Ġstrings . ĠThus , Ġfor Ġeach Ġuser , Ġwe Ġcompute Ġthe Ġaverage ĠLCS Ġscore Ġbetween Ġall Ġpossible Ġpairs Ġof Ġunique Ġdisplay Ġnames Ġand Ġus ern ames Ġadopted Ġby Ġthem . ĠA Ġhigh Ġaverage ĠLCS Ġscore Ġindicates Ġthat Ġthe Ġnewer Ġusername Ġor Ġdisplay Ġname Ġwere Ġnot Ġsignificantly Ġdifferent Ġfrom Ġthe Ġprevious Ġones . </s> The Ġgraph Ġin ĠFigure ĠFIG REF 18 Ġshows Ġthat Ġnearly Ġ$ 80 \ % $ Ġof Ġthe Ġusers Ġin Ġset Ġ$ S $ Ġmade Ġmore Ġsignificant Ġchanges Ġto Ġtheir ĠDisplay ĠNames Ġas Ġcompared Ġto Ġthe Ġpolitical Ġhandles . Ġ( This Ġcan Ġbe Ġseen Ġby Ġconsidering Ġa Ġhorizontal Ġline Ġon Ġthe Ġgraph . ĠFor Ġany Ġgiven Ġfraction Ġof Ġusers , Ġthe Ġaverage ĠLCS Ġscore Ġof Ġset Ġ$ S $ Ġ( Follow ers ) Ġis Ġlesser Ġthan Ġset Ġ$ P $ Ġ( Polit icians ) Ġfor Ġapproximately Ġ$ 80 \ % $ Ġpercent Ġof Ġusers ). ĠFor Ġaround Ġ$ 10 \ % $ Ġof Ġthe Ġusers Ġin Ġset Ġ$ P $, Ġthe Ġnew Ġand Ġold Ġprofile Ġattributes Ġwere Ġunrelated Ġ( LCS Ġscore Ġ< Ġ$ 0 . 5 $ ) Ġand Ġfor Ġusers Ġin Ġset Ġ$ S $, Ġthis Ġvalue Ġwent Ġup Ġto Ġ$ 30 \ % $. </s> INS IGHT Ġ3 : ĠPolitical Ġhandles Ġtend Ġto Ġmake Ġnew Ġchanges Ġrelated Ġto Ġprevious Ġattribute Ġvalues . ĠHowever , Ġthe Ġfollowers Ġmake Ġcomparatively Ġless Ġrelated Ġchanges Ġto Ġprevious Ġattribute Ġvalues . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata </s> In Ġthis Ġsection , Ġwe Ġfirst Ġdescribe Ġthe Ġphenomenon Ġof Ġmention Ġof Ġpolitical Ġparties Ġnames Ġin Ġthe Ġprofile Ġattributes Ġof Ġusers . ĠThis Ġis Ġfollowed Ġby Ġthe Ġanalysis Ġof Ġprofiles Ġthat Ġmake Ġspecific Ġmentions Ġof Ġpolitical Ġhandles Ġin Ġtheir Ġprofile Ġattributes . ĠBoth Ġof Ġthese Ġconstitute Ġan Ġorganic Ġway Ġof Ġshowing Ġsupport Ġto Ġa Ġparty Ġand Ġdoes Ġnot Ġinvolve Ġany Ġdirect Ġcampaigning Ġby Ġthe Ġparties . ĠWe Ġalso Ġstudy Ġin Ġdetail Ġthe Ġ# Main B hi Ch ow kid ar Ġcampaign Ġand Ġanalyze Ġthe Ġcorresponding Ġchange Ġin Ġprofile Ġattributes Ġassociated Ġwith Ġit . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata Ġ: :: ĠParty Ġmention Ġin Ġthe Ġprofile Ġattributes </s> To Ġsustain Ġour Ġhypothesis Ġof Ġparty Ġmention Ġbeing Ġan Ġorganic Ġbehavior , Ġwe Ġanalyzed Ġparty Ġname Ġmentions Ġin Ġthe Ġprofiles Ġof Ġusers Ġin Ġset Ġ$\ math bf Ġ{ S } $. ĠFor Ġthis Ġanalysis , Ġwe Ġfocused Ġon Ġtwo Ġof Ġthe Ġmajor Ġnational Ġparties Ġin ĠIndia , Ġnamely ĠIndian ĠNational ĠCongress Ġ( INC ) Ġand ĠBh art iya ĠJ anta ĠParty Ġ( B JP ). ĠWe Ġspecifically Ġsearch Ġfor Ġmentions Ġof Ġthe Ġparty Ġnames Ġand Ġa Ġfew Ġof Ġits Ġvariants Ġin Ġthe Ġdisplay Ġname , Ġusername Ġand Ġdescription Ġof Ġthe Ġuser Ġprofiles . ĠWe Ġfind Ġthat Ġ$ 50 , 432 $ Ġusers Ġhave Ġmentioned ĠâĢ ľ B JP âĢ Ŀ Ġ( or Ġits Ġvariants ) Ġon Ġtheir Ġdescription Ġin Ġthe Ġfirst Ġsnapshot Ġof Ġour Ġdata Ġitself . ĠThis Ġconstitutes Ġapproximately Ġ$ 1 \ % $ Ġof Ġthe Ġtotal Ġnumber Ġof Ġactive Ġusers Ġin Ġour Ġdataset , Ġimplying Ġa Ġsubstantial Ġpresence Ġof Ġthe Ġparty Ġon ĠTwitter . ĠMoreover , Ġ$ 2 , 6 38 $ Ġusers Ġadded ĠâĢ ľ B JP âĢ Ŀ Ġ( or Ġits Ġvariants ) Ġto Ġtheir Ġdescription Ġover Ġthe Ġcourse Ġof Ġdata Ġcollection Ġand Ġ$ 1 , 6 38 $ Ġusers Ġremoved ĠâĢ ľ B JP âĢ Ŀ Ġfrom Ġtheir Ġdescription . ĠTable ĠT AB REF 20 Ġshows Ġthe Ġnumber Ġof Ġmentions Ġof Ġboth Ġparties Ġin Ġthe Ġdifferent Ġprofile Ġattributes . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata Ġ: :: ĠWho Ġfollows Ġwho </s> We Ġobserve Ġthat Ġa Ġlot Ġof Ġusers Ġin Ġour Ġdataset Ġwrote Ġthat Ġthey Ġare Ġproud Ġto Ġbe Ġfollowed Ġby Ġa Ġfamous Ġpolitical Ġhandle Ġin Ġtheir Ġdescription . ĠWe Ġshow Ġexample Ġof Ġsuch Ġcases Ġin ĠTable ĠT AB REF 22 . </s> We Ġperform Ġan Ġexhaustive Ġsearch Ġof Ġall Ġsuch Ġmentions Ġin Ġthe Ġdescription Ġattribute Ġof Ġthe Ġusers Ġand Ġfind Ġ$ 1 , 164 $ Ġinstances Ġof Ġthis Ġphenomenon . ĠThis Ġanalysis Ġand Ġthe Ġparty Ġmention Ġanalysis Ġin ĠSection ĠSEC REF 19 , Ġboth Ġtestify Ġthat Ġpeople Ġoften Ġdisplay Ġtheir Ġpolitical Ġinclination Ġon ĠTwitter Ġvia Ġtheir Ġprofile Ġattributes Ġitself . </s> INS IGHT Ġ4 : ĠTwitter Ġusers Ġoften Ġdisplay Ġtheir Ġpolitical Ġincl inations Ġin Ġtheir Ġprofile Ġattributes Ġitself Ġand Ġare Ġpretty Ġopen Ġabout Ġit . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata Ġ: :: ĠThe ĠChow kid ar Ġmovement </s> As Ġdiscussed Ġis ĠSection ĠSEC REF 1 , Ġ@ n are nd ram odi Ġadded ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġto Ġhis Ġdisplay Ġname Ġin Ġresponse Ġto Ġan Ġopposition Ġcampaign Ġcalled ĠâĢ ľ Ch ow kid ar Ġch or Ġha i âĢ Ŀ . ĠIn Ġa Ġcoordinated Ġcampaign , Ġseveral Ġother Ġleaders Ġand Ġministers Ġof ĠBJP Ġalso Ġchanged Ġtheir ĠTwitter Ġprofiles Ġby Ġadding Ġthe Ġprefix ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames . ĠThe Ġmovement , Ġhowever , Ġdid Ġnot Ġremain Ġconfined Ġamongst Ġthe Ġmembers Ġof Ġthe Ġparty Ġthemselves Ġand Ġsoon , Ġseveral ĠTwitter Ġusers Ġupdated Ġtheir Ġprofiles Ġas Ġwell . ĠWe Ġop ine Ġthat Ġthe Ġwhole Ġcampaign Ġof Ġadding ĠChow kid ar Ġto Ġthe Ġprofile Ġattributes Ġshow Ġan Ġin organic Ġbehavior , Ġwith Ġpolitical Ġleaders Ġacting Ġas Ġthe Ġcatalyst . ĠAn Ġinteresting Ġaspect Ġof Ġthis Ġcampaign Ġwas Ġthe Ġfact Ġthat Ġthe Ġusers Ġused Ġseveral Ġdifferent Ġvariants Ġof Ġthe Ġword ĠChow kid ar Ġwhile Ġadding Ġit Ġto Ġtheir ĠTwitter Ġprofiles . ĠSome Ġof Ġthe Ġmost Ġcommon Ġvariants Ġof ĠChow kid ar Ġthat Ġwere Ġpresent Ġin Ġour Ġdataset Ġalong Ġwith Ġits Ġfrequency Ġof Ġuse Ġis Ġshown Ġin ĠFigure ĠFIG REF 24 . </s> We Ġutilize Ġa Ġregular Ġexpression Ġquery Ġto Ġexhaust ively Ġsearch Ġall Ġthe Ġvariants Ġof Ġthe Ġword ĠChow kid ar Ġin Ġour Ġdataset . ĠWe Ġfound Ġ$ 2 , 60 , 607 $ Ġinstances Ġof Ġusers Ġin Ġset Ġ$ S $ Ġadded ĠChow kid ar Ġ( or Ġits Ġvariants ) Ġto Ġtheir Ġdisplay Ġnames . ĠThese Ġ$ 2 , 60 , 607 $ Ġinstances Ġcomprised Ġa Ġtotal Ġof Ġ241 Ġunique Ġch ow kid ar Ġvariants Ġof Ġwhich Ġ225 Ġhave Ġbeen Ġused Ġlesser Ġthan Ġ500 Ġtimes . ĠWe Ġalso Ġperform Ġthe Ġsame Ġanalysis Ġfor Ġthe Ġother Ġprofile Ġattributes Ġlike Ġdescription Ġand Ġusername Ġas Ġwell . ĠThe Ġnumber Ġof Ġusers Ġwho Ġadded ĠChow kid ar Ġto Ġtheir Ġdescription Ġand Ġusername Ġare Ġ$ 14 , 409 $ Ġand Ġ$ 12 , 651 $ Ġrespectively . ĠThe Ġunion Ġand Ġintersection Ġof Ġall Ġthese Ġusers Ġare Ġ$ 270 , 9 45 $ Ġand Ġ7 27 Ġrespectively , Ġimplying Ġthat Ġmost Ġusers Ġadded ĠChow kid ar Ġto Ġonly Ġone Ġof Ġtheir Ġprofile Ġattributes . </s> We Ġbelieve , Ġthe Ġeffect Ġof Ġchanging Ġthe Ġprofile Ġattribute Ġin Ġaccordance Ġwith ĠPrime ĠMinister 's Ġcampaign Ġis Ġan Ġexample Ġof Ġin organic Ġbehavior Ġcontag ion ĠB IB REF 6 , ĠB IB REF 9 . ĠThe Ġauthors Ġin ĠB IB REF 6 Ġargue Ġthat Ġopinion Ġdiff uses Ġeasily Ġin Ġa Ġnetwork Ġif Ġit Ġcomes Ġfrom Ġopinion Ġleaders Ġwho Ġare Ġconsidered Ġto Ġbe Ġusers Ġwith Ġa Ġvery Ġhigh Ġnumber Ġof Ġfollowers . ĠWe Ġsee Ġa Ġsimilar Ġbehavior Ġcontag ion Ġin Ġour Ġdataset Ġwith Ġrespect Ġto Ġthe ĠChow kid ar Ġmovement . </s> We Ġanalyze Ġthe Ġsimilarity Ġof Ġthe Ġdisplay Ġnames Ġwith Ġrespect Ġto Ġthe Ġbehavior Ġcontag ion Ġof ĠChow kid ar Ġmovement . ĠIn Ġthe ĠCD F Ġplot Ġof ĠFigure ĠFIG REF 18 , Ġa Ġsignificant Ġspike Ġis Ġobserved Ġin Ġthe Ġregion Ġof ĠLCS Ġvalues Ġbetween Ġ$ 0 . 6 $ -$ 0 . 8 $. ĠThis Ġspike Ġis Ġcaused Ġmostly Ġdue Ġto Ġthe Ġpolitical Ġhandles , Ġwho Ġadded ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames Ġwhich Ġaccounted Ġfor Ġ$ 95 . 7 \ % $ Ġof Ġthe Ġusers Ġin Ġthis Ġregion . ĠIn Ġset Ġ$ P $, Ġa Ġtotal Ġof Ġ373 Ġpeople Ġadded Ġthe Ġspecific Ġkeyword ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames Ġof Ġwhich Ġ315 Ġlie Ġin Ġthe Ġnormalized ĠLCS Ġrange Ġof Ġ$ 0 . 6 $ Ġto Ġ$ 0 . 8 $. ĠWe Ġperform Ġsimilar Ġanalysis Ġon Ġthe Ġusers Ġof Ġset Ġ$ S $ Ġand Ġfind Ġthat Ġaround Ġ$ 57 \ % $ Ġof Ġthe Ġusers Ġwithin Ġthe Ġregion Ġof Ġ$ 0 . 6 $ -$ 0 . 8 $ ĠLCS Ġadded Ġspecific Ġkeyword ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames . ĠWe Ġperform Ġsimilar Ġanalysis Ġon Ġother Ġcampaign Ġkeywords Ġlike ĠAb ĠH oga ĠNy ay Ġas Ġwell Ġbut Ġnone Ġof Ġthem Ġhave Ġthe Ġsame Ġlevel Ġof Ġinfluence Ġor Ġpopularity Ġlike Ġthat Ġof Ġthe ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġand Ġhence Ġwe Ġcall Ġit Ġas Ġthe ĠâĢ ľ Ch ow kid ar ĠMovement âĢ Ŀ . </s> On Ġ23 rd ĠMay , ĠNarendra ĠModi Ġremoved ĠChow kid ar Ġfrom Ġhis ĠTwitter Ġprofile Ġand Ġurged Ġothers Ġto Ġdo Ġthe Ġsame Ġas Ġshown Ġin ĠFigure ĠFIG REF 25 . ĠWhile Ġmost Ġusers Ġfollowed ĠNarendra ĠModi 's Ġinstructions Ġand Ġremoved ĠChow kid ar Ġfrom Ġtheir Ġprofiles , Ġsome Ġusers Ġstill Ġcontinued Ġto Ġadd Ġch ow kid ar Ġto Ġtheir Ġnames . ĠThe Ġweekly Ġaddition Ġand Ġremoval Ġof ĠChow kid ar Ġfrom Ġuser Ġprofiles Ġis Ġpresented Ġin Ġdetail Ġin ĠFigure ĠFIG REF 26 . ĠThus , Ġthe Ġbehavior Ġcontag ion Ġis Ġevident Ġfrom Ġthe Ġfact Ġthat Ġafter ĠNarendra ĠModi Ġremoved ĠChow kid ar Ġon Ġ23 rd ĠMay , Ġmajority Ġof Ġthe Ġpopulation Ġin Ġset Ġ$\ math bf Ġ{ S } $ Ġremoved Ġit Ġtoo . </s> INS IGHT Ġ5 : ĠThe ĠChow kid ar Ġmovement Ġwas Ġvery Ġwidespread Ġand Ġillustrated Ġcontag ion Ġbehavior . </s> Topic Ġmodelling Ġof Ġusers </s> A Ġlarge Ġnumber Ġof Ġpeople Ġwere Ġpart Ġof Ġthe ĠChow kid ar Ġmovement . ĠHence , Ġit Ġis Ġimportant Ġto Ġgauge Ġtheir Ġlevel Ġof Ġinterest / support Ġto Ġthe Ġparty Ġin Ġorder Ġto Ġaccurately Ġjudge Ġthe Ġimpact Ġof Ġthe Ġcampaign . ĠWe Ġthus Ġperform Ġtopic Ġmodelling Ġof Ġthe Ġdescription Ġattribute Ġof Ġthese Ġusers . ĠWe Ġutilize Ġthe ĠL DA ĠMal let Ġmodel ĠB IB REF 10 Ġfor Ġtopic Ġmodelling Ġafter Ġperforming Ġthe Ġrelevant Ġpre processing Ġsteps Ġon Ġthe Ġdata . ĠWe Ġset Ġthe Ġnumber Ġof Ġtopics Ġto Ġ10 Ġas Ġit Ġgave Ġthe Ġhighest Ġco herence Ġscore Ġin Ġour Ġexperiments . ĠThe Ġ10 Ġtopics Ġand Ġthe Ġ5 Ġmost Ġimportant Ġkeywords Ġbelonging Ġto Ġthese Ġtopics Ġare Ġshown Ġbelow Ġin ĠFigure ĠFIG REF 27 . ĠThese Ġtopics Ġreveal Ġseveral Ġinteresting Ġinsights . ĠThe Ġmost Ġdiscussed Ġtopic Ġamongst Ġthese Ġusers Ġinvolves ĠNarendra ĠModi Ġand Ġincludes Ġwords Ġlike ĠâĢ ľ nam o âĢ Ŀ Ġ( sl ang Ġfor ĠNarendra ĠModi ), Ġsupporter , Ġnationalist Ġand ĠâĢ ľ bh ak t âĢ Ŀ Ġ( dev out Ġbeliever ). ĠA Ġfew Ġother Ġtopics Ġalso Ġinvolve Ġpolitics Ġlike Ġtopic Ġ2 Ġand Ġtopic Ġ5 . ĠTopic Ġ2 , Ġin Ġparticular Ġcontains Ġthe Ġwords Ġch ow kid ar , Ġstudent , Ġand Ġstudy , Ġindicating Ġthat Ġa Ġsignificant Ġset Ġof Ġusers Ġwho Ġwere Ġpart Ġof Ġthis Ġcampaign Ġwere Ġactually Ġstudents . ĠTopic Ġnumber Ġ0 Ġaccounts Ġfor Ġmore Ġthan Ġ$ 40 \ % $ Ġof Ġthe Ġtotal Ġdescriptions , Ġwhich Ġessentially Ġmeans Ġthat Ġmost Ġof Ġthe Ġpeople Ġinvolved Ġin Ġthis Ġmovement Ġwere Ġsupporting ĠBJP Ġor ĠNarendra ĠModi . ĠWe Ġalso Ġplot Ġthe Ġtopics Ġassociated Ġwith Ġeach Ġuser 's Ġdescription Ġon Ġa Ġ2 D Ġplane Ġby Ġperforming Ġt S NE ĠB IB REF 11 Ġon Ġthe Ġ10 Ġdimensional Ġdata Ġto Ġascertain Ġthe Ġvariation Ġin Ġthe Ġtype Ġof Ġtopics . ĠThe Ġtopics Ġare Ġpretty Ġdistinct Ġand Ġwell Ġseparated Ġon Ġthe Ġ2 D Ġplane Ġas Ġcan Ġbe Ġseen Ġin ĠFigure ĠFIG REF 30 . </s> INS IGHT Ġ6 : ĠThe Ġusers Ġwho Ġwere Ġpart Ġof Ġthe Ġch ow kid ar Ġmovement Ġwere Ġmostly ĠNarendra ĠModi Ġsupporters Ġand Ġengaged Ġin Ġpolitical Ġtopics </s> Related ĠWork </s> Social ĠMedia Ġprofiles Ġare Ġa Ġway Ġto Ġpresent Ġoneself Ġand Ġto Ġcultivate Ġrelationship Ġwith Ġlike Ġminded Ġor Ġdesired Ġaudience . ĠB IB REF 0 , ĠB IB REF 12 . ĠWith Ġchange Ġin Ġtime Ġand Ġinterest , Ġusers Ġon ĠTwitter Ġchange Ġtheir Ġprofile Ġnames , Ġdescription , Ġscreen Ġnames , Ġlocation Ġor Ġlanguage ĠB IB REF 2 . ĠSome Ġof Ġthese Ġchanges Ġare Ġorganic ĠB IB REF 13 , ĠB IB REF 2 , Ġwhile Ġother Ġchanges Ġcan Ġbe Ġtriggered Ġby Ġcertain Ġevents ĠB IB REF 14 , ĠB IB REF 15 . ĠIn Ġthis Ġsection , Ġwe Ġlook Ġinto Ġthe Ġprevious Ġliterature Ġwhich Ġare Ġrelated Ġto Ġuser Ġprofile Ġattribute 's Ġchange Ġbehavior . ĠWe Ġfurther Ġlook Ġinto Ġhow ĠTwitter Ġhas Ġbeen Ġused Ġto Ġinitiate Ġor Ġsustain Ġmovements ĠB IB REF 14 , ĠB IB REF 15 , ĠB IB REF 1 . ĠFinally Ġwe Ġinvestigate Ġhow Ġthe Ġcontag ion Ġbehavior Ġadaptation Ġspread Ġthrough Ġopinion Ġleaders ĠB IB REF 6 , ĠB IB REF 9 Ġand Ġconclude Ġwith Ġour Ġwork 's Ġcontribution Ġto Ġthe Ġliterature . </s> Related ĠWork Ġ: :: ĠProfile Ġattribute Ġchange Ġbehavior </s> Previous Ġstudies Ġhave Ġfocused Ġon Ġchange Ġin Ġusername Ġattribute Ġextensively , Ġas Ġus ern ames Ġin ĠTwitter Ġhelps Ġin Ġlink ability , Ġmentions Ġand Ġbecomes Ġpart Ġof Ġthe Ġprofile 's ĠURL ĠB IB REF 16 , ĠB IB REF 13 , ĠB IB REF 17 . ĠWhile Ġthe Ġfocus Ġof Ġfew Ġworks Ġis Ġto Ġunderstand Ġthe Ġreason Ġthe Ġusers Ġchange Ġtheir Ġprofile Ġnames ĠB IB REF 16 , ĠB IB REF 2 , Ġothers Ġstrongly Ġargue Ġusername Ġchange Ġis Ġa Ġbad Ġidea ĠB IB REF 17 , ĠB IB REF 13 . ĠJ ain Ġet Ġal . Ġargue Ġthat Ġthe Ġmain Ġreason Ġpeople Ġopt Ġto Ġchange Ġtheir Ġusername Ġis Ġto Ġleverage Ġmore Ġtweet Ġspace . ĠSimilarly , Ġthe Ġwork Ġby ĠMar ic ont i Ġet Ġal . Ġsuggests Ġthat Ġthe Ġchange Ġin Ġusername Ġmay Ġbe Ġa Ġresult Ġof Ġname Ġsquat ting , Ġwhere Ġa Ġperson Ġmay Ġtake Ġaway Ġthe Ġusername Ġof Ġa Ġpopular Ġhandle Ġfor Ġmalicious Ġreasons . ĠMore Ġrecent Ġstudies Ġin Ġthe Ġarea Ġhave Ġfocused Ġon Ġseveral Ġprofile Ġattributes Ġchanges ĠB IB REF 2 . ĠThey Ġsuggest Ġthat Ġprofile Ġattributes Ġcan Ġbe Ġused Ġto Ġrepresent Ġoneself Ġat Ġmicro - level Ġas Ġwell Ġas Ġrepresent Ġsocial Ġmovement Ġat Ġmacro - level . </s> Related ĠWork Ġ: :: ĠSocial ĠMedia Ġfor Ġonline Ġmovements </s> The Ġrole Ġof ĠSocial ĠMedia Ġduring Ġsocial Ġmovements Ġhave Ġbeen Ġstudied Ġvery Ġextensively ĠB IB REF 14 , ĠB IB REF 18 , ĠB IB REF 19 , ĠB IB REF 15 . ĠWith Ġthe Ġhelp Ġof ĠSocial ĠMedia , Ġparticipation Ġand Ġease Ġto Ġexpress Ġoneself Ġbecomes Ġeasy ĠB IB REF 1 . ĠThere Ġhave Ġbeen Ġmany Ġstudies Ġwhere ĠTwitter Ġhas Ġbeen Ġused Ġto Ġgather Ġthe Ġinsight Ġof Ġonline Ġusers Ġin Ġthe Ġform Ġof Ġtweets ĠB IB REF 1 , ĠB IB REF 14 , ĠB IB REF 15 . ĠThe Ġauthors Ġin ĠB IB REF 14 Ġstudy Ġthe Ġtweets Ġto Ġunderstand Ġhow Ġpeople Ġreacted Ġto Ġdecriminal ization Ġof ĠLGBT Ġin ĠIndia . ĠThe Ġstudy Ġof ĠGe zi ĠPark Ġprotest Ġshows Ġhow Ġthe Ġprotest Ġtook Ġa Ġpolitical Ġturn , Ġas Ġpeople Ġchanged Ġtheir Ġus ern ames Ġto Ġshow Ġsupport Ġto Ġtheir Ġpolitical Ġleaders ĠB IB REF 1 . </s> The Ġsuccess Ġof Ġthe Ġparty ĠâĢ ľ Alternative Ġfor ĠDe utsch land ( AF D ) âĢ Ŀ Ġin ĠGerman ĠFederal ĠElections , Ġ2017 Ġcan Ġbe Ġc atered Ġto Ġits Ġlarge Ġonline Ġpresence ĠB IB REF 18 . ĠThe Ġauthors Ġin ĠB IB REF 18 Ġstudy Ġthe Ġtweets Ġand Ġret we ets Ġnetworks Ġduring Ġthe ĠGerman Ġelections Ġand Ġanalyze Ġhow Ġusers Ġare Ġorganized Ġinto Ġcommunities Ġand Ġhow Ġinformation Ġflows Ġbetween Ġthem . ĠThe Ġrole Ġof Ġinfluential Ġleaders Ġduring Ġelections Ġhas Ġalso Ġbeen Ġstudied Ġin Ġdetail Ġin Ġstudies Ġlike ĠB IB REF 20 . ĠThis Ġbrings Ġus Ġto Ġthe Ġstudy Ġof Ġhow Ġinfluential Ġleaders Ġcontribute Ġto Ġspread Ġand Ġadaptation Ġof Ġevents . </s> Related ĠWork Ġ: :: ĠAd option Ġof Ġbehavior Ġfrom Ġleaders </s> The Ġreal Ġand Ġvirtual Ġworld Ġare Ġintertwined Ġin Ġa Ġway Ġthat Ġany Ġspeech , Ġevent , Ġor Ġaction Ġof Ġleaders Ġmay Ġtrigger Ġthe Ġchanges Ġis Ġbehavior Ġof Ġpeople Ġonline ĠB IB REF 1 , ĠB IB REF 21 . ĠOpinion Ġleaders Ġhave Ġbeen Ġdefined Ġas Ġ` the Ġindividuals Ġwho Ġwere Ġlikely Ġto Ġinfluence Ġother Ġpersons Ġin Ġtheir Ġimmediate Ġenvironment .' ĠB IB REF 22 ĠOpinion Ġleaders Ġon Ġsocial Ġmedia Ġplatforms Ġhelp Ġin Ġadoption Ġof Ġbehavior Ġby Ġfollowers Ġeasily ĠB IB REF 9 , ĠB IB REF 6 . ĠThe Ġperformance Ġand Ġeffectiveness Ġof Ġthe Ġopinion Ġleader Ġdirectly Ġaffect Ġthe Ġcontag ion Ġof Ġthe Ġleader ĠB IB REF 9 . ĠOn ĠTwitter , Ġopinion Ġleaders Ġshow Ġhigh Ġmotivation Ġfor Ġinformation Ġseeking Ġand Ġpublic Ġexpression . ĠMoreover , Ġthey Ġmake Ġa Ġsignificant Ġcontribution Ġto Ġpeople 's Ġpolitical Ġinvolvement ĠB IB REF 21 . </s> Our Ġwork Ġcontributes Ġto Ġthis Ġliterature Ġin Ġtwo Ġmajor Ġways . ĠFirst , Ġwe Ġinvestigate Ġhow Ġprofile Ġattribute Ġchanges Ġare Ġsimilar Ġor Ġdifferent Ġfrom Ġthe Ġinfluential Ġleaders Ġin Ġthe Ġcountry Ġand Ġhow Ġmuch Ġof Ġthese Ġchanges Ġare Ġthe Ġresult Ġof Ġthe Ġsame . ĠThen Ġwe Ġsee Ġhow Ġthe Ġusers Ġchange Ġtheir Ġprofile Ġinformation Ġin Ġthe Ġmidst Ġof Ġelection Ġin Ġthe Ġlargest Ġdemocracy Ġin Ġthe Ġworld . </s> Discussion </s> In Ġthis Ġpaper , Ġwe Ġstudy Ġthe Ġuse Ġof Ġprofile Ġattributes Ġon ĠTwitter Ġas Ġthe Ġmajor Ġendorsement Ġduring Ġ# L ok Sab ha Elect ios 2019 . ĠWe Ġidentify Ġ$ 1 , 293 $ Ġverified Ġpolitical Ġleader 's Ġprofile Ġand Ġextract Ġ$ 49 , 433 , 640 $ Ġunique Ġfollowers . ĠWe Ġcollect Ġthe Ġdaily Ġand Ġweekly Ġsnapshots Ġof Ġthe Ġpolitical Ġleaders Ġand Ġthe Ġfollower Ġhandles Ġfrom Ġ5 th ĠApril , Ġ2019 Ġto Ġ5 th ĠJune , Ġ2019 . ĠWith Ġthe Ġconstraint Ġof ĠTwitter ĠAPI , Ġwe Ġcollected Ġa Ġtotal Ġof Ġ9 Ġsnapshots Ġof Ġthe Ġfollower 's Ġprofile Ġand Ġ61 Ġsnapshots Ġof ĠPolitical Ġhandles . ĠWe Ġconsider Ġthe Ġ5 Ġmajor Ġattributes Ġfor Ġthe Ġanalysis , Ġincluding , ĠProfile ĠName , ĠUsername , ĠDescription , ĠProfile ĠImage , Ġand ĠLocation . </s> Firstly , Ġwe Ġanalyze Ġthe Ġmost Ġchanged Ġattributes Ġamong Ġthe Ġ5 Ġmajor Ġattributes . ĠWe Ġfind Ġthat Ġmost Ġof Ġthe Ġusers Ġmade Ġat - most Ġ4 Ġchanges Ġduring Ġthe Ġelection Ġcampaign , Ġwith Ġprofile Ġimage Ġbeing Ġthe Ġmost Ġchanged Ġattribute Ġfor Ġboth Ġthe Ġpolitical Ġas Ġwell Ġas Ġfollower Ġhandles . ĠWe Ġalso Ġshow Ġthat Ġpolitical Ġhandles Ġmade Ġmore Ġchanges Ġto Ġprofile Ġattributes Ġthan Ġthe Ġfollower Ġhandles . ĠAbout Ġ$ 80 \ % $ Ġchanges Ġmade Ġby Ġthe Ġfollowers Ġin Ġthe Ġperiod Ġof Ġdata Ġcollection Ġwas Ġrelated Ġto Ġ# L ok Sab ha Elect ios 2019 . ĠWhile Ġthe Ġprofile Ġhandles Ġmade Ġchanges , Ġthe Ġchanges Ġmostly Ġincluded Ġgoing Ġback Ġand Ġforth Ġto Ġsame Ġprofile Ġattribute , Ġwith Ġthe Ġchange Ġin Ġattribute Ġbeing Ġactive Ġsupport Ġto Ġa Ġpolitical Ġparty Ġor Ġpolitical Ġleader . </s> We Ġargue Ġthat Ġwe Ġcan Ġpredict Ġthe Ġpolitical Ġinclination Ġof Ġa Ġuser Ġusing Ġjust Ġthe Ġprofile Ġattribute Ġof Ġthe Ġusers . ĠWe Ġfurther Ġshow Ġthat Ġthe Ġpresence Ġof Ġparty Ġname Ġin Ġthe Ġprofile Ġattribute Ġcan Ġbe Ġconsidered Ġas Ġan Ġorganic Ġbehavior Ġand Ġsignals Ġsupport Ġto Ġa Ġparty . ĠHowever , Ġwe Ġargue Ġthat Ġthe Ġaddition Ġof Ġelection Ġcampaign Ġrelated Ġkeywords Ġto Ġthe Ġprofile Ġis Ġa Ġform Ġof Ġin organic Ġbehavior . ĠThe Ġin organic Ġbehavior Ġanalysis Ġfalls Ġinline Ġwith Ġbehavior Ġcontag ion , Ġwhere Ġthe Ġfollowers Ġtend Ġto Ġadapt Ġto Ġthe Ġbehavior Ġof Ġtheir Ġopinion Ġleaders . ĠThe ĠâĢ ľ Ch ow kid ar Ġmovement âĢ Ŀ Ġshowed Ġa Ġsimilar Ġeffect Ġin Ġthe Ġ# L ok Sab ha Elect ios 2019 , Ġwhich Ġwas Ġevident Ġby Ġhow Ġthe Ġother Ġpolitical Ġleaders Ġand Ġfollowers Ġof ĠBJP Ġparty Ġadded Ġch ow kid ar Ġto Ġtheir Ġprofile Ġattributes Ġafter Ġ@ n are nd ram odi Ġdid . ĠWe Ġthus , Ġargue Ġthat Ġpeople Ġdon 't Ġshy Ġaway Ġfrom Ġshowing Ġsupport Ġto Ġpolitical Ġparties Ġthrough Ġprofile Ġinformation . </s> We Ġbelieve Ġour Ġanalysis Ġwill Ġbe Ġhelpful Ġto Ġunderstand Ġhow Ġsocial Ġmovement Ġlike Ġelection Ġare Ġperceived Ġand Ġsustained Ġthrough Ġprofile Ġattributes Ġon Ġsocial Ġmedia . ĠOur Ġfindings Ġprovide Ġbaseline Ġdata Ġfor Ġstudy Ġin Ġthe Ġdirection Ġof Ġelection Ġcampaign Ġand Ġto Ġfurther Ġanalyze Ġpeople 's Ġperspective Ġon Ġelections .
Introduction User profiles on social media platforms serve as a virtual introduction of the users. People often maintain their online profile space to reflect their likes and values. Further, how users maintain their profile helps them develop relationship with coveted audience BIBREF0. A user profile on Twitter is composed of several attributes with some of the most prominent ones being the profile name, screen name, profile image, location, description, followers count, and friend count. While the screen name, display name, profile image, and description identify the user, the follower and friend counts represent the user's social connectivity. Profile changes might represent identity choice at a small level. However, previous studies have shown that on a broader level, profile changes may be an indication of a rise in a social movement BIBREF1, BIBREF2. In this work, we conduct a large-scale study of the profile change behavior of users on Twitter during the 2019 general elections in India. These elections are of interest from the perspective of social media analysis for many reasons. Firstly, the general elections in India were held in 7 phases from 11th April, 2019 to 19th May, 2019. Thus, the elections serve as a rich source of data for social movements and changing political alignments throughout the two months. Secondly, the increase in the Internet user-base and wider adoption of smartphones has made social media outreach an essential part of the broader election campaign. Thus, there exists a considerable volume of political discourse data that can be mined to gain interesting insights. Twitter was widely used for political discourse even during the 2014 general elections BIBREF3 and the number was bound to increase this year. A piece of evidence in favor of this argument is the enormous 49 million followers that Narendra Modi, the Prime Minister of India has on Twitter. We believe profile attributes have significant potential to understand how social media played a vital role in the election campaign by political parties as well as the supporters during the #LokSabhaElections2019. A prominent example of the use of profile changes for political campaigning is the #MainBhiChowkidar campaign launched by the Bhartiya Janta Party (BJP) during the #LokSabhaElections2019. In Figure FIGREF2, we show the first occurrence of #MainBhiChowkidar campaign on Twitter which was launched in response to an opposition campaign called “Chowkidar chor hai” (The watchman is a thief). On 17th March 2019, the Indian Prime Minister Narendra Modi stepped up this campaign and added Chowkidar (Watchman) to his display name. This spurred off a social movement on Twitter with users from across the nation and political spectrum updating their profiles on Twitter by prefixing “Chowkidar” to their display name BIBREF4. We are thus interested in studying the different facets of this campaign, in particular, the name changes in the display name of accounts, verified, and others. In Figure FIGREF3, we compare the Twitter profiles of the two leaders of national parties during Lok Sabha Election, 2019. The top profile belongs to @narendramodi (Narendra Modi), PM of India and member of the ruling party Bharatiya Janata Party (BJP), while the bottom profile belongs to @RahulGandhi (Rahul Gandhi), president of Indian National Congress (INC). Figure FIGREF3 shows a snapshot of both leaders before, during, and after the Lok Sabha Elections in 2019. The ruling leader showed brevity in the use of his profile information for political campaigning and changed his profile information during and after the elections, which was not the case with opposition. Intending to understand the political discourse from the lens of profile attributes on social media, we address the following questions. Introduction ::: Research Questions To analyze the importance of user-profiles in elections, we need to distinguish between the profile change behavior of political accounts and follower accounts. This brings us to the first research question: RQ1 [Comparison]: How often do political accounts and common user accounts change their profile information and which profile attributes are changed the most. The political inclination of Twitter users can be classified based on the content of their past tweets and community detection methods BIBREF5. However, the same analysis has not been extended to the study of user-profiles in detail. We thus, study the following: RQ2 [Political Inclination]: Do users reveal their political inclination on Twitter directly/indirectly using their user profiles only? Behavior contagion is a phenomenon where individuals adopt a behavior if one of their opinion leaders adopts it BIBREF6. We analyze the profile change behavior of accounts based on the “Chowkidar” movement with the intent to find behavior contagion of an opinion leader. Thus, our third research question is: RQ3 [Behavior Contagion]: Can profile change behavior be catered to behavior contagion effect where followers tend to adopt an opinion as their opinion leader adopts it? We are interested in knowing the common attributes of the people who took part in the chowkidar movement. To characterize these users, we need to analyze the major topic patterns that these users engage in. Thus, we ask the question: RQ4 [Topic Modelling]: What are the most discussed topics amongst the users that were part of the Chowkidar campaign? Introduction ::: Contributions In summary, our main contributions are: We collect daily and weekly profile snapshots of $1,293$ political handles on Twitter and over 55 million of their followers respectively over a period of two months during and after the #LokSabhaElections2019. We will make the data and code available on our website. We analyze the political and follower handles for profile changes over the snapshots and show that the political handles engage in more profile change behavior. We also show that certain profile attributes are changed more frequently than others with subtle differences in the behavior of political and follower accounts. We show users display support for a political party on Twitter through both organic means like party name mentions on their profile, and inorganic means such as showing support for a movement like #MainBhiChowkidar. We analyze the Chowkidar movement in detail and demonstrate that it illustrates a contagion behavior. We perform topic modelling of the users who took part in the Chowkidar movement and show that they were most likely Narendra Modi supporters and mostly discussed political topics on Twitter. Dataset Collection and Description Our data collection process is divided into two stages. We first manually curate a set of political accounts on Twitter. We use this set of users as a seed set and then collect the profile information of all the followers of these handles. Seed set: To answer the RQ1, we first manually curate a set of $2,577$ Twitter handles of politicians and official/unofficial party handles involved in Lok Sabha Elections 2019. Let this set of users be called $\mathbf {U}$. Of the $2,577$ curated handles, only $1,293$ were verified at the beginning of April. Let this set of verified handles be called $\mathbf {P}$. We collect a snapshot of the profile information of $\mathbf {P}$ daily for two months from 5th April - 5th June 2019. Tracked set: We use the set $P$ of verified Twitter handles as a seed set and collect the profile information of all their followers. We hypothesize that the users who follow one or more political handles are more likely to express their political orientation on Twitter. Thus, we consider only the followers of all the political handles in our work. The total number of followers of all the handles in set $\mathbf {P}$ are 600 million, of which only $55,830,844$ users are unique. Owing to Twitter API constraints, we collect snapshots of user-profiles for these handles only once a week over the two months of April 5 - June 5, 2019. There were a total of 9 snapshots, of which 7 coincided with the phases of the election. The exact dates on which the snapshots were taken has been mentioned in Table TABREF10. As we collect snapshots over the of two months, the number of followers of political handles in set $P$ can increase significantly. Similarly, a small subset of followers of handles in $P$ could also have been suspended/deleted. To maintain consistency in our analysis, we only use those handles for our analysis that are present in all the 9 snapshots. We call this set of users as $\mathbf {S}$. The set $\mathbf {S}$ consists of 49,433,640 unique accounts. Dataset: Our dataset consists of daily and weekly snapshots of profile information of users in set $\mathbf {S}$ and $\mathbf {P}$ respectively. We utilize the Twitter API to collect the profile information of users in both the above sets. Twitter API returns a user object that consists of a total of 34 attributes like name, screen name, description, profile image, location, and followers. We further pre-process the dataset to remove all the handles that were inactive for a long time before proceeding with further analysis. In set $\mathbf {S}$, more than 15 million out of the total 49 million users have made no tweets and have marked no favourites as well. We call the remaining 34 million users as Active users. Of the Active users, only 5 million users have made a tweet in year 2019. The distribution of the last tweet time of all users in set $S$ is shown in Figure FIGREF14. The 5 million twitter users since the start of 2019 fall in favor of the argument that a minority of users are only responsible for most of the political opinions BIBREF7. We believe there is a correlation between Active users and users who make changes in profile attribute. We, therefore use the set of 34 million users(Active Users of set $\mathbf {S}$ ) for our further analysis. Profile Attribute Analysis In this section we characterize the profile change behavior of political accounts (Set $P$) and follower accounts (Set $S$). Profile Attribute Analysis ::: Political Handles Increase in the Internet user-base and widespread adoption of smartphones in the country has led to an increase in the importance of social media campaigns during the elections. This is evident by the fact that in our dataset (Set $U$), we found that a total of 54 handles got verified throughout 61 snapshots. Moreover the average number of followers of handles in set $P$ increased from $3,08,716.84$ to $3,19,881.09$ over the 61 snapshots. Given that the average followers of set $P$ increased by approximately $3\%$ and 54 profile handles got verified in the span of 61 days, we believe that efforts of leaders on social media campaign can't be denied. We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively. The username is a unique handle or screen name associated with each twitter user that appears on the profile URL and is used to communicate with each other on Twitter. Some username examples are $@narendramodi$, $@RahulGandhi$ etc. The display name on the other hand, is merely a personal identifier that is displayed on the profile page of a user, e.g. `Narendra Modi', `Rahul Gandhi', etc. The description is a string to describe the account, while the location is user-defined profile location. We considered the 5 profile attributes as stated above since these attributes are key elements of a users identity and saliently define users likes and values BIBREF2. For the set of users in $P$, the total number of changes in these 5 profile attributes over 61 snapshots is 974. Figure FIGREF12 shows the distribution of profile changes for specific attributes over all the 61 snapshots. The most changed attribute among the political handles is the profile image followed by display name. In the same Figure, there is a sharp increase in the number of changes to the display name attribute on 23rd May, 2019. The increase is because @narendramodi removed Chowkidar from the display name of his Twitter handle which resulted in many other political handles doing the same. Another interesting trend is that none of the handles changed their username over all the snapshots. This could be attributed to the fact that Twitter discourages a change of usernames for verified accounts as the verified status might not remain intact after the change BIBREF8. Profile Attribute Analysis ::: Follower Handles To characterize the profile changing behavior amongst the followers , we study the profile snapshots of all the users in set $S$ over 9 snapshots. Of all the active user accounts, $1,363,499$ users made at least one change to their profile attributes over all the snapshots. Thus, over $3\%$ of all the active users engaged in some form of profile change activity over the 9 snapshots. Of these $1,363,499$ users, more than $95\%$ of the users made only 1 change to their profile, and around 275 users made more than 20 changes. On an average, each one of these $1,363,499$ users made about 2 changes to their profiles with a standard deviation of $1.42$. In order to compare the profile change behavior of political accounts and follower accounts, we analyzed the profile changes of set $\mathbf {P}$ over the 9 snapshots only. For the same 9 snapshots, $54.9\%$ of all users in set $\mathbf {P}$ made at least one change to their profile attributes. This is in sharp contrast to the the users of set $\mathbf {S}$, where only $3\%$ of users made any changes to their profile attributes. Similarly, users of set $\mathbf {P}$ made $75.32\%$ changes to their profiles on an average, which is 25 times more than the set $\mathbf {S}$. INSIGHT 1: Political handles are more likely to engage in profile changing behavior as compared to their followers. In Figure FIGREF15, we plot the number of changes in given attributes over all the snapshots for the users in set $S$. From this plot, we find that not all the attributes are modified at an equal rate. Profile image and Location are the most changed profile attributes and account for nearly $34\%$ and $25\%$ respectively of the total profile changes in our dataset. We analyze the trends in Figure FIGREF12 and find that the political handles do not change their usernames at all. This is in contrast to the trend in Figure FIGREF15 where we see that there are a lot of handles that change their usernames multiple times. The most likely reason for the same is that most of the follower handles are not verified and would not loose their verified status on changing their username. The follower handles also use their usernames to show support to a movement or person and revert back to their original username later. Out of the $79,991$ username changes, $1,711$ users of set ${S}$ went back and forth to the same name which included adding of Election related keywords. Examples of few such cases in our dataset are shown in Table TABREF16. INSIGHT 2: Users do not change all profile attributes equally, and the profile attributes that political and follower handles focus on are different. Profile Attribute Analysis ::: Similarity analysis Change in an attribute in the profile involves replacing an existing attribute with a new one. We analyze the similarity between the current, new display names and usernames used by users in set $\mathbf {P}$ and $\mathbf {S}$ respectively. For this analysis, we consider only the users who have changed their username or display name at least once in all 9 snapshots. We use the Longest Common Subsequence (LCS) to compute the similarity between two strings. Thus, for each user, we compute the average LCS score between all possible pairs of unique display names and usernames adopted by them. A high average LCS score indicates that the newer username or display name were not significantly different from the previous ones. The graph in Figure FIGREF18 shows that nearly $80\%$ of the users in set $S$ made more significant changes to their Display Names as compared to the political handles. (This can be seen by considering a horizontal line on the graph. For any given fraction of users, the average LCS score of set $S$ (Followers) is lesser than set $P$ (Politicians) for approximately $80\%$ percent of users). For around $10\%$ of the users in set $P$, the new and old profile attributes were unrelated (LCS score < $0.5$) and for users in set $S$, this value went up to $30\%$. INSIGHT 3: Political handles tend to make new changes related to previous attribute values. However, the followers make comparatively less related changes to previous attribute values. Political support through profile meta data In this section, we first describe the phenomenon of mention of political parties names in the profile attributes of users. This is followed by the analysis of profiles that make specific mentions of political handles in their profile attributes. Both of these constitute an organic way of showing support to a party and does not involve any direct campaigning by the parties. We also study in detail the #MainBhiChowkidar campaign and analyze the corresponding change in profile attributes associated with it. Political support through profile meta data ::: Party mention in the profile attributes To sustain our hypothesis of party mention being an organic behavior, we analyzed party name mentions in the profiles of users in set $\mathbf {S}$. For this analysis, we focused on two of the major national parties in India, namely Indian National Congress (INC) and Bhartiya Janta Party (BJP). We specifically search for mentions of the party names and a few of its variants in the display name, username and description of the user profiles. We find that $50,432$ users have mentioned “BJP” (or its variants) on their description in the first snapshot of our data itself. This constitutes approximately $1\%$ of the total number of active users in our dataset, implying a substantial presence of the party on Twitter. Moreover, $2,638$ users added “BJP” (or its variants) to their description over the course of data collection and $1,638$ users removed “BJP” from their description. Table TABREF20 shows the number of mentions of both parties in the different profile attributes. Political support through profile meta data ::: Who follows who We observe that a lot of users in our dataset wrote that they are proud to be followed by a famous political handle in their description. We show example of such cases in Table TABREF22. We perform an exhaustive search of all such mentions in the description attribute of the users and find $1,164$ instances of this phenomenon. This analysis and the party mention analysis in Section SECREF19, both testify that people often display their political inclination on Twitter via their profile attributes itself. INSIGHT 4: Twitter users often display their political inclinations in their profile attributes itself and are pretty open about it. Political support through profile meta data ::: The Chowkidar movement As discussed is Section SECREF1, @narendramodi added “Chowkidar” to his display name in response to an opposition campaign called “Chowkidar chor hai”. In a coordinated campaign, several other leaders and ministers of BJP also changed their Twitter profiles by adding the prefix Chowkidar to their display names. The movement, however, did not remain confined amongst the members of the party themselves and soon, several Twitter users updated their profiles as well. We opine that the whole campaign of adding Chowkidar to the profile attributes show an inorganic behavior, with political leaders acting as the catalyst. An interesting aspect of this campaign was the fact that the users used several different variants of the word Chowkidar while adding it to their Twitter profiles. Some of the most common variants of Chowkidar that were present in our dataset along with its frequency of use is shown in Figure FIGREF24. We utilize a regular expression query to exhaustively search all the variants of the word Chowkidar in our dataset. We found $2,60,607$ instances of users in set $S$ added Chowkidar (or its variants) to their display names. These $2,60,607$ instances comprised a total of 241 unique chowkidar variants of which 225 have been used lesser than 500 times. We also perform the same analysis for the other profile attributes like description and username as well. The number of users who added Chowkidar to their description and username are $14,409$ and $12,651$ respectively. The union and intersection of all these users are $270,945$ and 727 respectively, implying that most users added Chowkidar to only one of their profile attributes. We believe, the effect of changing the profile attribute in accordance with Prime Minister's campaign is an example of inorganic behavior contagion BIBREF6, BIBREF9. The authors in BIBREF6 argue that opinion diffuses easily in a network if it comes from opinion leaders who are considered to be users with a very high number of followers. We see a similar behavior contagion in our dataset with respect to the Chowkidar movement. We analyze the similarity of the display names with respect to the behavior contagion of Chowkidar movement. In the CDF plot of Figure FIGREF18, a significant spike is observed in the region of LCS values between $0.6$-$0.8$. This spike is caused mostly due to the political handles, who added Chowkidar to their display names which accounted for $95.7\%$ of the users in this region. In set $P$, a total of 373 people added the specific keyword Chowkidar to their display names of which 315 lie in the normalized LCS range of $0.6$ to $0.8$. We perform similar analysis on the users of set $S$ and find that around $57\%$ of the users within the region of $0.6$-$0.8$ LCS added specific keyword Chowkidar to their display names. We perform similar analysis on other campaign keywords like Ab Hoga Nyay as well but none of them have the same level of influence or popularity like that of the “Chowkidar” and hence we call it as the “Chowkidar Movement”. On 23rd May, Narendra Modi removed Chowkidar from his Twitter profile and urged others to do the same as shown in Figure FIGREF25. While most users followed Narendra Modi's instructions and removed Chowkidar from their profiles, some users still continued to add chowkidar to their names. The weekly addition and removal of Chowkidar from user profiles is presented in detail in Figure FIGREF26. Thus, the behavior contagion is evident from the fact that after Narendra Modi removed Chowkidar on 23rd May, majority of the population in set $\mathbf {S}$ removed it too. INSIGHT 5: The Chowkidar movement was very widespread and illustrated contagion behavior. Topic modelling of users A large number of people were part of the Chowkidar movement. Hence, it is important to gauge their level of interest/support to the party in order to accurately judge the impact of the campaign. We thus perform topic modelling of the description attribute of these users. We utilize the LDA Mallet model BIBREF10 for topic modelling after performing the relevant preprocessing steps on the data. We set the number of topics to 10 as it gave the highest coherence score in our experiments. The 10 topics and the 5 most important keywords belonging to these topics are shown below in Figure FIGREF27. These topics reveal several interesting insights. The most discussed topic amongst these users involves Narendra Modi and includes words like “namo” (slang for Narendra Modi), supporter, nationalist and “bhakt” (devout believer). A few other topics also involve politics like topic 2 and topic 5. Topic 2, in particular contains the words chowkidar, student, and study, indicating that a significant set of users who were part of this campaign were actually students. Topic number 0 accounts for more than $40\%$ of the total descriptions, which essentially means that most of the people involved in this movement were supporting BJP or Narendra Modi. We also plot the topics associated with each user's description on a 2D plane by performing tSNE BIBREF11 on the 10 dimensional data to ascertain the variation in the type of topics. The topics are pretty distinct and well separated on the 2D plane as can be seen in Figure FIGREF30. INSIGHT 6: The users who were part of the chowkidar movement were mostly Narendra Modi supporters and engaged in political topics Related Work Social Media profiles are a way to present oneself and to cultivate relationship with like minded or desired audience. BIBREF0, BIBREF12. With change in time and interest, users on Twitter change their profile names, description, screen names, location or language BIBREF2. Some of these changes are organic BIBREF13, BIBREF2, while other changes can be triggered by certain events BIBREF14, BIBREF15. In this section, we look into the previous literature which are related to user profile attribute's change behavior. We further look into how Twitter has been used to initiate or sustain movements BIBREF14, BIBREF15, BIBREF1. Finally we investigate how the contagion behavior adaptation spread through opinion leaders BIBREF6, BIBREF9 and conclude with our work's contribution to the literature. Related Work ::: Profile attribute change behavior Previous studies have focused on change in username attribute extensively, as usernames in Twitter helps in linkability, mentions and becomes part of the profile's URL BIBREF16, BIBREF13, BIBREF17. While the focus of few works is to understand the reason the users change their profile names BIBREF16, BIBREF2, others strongly argue username change is a bad idea BIBREF17, BIBREF13. Jain et al. argue that the main reason people opt to change their username is to leverage more tweet space. Similarly, the work by Mariconti et al. suggests that the change in username may be a result of name squatting, where a person may take away the username of a popular handle for malicious reasons. More recent studies in the area have focused on several profile attributes changes BIBREF2. They suggest that profile attributes can be used to represent oneself at micro-level as well as represent social movement at macro-level. Related Work ::: Social Media for online movements The role of Social Media during social movements have been studied very extensively BIBREF14, BIBREF18, BIBREF19, BIBREF15. With the help of Social Media, participation and ease to express oneself becomes easy BIBREF1. There have been many studies where Twitter has been used to gather the insight of online users in the form of tweets BIBREF1, BIBREF14, BIBREF15. The authors in BIBREF14 study the tweets to understand how people reacted to decriminalization of LGBT in India. The study of Gezi Park protest shows how the protest took a political turn, as people changed their usernames to show support to their political leaders BIBREF1. The success of the party “Alternative for Deutschland(AFD)” in German Federal Elections, 2017 can be catered to its large online presence BIBREF18. The authors in BIBREF18 study the tweets and retweets networks during the German elections and analyze how users are organized into communities and how information flows between them. The role of influential leaders during elections has also been studied in detail in studies like BIBREF20. This brings us to the study of how influential leaders contribute to spread and adaptation of events. Related Work ::: Adoption of behavior from leaders The real and virtual world are intertwined in a way that any speech, event, or action of leaders may trigger the changes is behavior of people online BIBREF1, BIBREF21. Opinion leaders have been defined as `the individuals who were likely to influence other persons in their immediate environment.' BIBREF22 Opinion leaders on social media platforms help in adoption of behavior by followers easily BIBREF9, BIBREF6. The performance and effectiveness of the opinion leader directly affect the contagion of the leader BIBREF9. On Twitter, opinion leaders show high motivation for information seeking and public expression. Moreover, they make a significant contribution to people's political involvement BIBREF21. Our work contributes to this literature in two major ways. First, we investigate how profile attribute changes are similar or different from the influential leaders in the country and how much of these changes are the result of the same. Then we see how the users change their profile information in the midst of election in the largest democracy in the world. Discussion In this paper, we study the use of profile attributes on Twitter as the major endorsement during #LokSabhaElectios2019. We identify $1,293$ verified political leader's profile and extract $49,433,640$ unique followers. We collect the daily and weekly snapshots of the political leaders and the follower handles from 5th April, 2019 to 5th June, 2019. With the constraint of Twitter API, we collected a total of 9 snapshots of the follower's profile and 61 snapshots of Political handles. We consider the 5 major attributes for the analysis, including, Profile Name, Username, Description, Profile Image, and Location. Firstly, we analyze the most changed attributes among the 5 major attributes. We find that most of the users made at-most 4 changes during the election campaign, with profile image being the most changed attribute for both the political as well as follower handles. We also show that political handles made more changes to profile attributes than the follower handles. About $80\%$ changes made by the followers in the period of data collection was related to #LokSabhaElectios2019. While the profile handles made changes, the changes mostly included going back and forth to same profile attribute, with the change in attribute being active support to a political party or political leader. We argue that we can predict the political inclination of a user using just the profile attribute of the users. We further show that the presence of party name in the profile attribute can be considered as an organic behavior and signals support to a party. However, we argue that the addition of election campaign related keywords to the profile is a form of inorganic behavior. The inorganic behavior analysis falls inline with behavior contagion, where the followers tend to adapt to the behavior of their opinion leaders. The “Chowkidar movement” showed a similar effect in the #LokSabhaElectios2019, which was evident by how the other political leaders and followers of BJP party added chowkidar to their profile attributes after @narendramodi did. We thus, argue that people don't shy away from showing support to political parties through profile information. We believe our analysis will be helpful to understand how social movement like election are perceived and sustained through profile attributes on social media. Our findings provide baseline data for study in the direction of election campaign and to further analyze people's perspective on elections.
What profile metadata is used for this analysis?
e374169ee10f835f660ab8403a5701114586f167
What Ġprofile Ġmetadata Ġis Ġused Ġfor Ġthis Ġanalysis ?
username , Ġdisplay Ġname , Ġprofile Ġimage , Ġlocation , Ġdescription
<s> What Ġprofile Ġmetadata Ġis Ġused Ġfor Ġthis Ġanalysis ? </s> Introduction </s> User Ġprofiles Ġon Ġsocial Ġmedia Ġplatforms Ġserve Ġas Ġa Ġvirtual Ġintroduction Ġof Ġthe Ġusers . ĠPeople Ġoften Ġmaintain Ġtheir Ġonline Ġprofile Ġspace Ġto Ġreflect Ġtheir Ġlikes Ġand Ġvalues . ĠFurther , Ġhow Ġusers Ġmaintain Ġtheir Ġprofile Ġhelps Ġthem Ġdevelop Ġrelationship Ġwith Ġcoveted Ġaudience ĠB IB REF 0 . ĠA Ġuser Ġprofile Ġon ĠTwitter Ġis Ġcomposed Ġof Ġseveral Ġattributes Ġwith Ġsome Ġof Ġthe Ġmost Ġprominent Ġones Ġbeing Ġthe Ġprofile Ġname , Ġscreen Ġname , Ġprofile Ġimage , Ġlocation , Ġdescription , Ġfollowers Ġcount , Ġand Ġfriend Ġcount . ĠWhile Ġthe Ġscreen Ġname , Ġdisplay Ġname , Ġprofile Ġimage , Ġand Ġdescription Ġidentify Ġthe Ġuser , Ġthe Ġfollower Ġand Ġfriend Ġcounts Ġrepresent Ġthe Ġuser 's Ġsocial Ġconnectivity . ĠProfile Ġchanges Ġmight Ġrepresent Ġidentity Ġchoice Ġat Ġa Ġsmall Ġlevel . ĠHowever , Ġprevious Ġstudies Ġhave Ġshown Ġthat Ġon Ġa Ġbroader Ġlevel , Ġprofile Ġchanges Ġmay Ġbe Ġan Ġindication Ġof Ġa Ġrise Ġin Ġa Ġsocial Ġmovement ĠB IB REF 1 , ĠB IB REF 2 . </s> In Ġthis Ġwork , Ġwe Ġconduct Ġa Ġlarge - scale Ġstudy Ġof Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġusers Ġon ĠTwitter Ġduring Ġthe Ġ2019 Ġgeneral Ġelections Ġin ĠIndia . ĠThese Ġelections Ġare Ġof Ġinterest Ġfrom Ġthe Ġperspective Ġof Ġsocial Ġmedia Ġanalysis Ġfor Ġmany Ġreasons . ĠFirstly , Ġthe Ġgeneral Ġelections Ġin ĠIndia Ġwere Ġheld Ġin Ġ7 Ġphases Ġfrom Ġ11 th ĠApril , Ġ2019 Ġto Ġ19 th ĠMay , Ġ2019 . ĠThus , Ġthe Ġelections Ġserve Ġas Ġa Ġrich Ġsource Ġof Ġdata Ġfor Ġsocial Ġmovements Ġand Ġchanging Ġpolitical Ġalign ments Ġthroughout Ġthe Ġtwo Ġmonths . ĠSecondly , Ġthe Ġincrease Ġin Ġthe ĠInternet Ġuser - base Ġand Ġwider Ġadoption Ġof Ġsmartphones Ġhas Ġmade Ġsocial Ġmedia Ġoutreach Ġan Ġessential Ġpart Ġof Ġthe Ġbroader Ġelection Ġcampaign . ĠThus , Ġthere Ġexists Ġa Ġconsiderable Ġvolume Ġof Ġpolitical Ġdiscourse Ġdata Ġthat Ġcan Ġbe Ġmined Ġto Ġgain Ġinteresting Ġinsights . ĠTwitter Ġwas Ġwidely Ġused Ġfor Ġpolitical Ġdiscourse Ġeven Ġduring Ġthe Ġ2014 Ġgeneral Ġelections ĠB IB REF 3 Ġand Ġthe Ġnumber Ġwas Ġbound Ġto Ġincrease Ġthis Ġyear . ĠA Ġpiece Ġof Ġevidence Ġin Ġfavor Ġof Ġthis Ġargument Ġis Ġthe Ġenormous Ġ49 Ġmillion Ġfollowers Ġthat ĠNarendra ĠModi , Ġthe ĠPrime ĠMinister Ġof ĠIndia Ġhas Ġon ĠTwitter . </s> We Ġbelieve Ġprofile Ġattributes Ġhave Ġsignificant Ġpotential Ġto Ġunderstand Ġhow Ġsocial Ġmedia Ġplayed Ġa Ġvital Ġrole Ġin Ġthe Ġelection Ġcampaign Ġby Ġpolitical Ġparties Ġas Ġwell Ġas Ġthe Ġsupporters Ġduring Ġthe Ġ# L ok Sab ha Elect ions 2019 . ĠA Ġprominent Ġexample Ġof Ġthe Ġuse Ġof Ġprofile Ġchanges Ġfor Ġpolitical Ġcampaigning Ġis Ġthe Ġ# Main B hi Ch ow kid ar Ġcampaign Ġlaunched Ġby Ġthe ĠBh art iya ĠJ anta ĠParty Ġ( B JP ) Ġduring Ġthe Ġ# L ok Sab ha Elect ions 2019 . ĠIn ĠFigure ĠFIG REF 2 , Ġwe Ġshow Ġthe Ġfirst Ġoccurrence Ġof Ġ# Main B hi Ch ow kid ar Ġcampaign Ġon ĠTwitter Ġwhich Ġwas Ġlaunched Ġin Ġresponse Ġto Ġan Ġopposition Ġcampaign Ġcalled ĠâĢ ľ Ch ow kid ar Ġch or Ġha i âĢ Ŀ Ġ( The Ġwatch man Ġis Ġa Ġthief ). ĠOn Ġ17 th ĠMarch Ġ2019 , Ġthe ĠIndian ĠPrime ĠMinister ĠNarendra ĠModi Ġstepped Ġup Ġthis Ġcampaign Ġand Ġadded ĠChow kid ar Ġ( Watch man ) Ġto Ġhis Ġdisplay Ġname . ĠThis Ġspurred Ġoff Ġa Ġsocial Ġmovement Ġon ĠTwitter Ġwith Ġusers Ġfrom Ġacross Ġthe Ġnation Ġand Ġpolitical Ġspectrum Ġupdating Ġtheir Ġprofiles Ġon ĠTwitter Ġby Ġprefix ing ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġto Ġtheir Ġdisplay Ġname ĠB IB REF 4 . ĠWe Ġare Ġthus Ġinterested Ġin Ġstudying Ġthe Ġdifferent Ġfacets Ġof Ġthis Ġcampaign , Ġin Ġparticular , Ġthe Ġname Ġchanges Ġin Ġthe Ġdisplay Ġname Ġof Ġaccounts , Ġverified , Ġand Ġothers . </s> In ĠFigure ĠFIG REF 3 , Ġwe Ġcompare Ġthe ĠTwitter Ġprofiles Ġof Ġthe Ġtwo Ġleaders Ġof Ġnational Ġparties Ġduring ĠLok ĠSabha ĠElection , Ġ2019 . ĠThe Ġtop Ġprofile Ġbelongs Ġto Ġ@ n are nd ram odi Ġ( N are ndra ĠModi ), ĠPM Ġof ĠIndia Ġand Ġmember Ġof Ġthe Ġruling Ġparty ĠBhar ati ya ĠJan ata ĠParty Ġ( B JP ), Ġwhile Ġthe Ġbottom Ġprofile Ġbelongs Ġto Ġ@ Rah ul G and hi Ġ( Rah ul ĠGandhi ), Ġpresident Ġof ĠIndian ĠNational ĠCongress Ġ( INC ). ĠFigure ĠFIG REF 3 Ġshows Ġa Ġsnapshot Ġof Ġboth Ġleaders Ġbefore , Ġduring , Ġand Ġafter Ġthe ĠLok ĠSabha ĠElections Ġin Ġ2019 . ĠThe Ġruling Ġleader Ġshowed Ġbre vity Ġin Ġthe Ġuse Ġof Ġhis Ġprofile Ġinformation Ġfor Ġpolitical Ġcampaigning Ġand Ġchanged Ġhis Ġprofile Ġinformation Ġduring Ġand Ġafter Ġthe Ġelections , Ġwhich Ġwas Ġnot Ġthe Ġcase Ġwith Ġopposition . </s> Int ending Ġto Ġunderstand Ġthe Ġpolitical Ġdiscourse Ġfrom Ġthe Ġlens Ġof Ġprofile Ġattributes Ġon Ġsocial Ġmedia , Ġwe Ġaddress Ġthe Ġfollowing Ġquestions . </s> Introduction Ġ: :: ĠResearch ĠQuestions </s> To Ġanalyze Ġthe Ġimportance Ġof Ġuser - prof iles Ġin Ġelections , Ġwe Ġneed Ġto Ġdistinguish Ġbetween Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġpolitical Ġaccounts Ġand Ġfollower Ġaccounts . ĠThis Ġbrings Ġus Ġto Ġthe Ġfirst Ġresearch Ġquestion : </s> R Q 1 Ġ[ Compar ison ]: ĠHow Ġoften Ġdo Ġpolitical Ġaccounts Ġand Ġcommon Ġuser Ġaccounts Ġchange Ġtheir Ġprofile Ġinformation Ġand Ġwhich Ġprofile Ġattributes Ġare Ġchanged Ġthe Ġmost . </s> The Ġpolitical Ġinclination Ġof ĠTwitter Ġusers Ġcan Ġbe Ġclassified Ġbased Ġon Ġthe Ġcontent Ġof Ġtheir Ġpast Ġtweets Ġand Ġcommunity Ġdetection Ġmethods ĠB IB REF 5 . ĠHowever , Ġthe Ġsame Ġanalysis Ġhas Ġnot Ġbeen Ġextended Ġto Ġthe Ġstudy Ġof Ġuser - prof iles Ġin Ġdetail . ĠWe Ġthus , Ġstudy Ġthe Ġfollowing : </s> R Q 2 Ġ[ Political ĠIn cl ination ]: ĠDo Ġusers Ġreveal Ġtheir Ġpolitical Ġinclination Ġon ĠTwitter Ġdirectly / ind irect ly Ġusing Ġtheir Ġuser Ġprofiles Ġonly ? </s> Beh avior Ġcontag ion Ġis Ġa Ġphenomenon Ġwhere Ġindividuals Ġadopt Ġa Ġbehavior Ġif Ġone Ġof Ġtheir Ġopinion Ġleaders Ġadop ts Ġit ĠB IB REF 6 . ĠWe Ġanalyze Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġaccounts Ġbased Ġon Ġthe ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġmovement Ġwith Ġthe Ġintent Ġto Ġfind Ġbehavior Ġcontag ion Ġof Ġan Ġopinion Ġleader . ĠThus , Ġour Ġthird Ġresearch Ġquestion Ġis : </s> R Q 3 Ġ[ Beh avior ĠCont ag ion ]: ĠCan Ġprofile Ġchange Ġbehavior Ġbe Ġc atered Ġto Ġbehavior Ġcontag ion Ġeffect Ġwhere Ġfollowers Ġtend Ġto Ġadopt Ġan Ġopinion Ġas Ġtheir Ġopinion Ġleader Ġadop ts Ġit ? </s> We Ġare Ġinterested Ġin Ġknowing Ġthe Ġcommon Ġattributes Ġof Ġthe Ġpeople Ġwho Ġtook Ġpart Ġin Ġthe Ġch ow kid ar Ġmovement . ĠTo Ġcharacterize Ġthese Ġusers , Ġwe Ġneed Ġto Ġanalyze Ġthe Ġmajor Ġtopic Ġpatterns Ġthat Ġthese Ġusers Ġengage Ġin . ĠThus , Ġwe Ġask Ġthe Ġquestion : </s> R Q 4 Ġ[ Topic ĠMod elling ]: ĠWhat Ġare Ġthe Ġmost Ġdiscussed Ġtopics Ġamongst Ġthe Ġusers Ġthat Ġwere Ġpart Ġof Ġthe ĠChow kid ar Ġcampaign ? </s> Introduction Ġ: :: ĠContributions </s> In Ġsummary , Ġour Ġmain Ġcontributions Ġare : </s> We Ġcollect Ġdaily Ġand Ġweekly Ġprofile Ġsnapshots Ġof Ġ$ 1 , 293 $ Ġpolitical Ġhandles Ġon ĠTwitter Ġand Ġover Ġ55 Ġmillion Ġof Ġtheir Ġfollowers Ġrespectively Ġover Ġa Ġperiod Ġof Ġtwo Ġmonths Ġduring Ġand Ġafter Ġthe Ġ# L ok Sab ha Elect ions 2019 . ĠWe Ġwill Ġmake Ġthe Ġdata Ġand Ġcode Ġavailable Ġon Ġour Ġwebsite . </s> We Ġanalyze Ġthe Ġpolitical Ġand Ġfollower Ġhandles Ġfor Ġprofile Ġchanges Ġover Ġthe Ġsnapshots Ġand Ġshow Ġthat Ġthe Ġpolitical Ġhandles Ġengage Ġin Ġmore Ġprofile Ġchange Ġbehavior . ĠWe Ġalso Ġshow Ġthat Ġcertain Ġprofile Ġattributes Ġare Ġchanged Ġmore Ġfrequently Ġthan Ġothers Ġwith Ġsubtle Ġdifferences Ġin Ġthe Ġbehavior Ġof Ġpolitical Ġand Ġfollower Ġaccounts . </s> We Ġshow Ġusers Ġdisplay Ġsupport Ġfor Ġa Ġpolitical Ġparty Ġon ĠTwitter Ġthrough Ġboth Ġorganic Ġmeans Ġlike Ġparty Ġname Ġmentions Ġon Ġtheir Ġprofile , Ġand Ġin organic Ġmeans Ġsuch Ġas Ġshowing Ġsupport Ġfor Ġa Ġmovement Ġlike Ġ# Main B hi Ch ow kid ar . ĠWe Ġanalyze Ġthe ĠChow kid ar Ġmovement Ġin Ġdetail Ġand Ġdemonstrate Ġthat Ġit Ġillustrates Ġa Ġcontag ion Ġbehavior . </s> We Ġperform Ġtopic Ġmodelling Ġof Ġthe Ġusers Ġwho Ġtook Ġpart Ġin Ġthe ĠChow kid ar Ġmovement Ġand Ġshow Ġthat Ġthey Ġwere Ġmost Ġlikely ĠNarendra ĠModi Ġsupporters Ġand Ġmostly Ġdiscussed Ġpolitical Ġtopics Ġon ĠTwitter . </s> Dat as et ĠCollection Ġand ĠDescription </s> Our Ġdata Ġcollection Ġprocess Ġis Ġdivided Ġinto Ġtwo Ġstages . ĠWe Ġfirst Ġmanually Ġcur ate Ġa Ġset Ġof Ġpolitical Ġaccounts Ġon ĠTwitter . ĠWe Ġuse Ġthis Ġset Ġof Ġusers Ġas Ġa Ġseed Ġset Ġand Ġthen Ġcollect Ġthe Ġprofile Ġinformation Ġof Ġall Ġthe Ġfollowers Ġof Ġthese Ġhandles . </s> S eed Ġset : ĠTo Ġanswer Ġthe ĠR Q 1 , Ġwe Ġfirst Ġmanually Ġcur ate Ġa Ġset Ġof Ġ$ 2 , 577 $ ĠTwitter Ġhandles Ġof Ġpoliticians Ġand Ġofficial / un official Ġparty Ġhandles Ġinvolved Ġin ĠLok ĠSabha ĠElections Ġ2019 . ĠLet Ġthis Ġset Ġof Ġusers Ġbe Ġcalled Ġ$\ math bf Ġ{ U } $. ĠOf Ġthe Ġ$ 2 , 577 $ Ġcurated Ġhandles , Ġonly Ġ$ 1 , 293 $ Ġwere Ġverified Ġat Ġthe Ġbeginning Ġof ĠApril . ĠLet Ġthis Ġset Ġof Ġverified Ġhandles Ġbe Ġcalled Ġ$\ math bf Ġ{ P } $. ĠWe Ġcollect Ġa Ġsnapshot Ġof Ġthe Ġprofile Ġinformation Ġof Ġ$\ math bf Ġ{ P } $ Ġdaily Ġfor Ġtwo Ġmonths Ġfrom Ġ5 th ĠApril Ġ- Ġ5 th ĠJune Ġ2019 . </s> Tr acked Ġset : ĠWe Ġuse Ġthe Ġset Ġ$ P $ Ġof Ġverified ĠTwitter Ġhandles Ġas Ġa Ġseed Ġset Ġand Ġcollect Ġthe Ġprofile Ġinformation Ġof Ġall Ġtheir Ġfollowers . ĠWe Ġhypothes ize Ġthat Ġthe Ġusers Ġwho Ġfollow Ġone Ġor Ġmore Ġpolitical Ġhandles Ġare Ġmore Ġlikely Ġto Ġexpress Ġtheir Ġpolitical Ġorientation Ġon ĠTwitter . ĠThus , Ġwe Ġconsider Ġonly Ġthe Ġfollowers Ġof Ġall Ġthe Ġpolitical Ġhandles Ġin Ġour Ġwork . ĠThe Ġtotal Ġnumber Ġof Ġfollowers Ġof Ġall Ġthe Ġhandles Ġin Ġset Ġ$\ math bf Ġ{ P } $ Ġare Ġ600 Ġmillion , Ġof Ġwhich Ġonly Ġ$ 55 , 830 , 8 44 $ Ġusers Ġare Ġunique . ĠO wing Ġto ĠTwitter ĠAPI Ġconstraints , Ġwe Ġcollect Ġsnapshots Ġof Ġuser - prof iles Ġfor Ġthese Ġhandles Ġonly Ġonce Ġa Ġweek Ġover Ġthe Ġtwo Ġmonths Ġof ĠApril Ġ5 Ġ- ĠJune Ġ5 , Ġ2019 . ĠThere Ġwere Ġa Ġtotal Ġof Ġ9 Ġsnapshots , Ġof Ġwhich Ġ7 Ġcoincided Ġwith Ġthe Ġphases Ġof Ġthe Ġelection . ĠThe Ġexact Ġdates Ġon Ġwhich Ġthe Ġsnapshots Ġwere Ġtaken Ġhas Ġbeen Ġmentioned Ġin ĠTable ĠT AB REF 10 . ĠAs Ġwe Ġcollect Ġsnapshots Ġover Ġthe Ġof Ġtwo Ġmonths , Ġthe Ġnumber Ġof Ġfollowers Ġof Ġpolitical Ġhandles Ġin Ġset Ġ$ P $ Ġcan Ġincrease Ġsignificantly . ĠSimilarly , Ġa Ġsmall Ġsubset Ġof Ġfollowers Ġof Ġhandles Ġin Ġ$ P $ Ġcould Ġalso Ġhave Ġbeen Ġsuspended / de leted . ĠTo Ġmaintain Ġconsistency Ġin Ġour Ġanalysis , Ġwe Ġonly Ġuse Ġthose Ġhandles Ġfor Ġour Ġanalysis Ġthat Ġare Ġpresent Ġin Ġall Ġthe Ġ9 Ġsnapshots . ĠWe Ġcall Ġthis Ġset Ġof Ġusers Ġas Ġ$\ math bf Ġ{ S } $. ĠThe Ġset Ġ$\ math bf Ġ{ S } $ Ġconsists Ġof Ġ49 , 433 , 640 Ġunique Ġaccounts . </s> Dat as et : ĠOur Ġdataset Ġconsists Ġof Ġdaily Ġand Ġweekly Ġsnapshots Ġof Ġprofile Ġinformation Ġof Ġusers Ġin Ġset Ġ$\ math bf Ġ{ S } $ Ġand Ġ$\ math bf Ġ{ P } $ Ġrespectively . ĠWe Ġutilize Ġthe ĠTwitter ĠAPI Ġto Ġcollect Ġthe Ġprofile Ġinformation Ġof Ġusers Ġin Ġboth Ġthe Ġabove Ġsets . ĠTwitter ĠAPI Ġreturns Ġa Ġuser Ġobject Ġthat Ġconsists Ġof Ġa Ġtotal Ġof Ġ34 Ġattributes Ġlike Ġname , Ġscreen Ġname , Ġdescription , Ġprofile Ġimage , Ġlocation , Ġand Ġfollowers . ĠWe Ġfurther Ġpre - process Ġthe Ġdataset Ġto Ġremove Ġall Ġthe Ġhandles Ġthat Ġwere Ġinactive Ġfor Ġa Ġlong Ġtime Ġbefore Ġproceeding Ġwith Ġfurther Ġanalysis . </s> In Ġset Ġ$\ math bf Ġ{ S } $, Ġmore Ġthan Ġ15 Ġmillion Ġout Ġof Ġthe Ġtotal Ġ49 Ġmillion Ġusers Ġhave Ġmade Ġno Ġtweets Ġand Ġhave Ġmarked Ġno Ġfavourites Ġas Ġwell . ĠWe Ġcall Ġthe Ġremaining Ġ34 Ġmillion Ġusers Ġas ĠActive Ġusers . ĠOf Ġthe ĠActive Ġusers , Ġonly Ġ5 Ġmillion Ġusers Ġhave Ġmade Ġa Ġtweet Ġin Ġyear Ġ2019 . ĠThe Ġdistribution Ġof Ġthe Ġlast Ġtweet Ġtime Ġof Ġall Ġusers Ġin Ġset Ġ$ S $ Ġis Ġshown Ġin ĠFigure ĠFIG REF 14 . ĠThe Ġ5 Ġmillion Ġtwitter Ġusers Ġsince Ġthe Ġstart Ġof Ġ2019 Ġfall Ġin Ġfavor Ġof Ġthe Ġargument Ġthat Ġa Ġminority Ġof Ġusers Ġare Ġonly Ġresponsible Ġfor Ġmost Ġof Ġthe Ġpolitical Ġopinions ĠB IB REF 7 . ĠWe Ġbelieve Ġthere Ġis Ġa Ġcorrelation Ġbetween ĠActive Ġusers Ġand Ġusers Ġwho Ġmake Ġchanges Ġin Ġprofile Ġattribute . ĠWe , Ġtherefore Ġuse Ġthe Ġset Ġof Ġ34 Ġmillion Ġusers ( Active ĠUsers Ġof Ġset Ġ$\ math bf Ġ{ S } $ Ġ) Ġfor Ġour Ġfurther Ġanalysis . </s> Profile ĠAtt ribute ĠAnalysis </s> In Ġthis Ġsection Ġwe Ġcharacterize Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġpolitical Ġaccounts Ġ( Set Ġ$ P $ ) Ġand Ġfollower Ġaccounts Ġ( Set Ġ$ S $ ). </s> Profile ĠAtt ribute ĠAnalysis Ġ: :: ĠPolitical ĠHand les </s> Increase Ġin Ġthe ĠInternet Ġuser - base Ġand Ġwidespread Ġadoption Ġof Ġsmartphones Ġin Ġthe Ġcountry Ġhas Ġled Ġto Ġan Ġincrease Ġin Ġthe Ġimportance Ġof Ġsocial Ġmedia Ġcampaigns Ġduring Ġthe Ġelections . ĠThis Ġis Ġevident Ġby Ġthe Ġfact Ġthat Ġin Ġour Ġdataset Ġ( Set Ġ$ U $ ), Ġwe Ġfound Ġthat Ġa Ġtotal Ġof Ġ54 Ġhandles Ġgot Ġverified Ġthroughout Ġ61 Ġsnapshots . ĠMoreover Ġthe Ġaverage Ġnumber Ġof Ġfollowers Ġof Ġhandles Ġin Ġset Ġ$ P $ Ġincreased Ġfrom Ġ$ 3 , 08 , 7 16 . 84 $ Ġto Ġ$ 3 , 19 , 88 1 . 09 $ Ġover Ġthe Ġ61 Ġsnapshots . ĠGiven Ġthat Ġthe Ġaverage Ġfollowers Ġof Ġset Ġ$ P $ Ġincreased Ġby Ġapproximately Ġ$ 3 \ % $ Ġand Ġ54 Ġprofile Ġhandles Ġgot Ġverified Ġin Ġthe Ġspan Ġof Ġ61 Ġdays , Ġwe Ġbelieve Ġthat Ġefforts Ġof Ġleaders Ġon Ġsocial Ġmedia Ġcampaign Ġcan 't Ġbe Ġdenied . </s> We Ġconsider Ġ5 Ġmajor Ġprofile Ġattributes Ġfor Ġfurther Ġanalysis . ĠThese Ġattributes Ġare Ġusername , Ġdisplay Ġname , Ġprofile Ġimage , Ġlocation Ġand Ġdescription Ġrespectively . ĠThe Ġusername Ġis Ġa Ġunique Ġhandle Ġor Ġscreen Ġname Ġassociated Ġwith Ġeach Ġtwitter Ġuser Ġthat Ġappears Ġon Ġthe Ġprofile ĠURL Ġand Ġis Ġused Ġto Ġcommunicate Ġwith Ġeach Ġother Ġon ĠTwitter . ĠSome Ġusername Ġexamples Ġare Ġ$ @ n are nd ram odi $, Ġ$ @ Rah ul G and hi $ Ġetc . ĠThe Ġdisplay Ġname Ġon Ġthe Ġother Ġhand , Ġis Ġmerely Ġa Ġpersonal Ġidentifier Ġthat Ġis Ġdisplayed Ġon Ġthe Ġprofile Ġpage Ġof Ġa Ġuser , Ġe . g . Ġ` N are ndra ĠModi ', Ġ` Rah ul ĠGandhi ', Ġetc . ĠThe Ġdescription Ġis Ġa Ġstring Ġto Ġdescribe Ġthe Ġaccount , Ġwhile Ġthe Ġlocation Ġis Ġuser - defined Ġprofile Ġlocation . ĠWe Ġconsidered Ġthe Ġ5 Ġprofile Ġattributes Ġas Ġstated Ġabove Ġsince Ġthese Ġattributes Ġare Ġkey Ġelements Ġof Ġa Ġusers Ġidentity Ġand Ġsalient ly Ġdefine Ġusers Ġlikes Ġand Ġvalues ĠB IB REF 2 . </s> For Ġthe Ġset Ġof Ġusers Ġin Ġ$ P $, Ġthe Ġtotal Ġnumber Ġof Ġchanges Ġin Ġthese Ġ5 Ġprofile Ġattributes Ġover Ġ61 Ġsnapshots Ġis Ġ9 74 . ĠFigure ĠFIG REF 12 Ġshows Ġthe Ġdistribution Ġof Ġprofile Ġchanges Ġfor Ġspecific Ġattributes Ġover Ġall Ġthe Ġ61 Ġsnapshots . ĠThe Ġmost Ġchanged Ġattribute Ġamong Ġthe Ġpolitical Ġhandles Ġis Ġthe Ġprofile Ġimage Ġfollowed Ġby Ġdisplay Ġname . ĠIn Ġthe Ġsame ĠFigure , Ġthere Ġis Ġa Ġsharp Ġincrease Ġin Ġthe Ġnumber Ġof Ġchanges Ġto Ġthe Ġdisplay Ġname Ġattribute Ġon Ġ23 rd ĠMay , Ġ2019 . ĠThe Ġincrease Ġis Ġbecause Ġ@ n are nd ram odi Ġremoved ĠChow kid ar Ġfrom Ġthe Ġdisplay Ġname Ġof Ġhis ĠTwitter Ġhandle Ġwhich Ġresulted Ġin Ġmany Ġother Ġpolitical Ġhandles Ġdoing Ġthe Ġsame . ĠAnother Ġinteresting Ġtrend Ġis Ġthat Ġnone Ġof Ġthe Ġhandles Ġchanged Ġtheir Ġusername Ġover Ġall Ġthe Ġsnapshots . ĠThis Ġcould Ġbe Ġattributed Ġto Ġthe Ġfact Ġthat ĠTwitter Ġdiscour ages Ġa Ġchange Ġof Ġus ern ames Ġfor Ġverified Ġaccounts Ġas Ġthe Ġverified Ġstatus Ġmight Ġnot Ġremain Ġintact Ġafter Ġthe Ġchange ĠB IB REF 8 . </s> Profile ĠAtt ribute ĠAnalysis Ġ: :: ĠF ollower ĠHand les </s> To Ġcharacterize Ġthe Ġprofile Ġchanging Ġbehavior Ġamongst Ġthe Ġfollowers Ġ, Ġwe Ġstudy Ġthe Ġprofile Ġsnapshots Ġof Ġall Ġthe Ġusers Ġin Ġset Ġ$ S $ Ġover Ġ9 Ġsnapshots . </s> Of Ġall Ġthe Ġactive Ġuser Ġaccounts , Ġ$ 1 , 363 , 499 $ Ġusers Ġmade Ġat Ġleast Ġone Ġchange Ġto Ġtheir Ġprofile Ġattributes Ġover Ġall Ġthe Ġsnapshots . ĠThus , Ġover Ġ$ 3 \ % $ Ġof Ġall Ġthe Ġactive Ġusers Ġengaged Ġin Ġsome Ġform Ġof Ġprofile Ġchange Ġactivity Ġover Ġthe Ġ9 Ġsnapshots . ĠOf Ġthese Ġ$ 1 , 363 , 499 $ Ġusers , Ġmore Ġthan Ġ$ 95 \ % $ Ġof Ġthe Ġusers Ġmade Ġonly Ġ1 Ġchange Ġto Ġtheir Ġprofile , Ġand Ġaround Ġ275 Ġusers Ġmade Ġmore Ġthan Ġ20 Ġchanges . ĠOn Ġan Ġaverage , Ġeach Ġone Ġof Ġthese Ġ$ 1 , 363 , 499 $ Ġusers Ġmade Ġabout Ġ2 Ġchanges Ġto Ġtheir Ġprofiles Ġwith Ġa Ġstandard Ġdeviation Ġof Ġ$ 1 . 42 $. </s> In Ġorder Ġto Ġcompare Ġthe Ġprofile Ġchange Ġbehavior Ġof Ġpolitical Ġaccounts Ġand Ġfollower Ġaccounts , Ġwe Ġanalyzed Ġthe Ġprofile Ġchanges Ġof Ġset Ġ$\ math bf Ġ{ P } $ Ġover Ġthe Ġ9 Ġsnapshots Ġonly . ĠFor Ġthe Ġsame Ġ9 Ġsnapshots , Ġ$ 54 . 9 \ % $ Ġof Ġall Ġusers Ġin Ġset Ġ$\ math bf Ġ{ P } $ Ġmade Ġat Ġleast Ġone Ġchange Ġto Ġtheir Ġprofile Ġattributes . ĠThis Ġis Ġin Ġsharp Ġcontrast Ġto Ġthe Ġthe Ġusers Ġof Ġset Ġ$\ math bf Ġ{ S } $, Ġwhere Ġonly Ġ$ 3 \ % $ Ġof Ġusers Ġmade Ġany Ġchanges Ġto Ġtheir Ġprofile Ġattributes . ĠSimilarly , Ġusers Ġof Ġset Ġ$\ math bf Ġ{ P } $ Ġmade Ġ$ 75 . 32 \ % $ Ġchanges Ġto Ġtheir Ġprofiles Ġon Ġan Ġaverage , Ġwhich Ġis Ġ25 Ġtimes Ġmore Ġthan Ġthe Ġset Ġ$\ math bf Ġ{ S } $. </s> INS IGHT Ġ1 : ĠPolitical Ġhandles Ġare Ġmore Ġlikely Ġto Ġengage Ġin Ġprofile Ġchanging Ġbehavior Ġas Ġcompared Ġto Ġtheir Ġfollowers . </s> In ĠFigure ĠFIG REF 15 , Ġwe Ġplot Ġthe Ġnumber Ġof Ġchanges Ġin Ġgiven Ġattributes Ġover Ġall Ġthe Ġsnapshots Ġfor Ġthe Ġusers Ġin Ġset Ġ$ S $. ĠFrom Ġthis Ġplot , Ġwe Ġfind Ġthat Ġnot Ġall Ġthe Ġattributes Ġare Ġmodified Ġat Ġan Ġequal Ġrate . ĠProfile Ġimage Ġand ĠLocation Ġare Ġthe Ġmost Ġchanged Ġprofile Ġattributes Ġand Ġaccount Ġfor Ġnearly Ġ$ 34 \ % $ Ġand Ġ$ 25 \ % $ Ġrespectively Ġof Ġthe Ġtotal Ġprofile Ġchanges Ġin Ġour Ġdataset . ĠWe Ġanalyze Ġthe Ġtrends Ġin ĠFigure ĠFIG REF 12 Ġand Ġfind Ġthat Ġthe Ġpolitical Ġhandles Ġdo Ġnot Ġchange Ġtheir Ġus ern ames Ġat Ġall . ĠThis Ġis Ġin Ġcontrast Ġto Ġthe Ġtrend Ġin ĠFigure ĠFIG REF 15 Ġwhere Ġwe Ġsee Ġthat Ġthere Ġare Ġa Ġlot Ġof Ġhandles Ġthat Ġchange Ġtheir Ġus ern ames Ġmultiple Ġtimes . ĠThe Ġmost Ġlikely Ġreason Ġfor Ġthe Ġsame Ġis Ġthat Ġmost Ġof Ġthe Ġfollower Ġhandles Ġare Ġnot Ġverified Ġand Ġwould Ġnot Ġloose Ġtheir Ġverified Ġstatus Ġon Ġchanging Ġtheir Ġusername . </s> The Ġfollower Ġhandles Ġalso Ġuse Ġtheir Ġus ern ames Ġto Ġshow Ġsupport Ġto Ġa Ġmovement Ġor Ġperson Ġand Ġrevert Ġback Ġto Ġtheir Ġoriginal Ġusername Ġlater . ĠOut Ġof Ġthe Ġ$ 79 , 99 1 $ Ġusername Ġchanges , Ġ$ 1 , 7 11 $ Ġusers Ġof Ġset Ġ${ S } $ Ġwent Ġback Ġand Ġforth Ġto Ġthe Ġsame Ġname Ġwhich Ġincluded Ġadding Ġof ĠElection Ġrelated Ġkeywords . ĠExamples Ġof Ġfew Ġsuch Ġcases Ġin Ġour Ġdataset Ġare Ġshown Ġin ĠTable ĠT AB REF 16 . </s> INS IGHT Ġ2 : ĠUsers Ġdo Ġnot Ġchange Ġall Ġprofile Ġattributes Ġequally , Ġand Ġthe Ġprofile Ġattributes Ġthat Ġpolitical Ġand Ġfollower Ġhandles Ġfocus Ġon Ġare Ġdifferent . </s> Profile ĠAtt ribute ĠAnalysis Ġ: :: ĠSimilar ity Ġanalysis </s> Change Ġin Ġan Ġattribute Ġin Ġthe Ġprofile Ġinvolves Ġreplacing Ġan Ġexisting Ġattribute Ġwith Ġa Ġnew Ġone . ĠWe Ġanalyze Ġthe Ġsimilarity Ġbetween Ġthe Ġcurrent , Ġnew Ġdisplay Ġnames Ġand Ġus ern ames Ġused Ġby Ġusers Ġin Ġset Ġ$\ math bf Ġ{ P } $ Ġand Ġ$\ math bf Ġ{ S } $ Ġrespectively . ĠFor Ġthis Ġanalysis , Ġwe Ġconsider Ġonly Ġthe Ġusers Ġwho Ġhave Ġchanged Ġtheir Ġusername Ġor Ġdisplay Ġname Ġat Ġleast Ġonce Ġin Ġall Ġ9 Ġsnapshots . ĠWe Ġuse Ġthe ĠLong est ĠCommon ĠSub sequence Ġ( LCS ) Ġto Ġcompute Ġthe Ġsimilarity Ġbetween Ġtwo Ġstrings . ĠThus , Ġfor Ġeach Ġuser , Ġwe Ġcompute Ġthe Ġaverage ĠLCS Ġscore Ġbetween Ġall Ġpossible Ġpairs Ġof Ġunique Ġdisplay Ġnames Ġand Ġus ern ames Ġadopted Ġby Ġthem . ĠA Ġhigh Ġaverage ĠLCS Ġscore Ġindicates Ġthat Ġthe Ġnewer Ġusername Ġor Ġdisplay Ġname Ġwere Ġnot Ġsignificantly Ġdifferent Ġfrom Ġthe Ġprevious Ġones . </s> The Ġgraph Ġin ĠFigure ĠFIG REF 18 Ġshows Ġthat Ġnearly Ġ$ 80 \ % $ Ġof Ġthe Ġusers Ġin Ġset Ġ$ S $ Ġmade Ġmore Ġsignificant Ġchanges Ġto Ġtheir ĠDisplay ĠNames Ġas Ġcompared Ġto Ġthe Ġpolitical Ġhandles . Ġ( This Ġcan Ġbe Ġseen Ġby Ġconsidering Ġa Ġhorizontal Ġline Ġon Ġthe Ġgraph . ĠFor Ġany Ġgiven Ġfraction Ġof Ġusers , Ġthe Ġaverage ĠLCS Ġscore Ġof Ġset Ġ$ S $ Ġ( Follow ers ) Ġis Ġlesser Ġthan Ġset Ġ$ P $ Ġ( Polit icians ) Ġfor Ġapproximately Ġ$ 80 \ % $ Ġpercent Ġof Ġusers ). ĠFor Ġaround Ġ$ 10 \ % $ Ġof Ġthe Ġusers Ġin Ġset Ġ$ P $, Ġthe Ġnew Ġand Ġold Ġprofile Ġattributes Ġwere Ġunrelated Ġ( LCS Ġscore Ġ< Ġ$ 0 . 5 $ ) Ġand Ġfor Ġusers Ġin Ġset Ġ$ S $, Ġthis Ġvalue Ġwent Ġup Ġto Ġ$ 30 \ % $. </s> INS IGHT Ġ3 : ĠPolitical Ġhandles Ġtend Ġto Ġmake Ġnew Ġchanges Ġrelated Ġto Ġprevious Ġattribute Ġvalues . ĠHowever , Ġthe Ġfollowers Ġmake Ġcomparatively Ġless Ġrelated Ġchanges Ġto Ġprevious Ġattribute Ġvalues . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata </s> In Ġthis Ġsection , Ġwe Ġfirst Ġdescribe Ġthe Ġphenomenon Ġof Ġmention Ġof Ġpolitical Ġparties Ġnames Ġin Ġthe Ġprofile Ġattributes Ġof Ġusers . ĠThis Ġis Ġfollowed Ġby Ġthe Ġanalysis Ġof Ġprofiles Ġthat Ġmake Ġspecific Ġmentions Ġof Ġpolitical Ġhandles Ġin Ġtheir Ġprofile Ġattributes . ĠBoth Ġof Ġthese Ġconstitute Ġan Ġorganic Ġway Ġof Ġshowing Ġsupport Ġto Ġa Ġparty Ġand Ġdoes Ġnot Ġinvolve Ġany Ġdirect Ġcampaigning Ġby Ġthe Ġparties . ĠWe Ġalso Ġstudy Ġin Ġdetail Ġthe Ġ# Main B hi Ch ow kid ar Ġcampaign Ġand Ġanalyze Ġthe Ġcorresponding Ġchange Ġin Ġprofile Ġattributes Ġassociated Ġwith Ġit . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata Ġ: :: ĠParty Ġmention Ġin Ġthe Ġprofile Ġattributes </s> To Ġsustain Ġour Ġhypothesis Ġof Ġparty Ġmention Ġbeing Ġan Ġorganic Ġbehavior , Ġwe Ġanalyzed Ġparty Ġname Ġmentions Ġin Ġthe Ġprofiles Ġof Ġusers Ġin Ġset Ġ$\ math bf Ġ{ S } $. ĠFor Ġthis Ġanalysis , Ġwe Ġfocused Ġon Ġtwo Ġof Ġthe Ġmajor Ġnational Ġparties Ġin ĠIndia , Ġnamely ĠIndian ĠNational ĠCongress Ġ( INC ) Ġand ĠBh art iya ĠJ anta ĠParty Ġ( B JP ). ĠWe Ġspecifically Ġsearch Ġfor Ġmentions Ġof Ġthe Ġparty Ġnames Ġand Ġa Ġfew Ġof Ġits Ġvariants Ġin Ġthe Ġdisplay Ġname , Ġusername Ġand Ġdescription Ġof Ġthe Ġuser Ġprofiles . ĠWe Ġfind Ġthat Ġ$ 50 , 432 $ Ġusers Ġhave Ġmentioned ĠâĢ ľ B JP âĢ Ŀ Ġ( or Ġits Ġvariants ) Ġon Ġtheir Ġdescription Ġin Ġthe Ġfirst Ġsnapshot Ġof Ġour Ġdata Ġitself . ĠThis Ġconstitutes Ġapproximately Ġ$ 1 \ % $ Ġof Ġthe Ġtotal Ġnumber Ġof Ġactive Ġusers Ġin Ġour Ġdataset , Ġimplying Ġa Ġsubstantial Ġpresence Ġof Ġthe Ġparty Ġon ĠTwitter . ĠMoreover , Ġ$ 2 , 6 38 $ Ġusers Ġadded ĠâĢ ľ B JP âĢ Ŀ Ġ( or Ġits Ġvariants ) Ġto Ġtheir Ġdescription Ġover Ġthe Ġcourse Ġof Ġdata Ġcollection Ġand Ġ$ 1 , 6 38 $ Ġusers Ġremoved ĠâĢ ľ B JP âĢ Ŀ Ġfrom Ġtheir Ġdescription . ĠTable ĠT AB REF 20 Ġshows Ġthe Ġnumber Ġof Ġmentions Ġof Ġboth Ġparties Ġin Ġthe Ġdifferent Ġprofile Ġattributes . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata Ġ: :: ĠWho Ġfollows Ġwho </s> We Ġobserve Ġthat Ġa Ġlot Ġof Ġusers Ġin Ġour Ġdataset Ġwrote Ġthat Ġthey Ġare Ġproud Ġto Ġbe Ġfollowed Ġby Ġa Ġfamous Ġpolitical Ġhandle Ġin Ġtheir Ġdescription . ĠWe Ġshow Ġexample Ġof Ġsuch Ġcases Ġin ĠTable ĠT AB REF 22 . </s> We Ġperform Ġan Ġexhaustive Ġsearch Ġof Ġall Ġsuch Ġmentions Ġin Ġthe Ġdescription Ġattribute Ġof Ġthe Ġusers Ġand Ġfind Ġ$ 1 , 164 $ Ġinstances Ġof Ġthis Ġphenomenon . ĠThis Ġanalysis Ġand Ġthe Ġparty Ġmention Ġanalysis Ġin ĠSection ĠSEC REF 19 , Ġboth Ġtestify Ġthat Ġpeople Ġoften Ġdisplay Ġtheir Ġpolitical Ġinclination Ġon ĠTwitter Ġvia Ġtheir Ġprofile Ġattributes Ġitself . </s> INS IGHT Ġ4 : ĠTwitter Ġusers Ġoften Ġdisplay Ġtheir Ġpolitical Ġincl inations Ġin Ġtheir Ġprofile Ġattributes Ġitself Ġand Ġare Ġpretty Ġopen Ġabout Ġit . </s> Political Ġsupport Ġthrough Ġprofile Ġmeta Ġdata Ġ: :: ĠThe ĠChow kid ar Ġmovement </s> As Ġdiscussed Ġis ĠSection ĠSEC REF 1 , Ġ@ n are nd ram odi Ġadded ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġto Ġhis Ġdisplay Ġname Ġin Ġresponse Ġto Ġan Ġopposition Ġcampaign Ġcalled ĠâĢ ľ Ch ow kid ar Ġch or Ġha i âĢ Ŀ . ĠIn Ġa Ġcoordinated Ġcampaign , Ġseveral Ġother Ġleaders Ġand Ġministers Ġof ĠBJP Ġalso Ġchanged Ġtheir ĠTwitter Ġprofiles Ġby Ġadding Ġthe Ġprefix ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames . ĠThe Ġmovement , Ġhowever , Ġdid Ġnot Ġremain Ġconfined Ġamongst Ġthe Ġmembers Ġof Ġthe Ġparty Ġthemselves Ġand Ġsoon , Ġseveral ĠTwitter Ġusers Ġupdated Ġtheir Ġprofiles Ġas Ġwell . ĠWe Ġop ine Ġthat Ġthe Ġwhole Ġcampaign Ġof Ġadding ĠChow kid ar Ġto Ġthe Ġprofile Ġattributes Ġshow Ġan Ġin organic Ġbehavior , Ġwith Ġpolitical Ġleaders Ġacting Ġas Ġthe Ġcatalyst . ĠAn Ġinteresting Ġaspect Ġof Ġthis Ġcampaign Ġwas Ġthe Ġfact Ġthat Ġthe Ġusers Ġused Ġseveral Ġdifferent Ġvariants Ġof Ġthe Ġword ĠChow kid ar Ġwhile Ġadding Ġit Ġto Ġtheir ĠTwitter Ġprofiles . ĠSome Ġof Ġthe Ġmost Ġcommon Ġvariants Ġof ĠChow kid ar Ġthat Ġwere Ġpresent Ġin Ġour Ġdataset Ġalong Ġwith Ġits Ġfrequency Ġof Ġuse Ġis Ġshown Ġin ĠFigure ĠFIG REF 24 . </s> We Ġutilize Ġa Ġregular Ġexpression Ġquery Ġto Ġexhaust ively Ġsearch Ġall Ġthe Ġvariants Ġof Ġthe Ġword ĠChow kid ar Ġin Ġour Ġdataset . ĠWe Ġfound Ġ$ 2 , 60 , 607 $ Ġinstances Ġof Ġusers Ġin Ġset Ġ$ S $ Ġadded ĠChow kid ar Ġ( or Ġits Ġvariants ) Ġto Ġtheir Ġdisplay Ġnames . ĠThese Ġ$ 2 , 60 , 607 $ Ġinstances Ġcomprised Ġa Ġtotal Ġof Ġ241 Ġunique Ġch ow kid ar Ġvariants Ġof Ġwhich Ġ225 Ġhave Ġbeen Ġused Ġlesser Ġthan Ġ500 Ġtimes . ĠWe Ġalso Ġperform Ġthe Ġsame Ġanalysis Ġfor Ġthe Ġother Ġprofile Ġattributes Ġlike Ġdescription Ġand Ġusername Ġas Ġwell . ĠThe Ġnumber Ġof Ġusers Ġwho Ġadded ĠChow kid ar Ġto Ġtheir Ġdescription Ġand Ġusername Ġare Ġ$ 14 , 409 $ Ġand Ġ$ 12 , 651 $ Ġrespectively . ĠThe Ġunion Ġand Ġintersection Ġof Ġall Ġthese Ġusers Ġare Ġ$ 270 , 9 45 $ Ġand Ġ7 27 Ġrespectively , Ġimplying Ġthat Ġmost Ġusers Ġadded ĠChow kid ar Ġto Ġonly Ġone Ġof Ġtheir Ġprofile Ġattributes . </s> We Ġbelieve , Ġthe Ġeffect Ġof Ġchanging Ġthe Ġprofile Ġattribute Ġin Ġaccordance Ġwith ĠPrime ĠMinister 's Ġcampaign Ġis Ġan Ġexample Ġof Ġin organic Ġbehavior Ġcontag ion ĠB IB REF 6 , ĠB IB REF 9 . ĠThe Ġauthors Ġin ĠB IB REF 6 Ġargue Ġthat Ġopinion Ġdiff uses Ġeasily Ġin Ġa Ġnetwork Ġif Ġit Ġcomes Ġfrom Ġopinion Ġleaders Ġwho Ġare Ġconsidered Ġto Ġbe Ġusers Ġwith Ġa Ġvery Ġhigh Ġnumber Ġof Ġfollowers . ĠWe Ġsee Ġa Ġsimilar Ġbehavior Ġcontag ion Ġin Ġour Ġdataset Ġwith Ġrespect Ġto Ġthe ĠChow kid ar Ġmovement . </s> We Ġanalyze Ġthe Ġsimilarity Ġof Ġthe Ġdisplay Ġnames Ġwith Ġrespect Ġto Ġthe Ġbehavior Ġcontag ion Ġof ĠChow kid ar Ġmovement . ĠIn Ġthe ĠCD F Ġplot Ġof ĠFigure ĠFIG REF 18 , Ġa Ġsignificant Ġspike Ġis Ġobserved Ġin Ġthe Ġregion Ġof ĠLCS Ġvalues Ġbetween Ġ$ 0 . 6 $ -$ 0 . 8 $. ĠThis Ġspike Ġis Ġcaused Ġmostly Ġdue Ġto Ġthe Ġpolitical Ġhandles , Ġwho Ġadded ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames Ġwhich Ġaccounted Ġfor Ġ$ 95 . 7 \ % $ Ġof Ġthe Ġusers Ġin Ġthis Ġregion . ĠIn Ġset Ġ$ P $, Ġa Ġtotal Ġof Ġ373 Ġpeople Ġadded Ġthe Ġspecific Ġkeyword ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames Ġof Ġwhich Ġ315 Ġlie Ġin Ġthe Ġnormalized ĠLCS Ġrange Ġof Ġ$ 0 . 6 $ Ġto Ġ$ 0 . 8 $. ĠWe Ġperform Ġsimilar Ġanalysis Ġon Ġthe Ġusers Ġof Ġset Ġ$ S $ Ġand Ġfind Ġthat Ġaround Ġ$ 57 \ % $ Ġof Ġthe Ġusers Ġwithin Ġthe Ġregion Ġof Ġ$ 0 . 6 $ -$ 0 . 8 $ ĠLCS Ġadded Ġspecific Ġkeyword ĠChow kid ar Ġto Ġtheir Ġdisplay Ġnames . ĠWe Ġperform Ġsimilar Ġanalysis Ġon Ġother Ġcampaign Ġkeywords Ġlike ĠAb ĠH oga ĠNy ay Ġas Ġwell Ġbut Ġnone Ġof Ġthem Ġhave Ġthe Ġsame Ġlevel Ġof Ġinfluence Ġor Ġpopularity Ġlike Ġthat Ġof Ġthe ĠâĢ ľ Ch ow kid ar âĢ Ŀ Ġand Ġhence Ġwe Ġcall Ġit Ġas Ġthe ĠâĢ ľ Ch ow kid ar ĠMovement âĢ Ŀ . </s> On Ġ23 rd ĠMay , ĠNarendra ĠModi Ġremoved ĠChow kid ar Ġfrom Ġhis ĠTwitter Ġprofile Ġand Ġurged Ġothers Ġto Ġdo Ġthe Ġsame Ġas Ġshown Ġin ĠFigure ĠFIG REF 25 . ĠWhile Ġmost Ġusers Ġfollowed ĠNarendra ĠModi 's Ġinstructions Ġand Ġremoved ĠChow kid ar Ġfrom Ġtheir Ġprofiles , Ġsome Ġusers Ġstill Ġcontinued Ġto Ġadd Ġch ow kid ar Ġto Ġtheir Ġnames . ĠThe Ġweekly Ġaddition Ġand Ġremoval Ġof ĠChow kid ar Ġfrom Ġuser Ġprofiles Ġis Ġpresented Ġin Ġdetail Ġin ĠFigure ĠFIG REF 26 . ĠThus , Ġthe Ġbehavior Ġcontag ion Ġis Ġevident Ġfrom Ġthe Ġfact Ġthat Ġafter ĠNarendra ĠModi Ġremoved ĠChow kid ar Ġon Ġ23 rd ĠMay , Ġmajority Ġof Ġthe Ġpopulation Ġin Ġset Ġ$\ math bf Ġ{ S } $ Ġremoved Ġit Ġtoo . </s> INS IGHT Ġ5 : ĠThe ĠChow kid ar Ġmovement Ġwas Ġvery Ġwidespread Ġand Ġillustrated Ġcontag ion Ġbehavior . </s> Topic Ġmodelling Ġof Ġusers </s> A Ġlarge Ġnumber Ġof Ġpeople Ġwere Ġpart Ġof Ġthe ĠChow kid ar Ġmovement . ĠHence , Ġit Ġis Ġimportant Ġto Ġgauge Ġtheir Ġlevel Ġof Ġinterest / support Ġto Ġthe Ġparty Ġin Ġorder Ġto Ġaccurately Ġjudge Ġthe Ġimpact Ġof Ġthe Ġcampaign . ĠWe Ġthus Ġperform Ġtopic Ġmodelling Ġof Ġthe Ġdescription Ġattribute Ġof Ġthese Ġusers . ĠWe Ġutilize Ġthe ĠL DA ĠMal let Ġmodel ĠB IB REF 10 Ġfor Ġtopic Ġmodelling Ġafter Ġperforming Ġthe Ġrelevant Ġpre processing Ġsteps Ġon Ġthe Ġdata . ĠWe Ġset Ġthe Ġnumber Ġof Ġtopics Ġto Ġ10 Ġas Ġit Ġgave Ġthe Ġhighest Ġco herence Ġscore Ġin Ġour Ġexperiments . ĠThe Ġ10 Ġtopics Ġand Ġthe Ġ5 Ġmost Ġimportant Ġkeywords Ġbelonging Ġto Ġthese Ġtopics Ġare Ġshown Ġbelow Ġin ĠFigure ĠFIG REF 27 . ĠThese Ġtopics Ġreveal Ġseveral Ġinteresting Ġinsights . ĠThe Ġmost Ġdiscussed Ġtopic Ġamongst Ġthese Ġusers Ġinvolves ĠNarendra ĠModi Ġand Ġincludes Ġwords Ġlike ĠâĢ ľ nam o âĢ Ŀ Ġ( sl ang Ġfor ĠNarendra ĠModi ), Ġsupporter , Ġnationalist Ġand ĠâĢ ľ bh ak t âĢ Ŀ Ġ( dev out Ġbeliever ). ĠA Ġfew Ġother Ġtopics Ġalso Ġinvolve Ġpolitics Ġlike Ġtopic Ġ2 Ġand Ġtopic Ġ5 . ĠTopic Ġ2 , Ġin Ġparticular Ġcontains Ġthe Ġwords Ġch ow kid ar , Ġstudent , Ġand Ġstudy , Ġindicating Ġthat Ġa Ġsignificant Ġset Ġof Ġusers Ġwho Ġwere Ġpart Ġof Ġthis Ġcampaign Ġwere Ġactually Ġstudents . ĠTopic Ġnumber Ġ0 Ġaccounts Ġfor Ġmore Ġthan Ġ$ 40 \ % $ Ġof Ġthe Ġtotal Ġdescriptions , Ġwhich Ġessentially Ġmeans Ġthat Ġmost Ġof Ġthe Ġpeople Ġinvolved Ġin Ġthis Ġmovement Ġwere Ġsupporting ĠBJP Ġor ĠNarendra ĠModi . ĠWe Ġalso Ġplot Ġthe Ġtopics Ġassociated Ġwith Ġeach Ġuser 's Ġdescription Ġon Ġa Ġ2 D Ġplane Ġby Ġperforming Ġt S NE ĠB IB REF 11 Ġon Ġthe Ġ10 Ġdimensional Ġdata Ġto Ġascertain Ġthe Ġvariation Ġin Ġthe Ġtype Ġof Ġtopics . ĠThe Ġtopics Ġare Ġpretty Ġdistinct Ġand Ġwell Ġseparated Ġon Ġthe Ġ2 D Ġplane Ġas Ġcan Ġbe Ġseen Ġin ĠFigure ĠFIG REF 30 . </s> INS IGHT Ġ6 : ĠThe Ġusers Ġwho Ġwere Ġpart Ġof Ġthe Ġch ow kid ar Ġmovement Ġwere Ġmostly ĠNarendra ĠModi Ġsupporters Ġand Ġengaged Ġin Ġpolitical Ġtopics </s> Related ĠWork </s> Social ĠMedia Ġprofiles Ġare Ġa Ġway Ġto Ġpresent Ġoneself Ġand Ġto Ġcultivate Ġrelationship Ġwith Ġlike Ġminded Ġor Ġdesired Ġaudience . ĠB IB REF 0 , ĠB IB REF 12 . ĠWith Ġchange Ġin Ġtime Ġand Ġinterest , Ġusers Ġon ĠTwitter Ġchange Ġtheir Ġprofile Ġnames , Ġdescription , Ġscreen Ġnames , Ġlocation Ġor Ġlanguage ĠB IB REF 2 . ĠSome Ġof Ġthese Ġchanges Ġare Ġorganic ĠB IB REF 13 , ĠB IB REF 2 , Ġwhile Ġother Ġchanges Ġcan Ġbe Ġtriggered Ġby Ġcertain Ġevents ĠB IB REF 14 , ĠB IB REF 15 . ĠIn Ġthis Ġsection , Ġwe Ġlook Ġinto Ġthe Ġprevious Ġliterature Ġwhich Ġare Ġrelated Ġto Ġuser Ġprofile Ġattribute 's Ġchange Ġbehavior . ĠWe Ġfurther Ġlook Ġinto Ġhow ĠTwitter Ġhas Ġbeen Ġused Ġto Ġinitiate Ġor Ġsustain Ġmovements ĠB IB REF 14 , ĠB IB REF 15 , ĠB IB REF 1 . ĠFinally Ġwe Ġinvestigate Ġhow Ġthe Ġcontag ion Ġbehavior Ġadaptation Ġspread Ġthrough Ġopinion Ġleaders ĠB IB REF 6 , ĠB IB REF 9 Ġand Ġconclude Ġwith Ġour Ġwork 's Ġcontribution Ġto Ġthe Ġliterature . </s> Related ĠWork Ġ: :: ĠProfile Ġattribute Ġchange Ġbehavior </s> Previous Ġstudies Ġhave Ġfocused Ġon Ġchange Ġin Ġusername Ġattribute Ġextensively , Ġas Ġus ern ames Ġin ĠTwitter Ġhelps Ġin Ġlink ability , Ġmentions Ġand Ġbecomes Ġpart Ġof Ġthe Ġprofile 's ĠURL ĠB IB REF 16 , ĠB IB REF 13 , ĠB IB REF 17 . ĠWhile Ġthe Ġfocus Ġof Ġfew Ġworks Ġis Ġto Ġunderstand Ġthe Ġreason Ġthe Ġusers Ġchange Ġtheir Ġprofile Ġnames ĠB IB REF 16 , ĠB IB REF 2 , Ġothers Ġstrongly Ġargue Ġusername Ġchange Ġis Ġa Ġbad Ġidea ĠB IB REF 17 , ĠB IB REF 13 . ĠJ ain Ġet Ġal . Ġargue Ġthat Ġthe Ġmain Ġreason Ġpeople Ġopt Ġto Ġchange Ġtheir Ġusername Ġis Ġto Ġleverage Ġmore Ġtweet Ġspace . ĠSimilarly , Ġthe Ġwork Ġby ĠMar ic ont i Ġet Ġal . Ġsuggests Ġthat Ġthe Ġchange Ġin Ġusername Ġmay Ġbe Ġa Ġresult Ġof Ġname Ġsquat ting , Ġwhere Ġa Ġperson Ġmay Ġtake Ġaway Ġthe Ġusername Ġof Ġa Ġpopular Ġhandle Ġfor Ġmalicious Ġreasons . ĠMore Ġrecent Ġstudies Ġin Ġthe Ġarea Ġhave Ġfocused Ġon Ġseveral Ġprofile Ġattributes Ġchanges ĠB IB REF 2 . ĠThey Ġsuggest Ġthat Ġprofile Ġattributes Ġcan Ġbe Ġused Ġto Ġrepresent Ġoneself Ġat Ġmicro - level Ġas Ġwell Ġas Ġrepresent Ġsocial Ġmovement Ġat Ġmacro - level . </s> Related ĠWork Ġ: :: ĠSocial ĠMedia Ġfor Ġonline Ġmovements </s> The Ġrole Ġof ĠSocial ĠMedia Ġduring Ġsocial Ġmovements Ġhave Ġbeen Ġstudied Ġvery Ġextensively ĠB IB REF 14 , ĠB IB REF 18 , ĠB IB REF 19 , ĠB IB REF 15 . ĠWith Ġthe Ġhelp Ġof ĠSocial ĠMedia , Ġparticipation Ġand Ġease Ġto Ġexpress Ġoneself Ġbecomes Ġeasy ĠB IB REF 1 . ĠThere Ġhave Ġbeen Ġmany Ġstudies Ġwhere ĠTwitter Ġhas Ġbeen Ġused Ġto Ġgather Ġthe Ġinsight Ġof Ġonline Ġusers Ġin Ġthe Ġform Ġof Ġtweets ĠB IB REF 1 , ĠB IB REF 14 , ĠB IB REF 15 . ĠThe Ġauthors Ġin ĠB IB REF 14 Ġstudy Ġthe Ġtweets Ġto Ġunderstand Ġhow Ġpeople Ġreacted Ġto Ġdecriminal ization Ġof ĠLGBT Ġin ĠIndia . ĠThe Ġstudy Ġof ĠGe zi ĠPark Ġprotest Ġshows Ġhow Ġthe Ġprotest Ġtook Ġa Ġpolitical Ġturn , Ġas Ġpeople Ġchanged Ġtheir Ġus ern ames Ġto Ġshow Ġsupport Ġto Ġtheir Ġpolitical Ġleaders ĠB IB REF 1 . </s> The Ġsuccess Ġof Ġthe Ġparty ĠâĢ ľ Alternative Ġfor ĠDe utsch land ( AF D ) âĢ Ŀ Ġin ĠGerman ĠFederal ĠElections , Ġ2017 Ġcan Ġbe Ġc atered Ġto Ġits Ġlarge Ġonline Ġpresence ĠB IB REF 18 . ĠThe Ġauthors Ġin ĠB IB REF 18 Ġstudy Ġthe Ġtweets Ġand Ġret we ets Ġnetworks Ġduring Ġthe ĠGerman Ġelections Ġand Ġanalyze Ġhow Ġusers Ġare Ġorganized Ġinto Ġcommunities Ġand Ġhow Ġinformation Ġflows Ġbetween Ġthem . ĠThe Ġrole Ġof Ġinfluential Ġleaders Ġduring Ġelections Ġhas Ġalso Ġbeen Ġstudied Ġin Ġdetail Ġin Ġstudies Ġlike ĠB IB REF 20 . ĠThis Ġbrings Ġus Ġto Ġthe Ġstudy Ġof Ġhow Ġinfluential Ġleaders Ġcontribute Ġto Ġspread Ġand Ġadaptation Ġof Ġevents . </s> Related ĠWork Ġ: :: ĠAd option Ġof Ġbehavior Ġfrom Ġleaders </s> The Ġreal Ġand Ġvirtual Ġworld Ġare Ġintertwined Ġin Ġa Ġway Ġthat Ġany Ġspeech , Ġevent , Ġor Ġaction Ġof Ġleaders Ġmay Ġtrigger Ġthe Ġchanges Ġis Ġbehavior Ġof Ġpeople Ġonline ĠB IB REF 1 , ĠB IB REF 21 . ĠOpinion Ġleaders Ġhave Ġbeen Ġdefined Ġas Ġ` the Ġindividuals Ġwho Ġwere Ġlikely Ġto Ġinfluence Ġother Ġpersons Ġin Ġtheir Ġimmediate Ġenvironment .' ĠB IB REF 22 ĠOpinion Ġleaders Ġon Ġsocial Ġmedia Ġplatforms Ġhelp Ġin Ġadoption Ġof Ġbehavior Ġby Ġfollowers Ġeasily ĠB IB REF 9 , ĠB IB REF 6 . ĠThe Ġperformance Ġand Ġeffectiveness Ġof Ġthe Ġopinion Ġleader Ġdirectly Ġaffect Ġthe Ġcontag ion Ġof Ġthe Ġleader ĠB IB REF 9 . ĠOn ĠTwitter , Ġopinion Ġleaders Ġshow Ġhigh Ġmotivation Ġfor Ġinformation Ġseeking Ġand Ġpublic Ġexpression . ĠMoreover , Ġthey Ġmake Ġa Ġsignificant Ġcontribution Ġto Ġpeople 's Ġpolitical Ġinvolvement ĠB IB REF 21 . </s> Our Ġwork Ġcontributes Ġto Ġthis Ġliterature Ġin Ġtwo Ġmajor Ġways . ĠFirst , Ġwe Ġinvestigate Ġhow Ġprofile Ġattribute Ġchanges Ġare Ġsimilar Ġor Ġdifferent Ġfrom Ġthe Ġinfluential Ġleaders Ġin Ġthe Ġcountry Ġand Ġhow Ġmuch Ġof Ġthese Ġchanges Ġare Ġthe Ġresult Ġof Ġthe Ġsame . ĠThen Ġwe Ġsee Ġhow Ġthe Ġusers Ġchange Ġtheir Ġprofile Ġinformation Ġin Ġthe Ġmidst Ġof Ġelection Ġin Ġthe Ġlargest Ġdemocracy Ġin Ġthe Ġworld . </s> Discussion </s> In Ġthis Ġpaper , Ġwe Ġstudy Ġthe Ġuse Ġof Ġprofile Ġattributes Ġon ĠTwitter Ġas Ġthe Ġmajor Ġendorsement Ġduring Ġ# L ok Sab ha Elect ios 2019 . ĠWe Ġidentify Ġ$ 1 , 293 $ Ġverified Ġpolitical Ġleader 's Ġprofile Ġand Ġextract Ġ$ 49 , 433 , 640 $ Ġunique Ġfollowers . ĠWe Ġcollect Ġthe Ġdaily Ġand Ġweekly Ġsnapshots Ġof Ġthe Ġpolitical Ġleaders Ġand Ġthe Ġfollower Ġhandles Ġfrom Ġ5 th ĠApril , Ġ2019 Ġto Ġ5 th ĠJune , Ġ2019 . ĠWith Ġthe Ġconstraint Ġof ĠTwitter ĠAPI , Ġwe Ġcollected Ġa Ġtotal Ġof Ġ9 Ġsnapshots Ġof Ġthe Ġfollower 's Ġprofile Ġand Ġ61 Ġsnapshots Ġof ĠPolitical Ġhandles . ĠWe Ġconsider Ġthe Ġ5 Ġmajor Ġattributes Ġfor Ġthe Ġanalysis , Ġincluding , ĠProfile ĠName , ĠUsername , ĠDescription , ĠProfile ĠImage , Ġand ĠLocation . </s> Firstly , Ġwe Ġanalyze Ġthe Ġmost Ġchanged Ġattributes Ġamong Ġthe Ġ5 Ġmajor Ġattributes . ĠWe Ġfind Ġthat Ġmost Ġof Ġthe Ġusers Ġmade Ġat - most Ġ4 Ġchanges Ġduring Ġthe Ġelection Ġcampaign , Ġwith Ġprofile Ġimage Ġbeing Ġthe Ġmost Ġchanged Ġattribute Ġfor Ġboth Ġthe Ġpolitical Ġas Ġwell Ġas Ġfollower Ġhandles . ĠWe Ġalso Ġshow Ġthat Ġpolitical Ġhandles Ġmade Ġmore Ġchanges Ġto Ġprofile Ġattributes Ġthan Ġthe Ġfollower Ġhandles . ĠAbout Ġ$ 80 \ % $ Ġchanges Ġmade Ġby Ġthe Ġfollowers Ġin Ġthe Ġperiod Ġof Ġdata Ġcollection Ġwas Ġrelated Ġto Ġ# L ok Sab ha Elect ios 2019 . ĠWhile Ġthe Ġprofile Ġhandles Ġmade Ġchanges , Ġthe Ġchanges Ġmostly Ġincluded Ġgoing Ġback Ġand Ġforth Ġto Ġsame Ġprofile Ġattribute , Ġwith Ġthe Ġchange Ġin Ġattribute Ġbeing Ġactive Ġsupport Ġto Ġa Ġpolitical Ġparty Ġor Ġpolitical Ġleader . </s> We Ġargue Ġthat Ġwe Ġcan Ġpredict Ġthe Ġpolitical Ġinclination Ġof Ġa Ġuser Ġusing Ġjust Ġthe Ġprofile Ġattribute Ġof Ġthe Ġusers . ĠWe Ġfurther Ġshow Ġthat Ġthe Ġpresence Ġof Ġparty Ġname Ġin Ġthe Ġprofile Ġattribute Ġcan Ġbe Ġconsidered Ġas Ġan Ġorganic Ġbehavior Ġand Ġsignals Ġsupport Ġto Ġa Ġparty . ĠHowever , Ġwe Ġargue Ġthat Ġthe Ġaddition Ġof Ġelection Ġcampaign Ġrelated Ġkeywords Ġto Ġthe Ġprofile Ġis Ġa Ġform Ġof Ġin organic Ġbehavior . ĠThe Ġin organic Ġbehavior Ġanalysis Ġfalls Ġinline Ġwith Ġbehavior Ġcontag ion , Ġwhere Ġthe Ġfollowers Ġtend Ġto Ġadapt Ġto Ġthe Ġbehavior Ġof Ġtheir Ġopinion Ġleaders . ĠThe ĠâĢ ľ Ch ow kid ar Ġmovement âĢ Ŀ Ġshowed Ġa Ġsimilar Ġeffect Ġin Ġthe Ġ# L ok Sab ha Elect ios 2019 , Ġwhich Ġwas Ġevident Ġby Ġhow Ġthe Ġother Ġpolitical Ġleaders Ġand Ġfollowers Ġof ĠBJP Ġparty Ġadded Ġch ow kid ar Ġto Ġtheir Ġprofile Ġattributes Ġafter Ġ@ n are nd ram odi Ġdid . ĠWe Ġthus , Ġargue Ġthat Ġpeople Ġdon 't Ġshy Ġaway Ġfrom Ġshowing Ġsupport Ġto Ġpolitical Ġparties Ġthrough Ġprofile Ġinformation . </s> We Ġbelieve Ġour Ġanalysis Ġwill Ġbe Ġhelpful Ġto Ġunderstand Ġhow Ġsocial Ġmovement Ġlike Ġelection Ġare Ġperceived Ġand Ġsustained Ġthrough Ġprofile Ġattributes Ġon Ġsocial Ġmedia . ĠOur Ġfindings Ġprovide Ġbaseline Ġdata Ġfor Ġstudy Ġin Ġthe Ġdirection Ġof Ġelection Ġcampaign Ġand Ġto Ġfurther Ġanalyze Ġpeople 's Ġperspective Ġon Ġelections .