using the Meaning Extraction Method
EXTRACTIVE
Unanswerable
NONE
Values. We also measure the usage of words related to people's core values as reported by Boyd et al. boyd2015. The sets of words, or themes, were excavated using the Meaning Extraction Method (MEM) BIBREF10 . MEM is a topic modeling approach applied to a corpus of texts created by hundreds of survey respondents from the U.S. who were asked to freely write about their personal values. To illustrate, Figure FIGREF9 shows the geographical distributions of two of these value themes: Religion and Hard Work. Southeastern states often considered as the nation's “Bible Belt” BIBREF11 were found to have generally higher usage of Religion words such as God, bible, and church. Another broad trend was that western-central states (e.g., Wyoming, Nebraska, Iowa) commonly blogged about Hard Work, using words such as hard, work, and job more often than bloggers in other regions.
1612.06685
Introduction </s> Bl ogging Ġgained Ġmomentum Ġin Ġ1999 Ġand Ġbecame Ġespecially Ġpopular Ġafter Ġthe Ġlaunch Ġof Ġfreely Ġavailable , Ġhosted Ġplatforms Ġsuch Ġas Ġblogger . com Ġor Ġlive journal . com . ĠBlog ging Ġhas Ġprogressively Ġbeen Ġused Ġby Ġindividuals Ġto Ġshare Ġnews , Ġideas , Ġand Ġinformation , Ġbut Ġit Ġhas Ġalso Ġdeveloped Ġa Ġmainstream Ġrole Ġto Ġthe Ġextent Ġthat Ġit Ġis Ġbeing Ġused Ġby Ġpolitical Ġconsultants Ġand Ġnews Ġservices Ġas Ġa Ġtool Ġfor Ġoutreach Ġand Ġopinion Ġforming Ġas Ġwell Ġas Ġby Ġbusinesses Ġas Ġa Ġmarketing Ġtool Ġto Ġpromote Ġproducts Ġand Ġservices ĠB IB REF 0 Ġ. </s> For Ġthis Ġpaper , Ġwe Ġcompiled Ġa Ġvery Ġlarge Ġge ol ocated Ġcollection Ġof Ġblogs , Ġwritten Ġby Ġindividuals Ġlocated Ġin Ġthe ĠU . S ., Ġwith Ġthe Ġpurpose Ġof Ġcreating Ġinsightful Ġm appings Ġof Ġthe Ġblogging Ġcommunity . ĠIn Ġparticular , Ġduring ĠMay - July Ġ2015 , Ġwe Ġgathered Ġthe Ġprofile Ġinformation Ġfor Ġall Ġthe Ġusers Ġthat Ġhave Ġself - reported Ġtheir Ġlocation Ġin Ġthe ĠU . S ., Ġalong Ġwith Ġa Ġnumber Ġof Ġposts Ġfor Ġall Ġtheir Ġassociated Ġblogs . ĠWe Ġutilize Ġthis Ġblog Ġcollection Ġto Ġgenerate Ġmaps Ġof Ġthe ĠU . S . Ġthat Ġreflect Ġuser Ġdemographics , Ġlanguage Ġuse , Ġand Ġdistributions Ġof Ġpsych ol ingu istic Ġand Ġsemantic Ġword Ġclasses . ĠWe Ġbelieve Ġthat Ġthese Ġmaps Ġcan Ġprovide Ġvaluable Ġinsights Ġand Ġpartial Ġverification Ġof Ġprevious Ġclaims Ġin Ġsupport Ġof Ġresearch Ġin Ġlinguistic Ġgeography ĠB IB REF 1 Ġ, Ġregional Ġpersonality ĠB IB REF 2 Ġ, Ġand Ġlanguage Ġanalysis ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, Ġas Ġwell Ġas Ġpsychology Ġand Ġits Ġrelation Ġto Ġhuman Ġgeography ĠB IB REF 5 Ġ. </s> Data ĠCollection </s> Our Ġpremise Ġis Ġthat Ġwe Ġcan Ġgenerate Ġinformative Ġmaps Ġusing Ġge ol ocated Ġinformation Ġavailable Ġon Ġsocial Ġmedia ; Ġtherefore , Ġwe Ġguide Ġthe Ġblog Ġcollection Ġprocess Ġwith Ġthe Ġconstraint Ġthat Ġwe Ġonly Ġaccept Ġblogs Ġthat Ġhave Ġspecific Ġlocation Ġinformation . ĠMoreover , Ġwe Ġaim Ġto Ġfind Ġblogs Ġbelonging Ġto Ġwriters Ġfrom Ġall Ġ50 ĠU . S . Ġstates , Ġwhich Ġwill Ġallow Ġus Ġto Ġbuild ĠU . S . Ġmaps Ġfor Ġvarious Ġdimensions Ġof Ġinterest . </s> We Ġfirst Ġstarted Ġby Ġcollecting Ġa Ġset Ġof Ġprofiles Ġof Ġbloggers Ġthat Ġmet Ġour Ġlocation Ġspecifications Ġby Ġsearching Ġindividual Ġstates Ġon Ġthe Ġprofile Ġfind er Ġon Ġhttp :// www . blog ger . com . ĠStarting Ġwith Ġthis Ġlist , Ġwe Ġcan Ġlocate Ġthe Ġprofile Ġpage Ġfor Ġa Ġuser , Ġand Ġsubsequently Ġextract Ġadditional Ġinformation , Ġwhich Ġincludes Ġfields Ġsuch Ġas Ġname , Ġemail , Ġoccupation , Ġindustry , Ġand Ġso Ġforth . ĠIt Ġis Ġimportant Ġto Ġnote Ġthat Ġthe Ġprofile Ġfind er Ġonly Ġidentifies Ġusers Ġthat Ġhave Ġan Ġexact Ġmatch Ġto Ġthe Ġlocation Ġspecified Ġin Ġthe Ġquery ; Ġwe Ġthus Ġbuilt Ġand Ġran Ġqueries Ġthat Ġused Ġboth Ġstate Ġabbrevi ations Ġ( e . g ., ĠTX , ĠAL ), Ġas Ġwell Ġas Ġthe Ġstates ' Ġfull Ġnames Ġ( e . g ., ĠTexas , ĠAlabama ). </s> After Ġcompleting Ġall Ġthe Ġprocessing Ġsteps , Ġwe Ġidentified Ġ197 , 5 27 Ġbloggers Ġwith Ġstate Ġlocation Ġinformation . ĠFor Ġeach Ġof Ġthese Ġbloggers , Ġwe Ġfound Ġtheir Ġblogs Ġ( note Ġthat Ġa Ġblogger Ġcan Ġhave Ġmultiple Ġblogs ), Ġfor Ġa Ġtotal Ġof Ġ335 , 698 Ġblogs . ĠFor Ġeach Ġof Ġthese Ġblogs , Ġwe Ġdownloaded Ġthe Ġ21 Ġmost Ġrecent Ġblog Ġpostings , Ġwhich Ġwere Ġcleaned Ġof ĠHTML Ġtags Ġand Ġtoken ized , Ġresulting Ġin Ġa Ġcollection Ġof Ġ4 , 600 , 465 Ġblog Ġposts . </s> Maps Ġfrom ĠBlog s </s> Our Ġdataset Ġprovides Ġm appings Ġbetween Ġlocation , Ġprofile Ġinformation , Ġand Ġlanguage Ġuse , Ġwhich Ġwe Ġcan Ġleverage Ġto Ġgenerate Ġmaps Ġthat Ġreflect Ġdemographic , Ġlinguistic , Ġand Ġpsych ol ingu istic Ġproperties Ġof Ġthe Ġpopulation Ġrepresented Ġin Ġthe Ġdataset . </s> People ĠMaps </s> The Ġfirst Ġmap Ġwe Ġgenerate Ġdepicts Ġthe Ġdistribution Ġof Ġthe Ġbloggers Ġin Ġour Ġdataset Ġacross Ġthe ĠU . S . ĠFigure ĠFIG REF 1 Ġshows Ġthe Ġdensity Ġof Ġusers Ġin Ġour Ġdataset Ġin Ġeach Ġof Ġthe Ġ50 Ġstates . ĠFor Ġinstance , Ġthe Ġdens est Ġstate Ġwas Ġfound Ġto Ġbe ĠCalifornia Ġwith Ġ11 , 701 Ġusers . ĠThe Ġsecond Ġdens est Ġis ĠTexas , Ġwith Ġ9 , 252 Ġusers , Ġfollowed Ġby ĠNew ĠYork , Ġwith Ġ9 , 136 . ĠThe Ġstate Ġwith Ġthe Ġfew est Ġbloggers Ġis ĠDelaware Ġwith Ġ1 , 217 Ġusers . ĠNot Ġsurprisingly , Ġthis Ġdistribution Ġcorrelates Ġwell Ġwith Ġthe Ġpopulation Ġof Ġthese Ġstates , Ġwith Ġa ĠSpear man 's Ġrank Ġcorrelation ĠIN LINE FORM 0 Ġof Ġ0 . 91 Ġand Ġa Ġp - value ĠIN LINE FORM 1 Ġ0 . 0001 , Ġand Ġis Ġvery Ġsimilar Ġto Ġthe Ġone Ġreported Ġin ĠLin Ġand ĠHal ava is ĠLin 04 . </s> Figure ĠFIG REF 1 Ġalso Ġshows Ġthe Ġcities Ġmentioned Ġmost Ġoften Ġin Ġour Ġdataset . ĠIn Ġparticular , Ġit Ġillustrates Ġall Ġ227 Ġcities Ġthat Ġhave Ġat Ġleast Ġ100 Ġbloggers . ĠThe Ġbigger Ġthe Ġdot Ġon Ġthe Ġmap , Ġthe Ġlarger Ġthe Ġnumber Ġof Ġusers Ġfound Ġin Ġthat Ġcity . ĠThe Ġfive Ġtop Ġblogger - d ense Ġcities , Ġin Ġorder , Ġare : ĠChicago , ĠNew ĠYork , ĠPortland , ĠSeattle , Ġand ĠAtlanta . </s> We Ġalso Ġgenerate Ġtwo Ġmaps Ġthat Ġdeline ate Ġthe Ġgender Ġdistribution Ġin Ġthe Ġdataset . ĠOverall , Ġthe Ġblogging Ġworld Ġseems Ġto Ġbe Ġdominated Ġby Ġfemales : Ġout Ġof Ġ153 , 209 Ġusers Ġwho Ġself - reported Ġtheir Ġgender , Ġonly Ġ52 , 725 Ġare Ġmen Ġand Ġ100 , 484 Ġare Ġwomen . ĠFigures ĠFIG REF 1 Ġand ĠFIG REF 1 Ġshow Ġthe Ġpercentage Ġof Ġmale Ġand Ġfemale Ġbloggers Ġin Ġeach Ġof Ġthe Ġ50 Ġstates . ĠAs Ġseen Ġin Ġthis Ġfigure , Ġthere Ġare Ġmore Ġthan Ġthe Ġaverage Ġnumber Ġof Ġmale Ġbloggers Ġin Ġstates Ġsuch Ġas ĠCalifornia Ġand ĠNew ĠYork , Ġwhereas ĠUtah Ġand ĠIdaho Ġhave Ġa Ġhigher Ġpercentage Ġof Ġwomen Ġbloggers . </s> Another Ġprofile Ġelement Ġthat Ġcan Ġlead Ġto Ġinteresting Ġmaps Ġis Ġthe ĠIndustry Ġfield ĠB IB REF 6 Ġ. ĠUsing Ġthis Ġfield , Ġwe Ġcreated Ġdifferent Ġmaps Ġthat Ġplot Ġthe Ġgeographical Ġdistribution Ġof Ġindustries Ġacross Ġthe Ġcountry . ĠAs Ġan Ġexample , ĠFigure ĠFIG REF 2 Ġshows Ġthe Ġpercentage Ġof Ġthe Ġusers Ġin Ġeach Ġstate Ġworking Ġin Ġthe Ġautomotive Ġand Ġtourism Ġindustries Ġrespectively . </s> L ingu istic ĠMaps </s> Another Ġuse Ġof Ġthe Ġinformation Ġfound Ġin Ġour Ġdataset Ġis Ġto Ġbuild Ġlinguistic Ġmaps , Ġwhich Ġreflect Ġthe Ġgeographic Ġlex ical Ġvariation Ġacross Ġthe Ġ50 Ġstates ĠB IB REF 7 Ġ. ĠWe Ġgenerate Ġmaps Ġthat Ġrepresent Ġthe Ġrelative Ġfrequency Ġby Ġwhich Ġa Ġword Ġoccurs Ġin Ġthe Ġdifferent Ġstates . ĠFigure ĠFIG REF 3 Ġshows Ġsample Ġmaps Ġcreated Ġfor Ġtwo Ġdifferent Ġwords . ĠThe Ġfigure Ġshows Ġthe Ġmap Ġgenerated Ġfor Ġone Ġlocation Ġspecific Ġword , ĠMau i , Ġwhich Ġunsurprisingly Ġis Ġfound Ġpredominantly Ġin ĠHawaii , Ġand Ġa Ġmap Ġfor Ġa Ġmore Ġcommon Ġword , Ġlake , Ġwhich Ġhas Ġa Ġhigh Ġoccurrence Ġrate Ġin ĠMinnesota Ġ( Land Ġof Ġ10 , 000 ĠLakes ) Ġand ĠUtah Ġ( home Ġof Ġthe ĠGreat ĠSalt ĠLake ). ĠOur Ġdemo Ġdescribed Ġin ĠSection ĠSEC REF 4 Ġ, Ġcan Ġalso Ġbe Ġused Ġto Ġgenerate Ġmaps Ġfor Ġfunction Ġwords , Ġwhich Ġcan Ġbe Ġvery Ġtelling Ġregarding Ġpeople 's Ġpersonality ĠB IB REF 8 Ġ. </s> Psych ol ingu istic Ġand ĠSem antic ĠMaps </s> LI WC . ĠIn Ġaddition Ġto Ġindividual Ġwords , Ġwe Ġcan Ġalso Ġcreate Ġmaps Ġfor Ġword Ġcategories Ġthat Ġreflect Ġa Ġcertain Ġpsych ol ingu istic Ġor Ġsemantic Ġproperty . ĠSeveral Ġlex ical Ġresources , Ġsuch Ġas ĠRog et Ġor ĠL ingu istic ĠInquiry Ġand ĠWord ĠCount ĠB IB REF 9 Ġ, Ġgroup Ġwords Ġinto Ġcategories . ĠExamples Ġof Ġsuch Ġcategories Ġare ĠMoney , Ġwhich Ġincludes Ġwords Ġsuch Ġas Ġrem uner ation , Ġdollar , Ġand Ġpayment ; Ġor ĠPositive Ġfeelings Ġwith Ġwords Ġsuch Ġas Ġhappy , Ġcheerful , Ġand Ġcelebration . ĠUsing Ġthe Ġdistribution Ġof Ġthe Ġindividual Ġwords Ġin Ġa Ġcategory , Ġwe Ġcan Ġcompile Ġdistributions Ġfor Ġthe Ġentire Ġcategory , Ġand Ġtherefore Ġgenerate Ġmaps Ġfor Ġthese Ġword Ġcategories . ĠFor Ġinstance , Ġfigure ĠFIG REF 8 Ġshows Ġthe Ġmaps Ġcreated Ġfor Ġtwo Ġcategories : ĠPositive ĠFeel ings Ġand ĠMoney . ĠThe Ġmaps Ġare Ġnot Ġsurprising , Ġand Ġinterestingly Ġthey Ġalso Ġreflect Ġan Ġinverse Ġcorrelation Ġbetween ĠMoney Ġand ĠPositive ĠFeel ings Ġ. </s> Values . ĠWe Ġalso Ġmeasure Ġthe Ġusage Ġof Ġwords Ġrelated Ġto Ġpeople 's Ġcore Ġvalues Ġas Ġreported Ġby ĠBoyd Ġet Ġal . Ġboy d 2015 . ĠThe Ġsets Ġof Ġwords , Ġor Ġthemes , Ġwere Ġexcav ated Ġusing Ġthe ĠMeaning ĠExt raction ĠMethod Ġ( M EM ) ĠB IB REF 10 Ġ. ĠMEM Ġis Ġa Ġtopic Ġmodeling Ġapproach Ġapplied Ġto Ġa Ġcorpus Ġof Ġtexts Ġcreated Ġby Ġhundreds Ġof Ġsurvey Ġrespondents Ġfrom Ġthe ĠU . S . Ġwho Ġwere Ġasked Ġto Ġfreely Ġwrite Ġabout Ġtheir Ġpersonal Ġvalues . ĠTo Ġillustrate , ĠFigure ĠFIG REF 9 Ġshows Ġthe Ġgeographical Ġdistributions Ġof Ġtwo Ġof Ġthese Ġvalue Ġthemes : ĠReligion Ġand ĠHard ĠWork . ĠS outheastern Ġstates Ġoften Ġconsidered Ġas Ġthe Ġnation 's ĠâĢ ľ B ible ĠBelt âĢ Ŀ ĠB IB REF 11 Ġwere Ġfound Ġto Ġhave Ġgenerally Ġhigher Ġusage Ġof ĠReligion Ġwords Ġsuch Ġas ĠGod , Ġbible , Ġand Ġchurch . ĠAnother Ġbroad Ġtrend Ġwas Ġthat Ġwestern - central Ġstates Ġ( e . g ., ĠWyoming , ĠNebraska , ĠIowa ) Ġcommonly Ġblog ged Ġabout ĠHard ĠWork , Ġusing Ġwords Ġsuch Ġas Ġhard , Ġwork , Ġand Ġjob Ġmore Ġoften Ġthan Ġbloggers Ġin Ġother Ġregions . </s> Web ĠDemon stration </s> A Ġprototype , Ġinteractive Ġchart ing Ġdemo Ġis Ġavailable Ġat Ġhttp :// lit . e ec s . um ich . edu / ~ ge oli w c /. ĠIn Ġaddition Ġto Ġdrawing Ġmaps Ġof Ġthe Ġgeographical Ġdistributions Ġon Ġthe Ġdifferent ĠLI WC Ġcategories , Ġthe Ġtool Ġcan Ġreport Ġthe Ġthree Ġmost Ġand Ġleast Ġcorrelated ĠLI WC Ġcategories Ġin Ġthe ĠU . S . Ġand Ġcompare Ġthe Ġdistributions Ġof Ġany Ġtwo Ġcategories . </s> Con clusions </s> In Ġthis Ġpaper , Ġwe Ġshowed Ġhow Ġwe Ġcan Ġeffectively Ġleverage Ġa Ġprod igious Ġblog Ġdataset . ĠNot Ġonly Ġdoes Ġthe Ġdataset Ġbring Ġout Ġthe Ġextensive Ġlinguistic Ġcontent Ġreflected Ġin Ġthe Ġblog Ġposts , Ġbut Ġalso Ġincludes Ġlocation Ġinformation Ġand Ġrich Ġmetadata . ĠThese Ġdata Ġallow Ġfor Ġthe Ġgeneration Ġof Ġmaps Ġthat Ġreflect Ġthe Ġdemographics Ġof Ġthe Ġpopulation , Ġvariations Ġin Ġlanguage Ġuse , Ġand Ġdifferences Ġin Ġpsych ol ingu istic Ġand Ġsemantic Ġcategories . ĠThese Ġm appings Ġcan Ġbe Ġvaluable Ġto Ġboth Ġpsychologists Ġand Ġlingu ists , Ġas Ġwell Ġas Ġlex ic ographers . ĠA Ġprototype Ġdemo Ġhas Ġbeen Ġmade Ġavailable Ġtogether Ġwith Ġthe Ġcode Ġused Ġto Ġcollect Ġour Ġdataset . </s> Acknowled gments </s> This Ġmaterial Ġis Ġbased Ġin Ġpart Ġupon Ġwork Ġsupported Ġby Ġthe ĠNational ĠScience ĠFoundation Ġ(# 13 44 257 ) Ġand Ġby Ġthe ĠJohn ĠTemple ton ĠFoundation Ġ(# 48 503 ). ĠAny Ġopinions , Ġfindings , Ġand Ġconclusions Ġor Ġrecommendations Ġexpressed Ġin Ġthis Ġmaterial Ġare Ġthose Ġof Ġthe Ġauthors Ġand Ġdo Ġnot Ġnecessarily Ġreflect Ġthe Ġviews Ġof Ġthe ĠNational ĠScience ĠFoundation Ġor Ġthe ĠJohn ĠTemple ton ĠFoundation . ĠWe Ġwould Ġlike Ġto Ġthank Ġour Ġcolleagues ĠH eng jing ĠWang , ĠJi ata o ĠFan , ĠX ing hai ĠZhang , Ġand ĠPo - J ung ĠHuang Ġwho Ġprovided Ġtechnical Ġhelp Ġwith Ġthe Ġimplementation Ġof Ġthe Ġdemo .
Introduction Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services BIBREF0 . For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U.S., with the purpose of creating insightful mappings of the blogging community. In particular, during May-July 2015, we gathered the profile information for all the users that have self-reported their location in the U.S., along with a number of posts for all their associated blogs. We utilize this blog collection to generate maps of the U.S. that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes. We believe that these maps can provide valuable insights and partial verification of previous claims in support of research in linguistic geography BIBREF1 , regional personality BIBREF2 , and language analysis BIBREF3 , BIBREF4 , as well as psychology and its relation to human geography BIBREF5 . Data Collection Our premise is that we can generate informative maps using geolocated information available on social media; therefore, we guide the blog collection process with the constraint that we only accept blogs that have specific location information. Moreover, we aim to find blogs belonging to writers from all 50 U.S. states, which will allow us to build U.S. maps for various dimensions of interest. We first started by collecting a set of profiles of bloggers that met our location specifications by searching individual states on the profile finder on http://www.blogger.com. Starting with this list, we can locate the profile page for a user, and subsequently extract additional information, which includes fields such as name, email, occupation, industry, and so forth. It is important to note that the profile finder only identifies users that have an exact match to the location specified in the query; we thus built and ran queries that used both state abbreviations (e.g., TX, AL), as well as the states' full names (e.g., Texas, Alabama). After completing all the processing steps, we identified 197,527 bloggers with state location information. For each of these bloggers, we found their blogs (note that a blogger can have multiple blogs), for a total of 335,698 blogs. For each of these blogs, we downloaded the 21 most recent blog postings, which were cleaned of HTML tags and tokenized, resulting in a collection of 4,600,465 blog posts. Maps from Blogs Our dataset provides mappings between location, profile information, and language use, which we can leverage to generate maps that reflect demographic, linguistic, and psycholinguistic properties of the population represented in the dataset. People Maps The first map we generate depicts the distribution of the bloggers in our dataset across the U.S. Figure FIGREF1 shows the density of users in our dataset in each of the 50 states. For instance, the densest state was found to be California with 11,701 users. The second densest is Texas, with 9,252 users, followed by New York, with 9,136. The state with the fewest bloggers is Delaware with 1,217 users. Not surprisingly, this distribution correlates well with the population of these states, with a Spearman's rank correlation INLINEFORM0 of 0.91 and a p-value INLINEFORM1 0.0001, and is very similar to the one reported in Lin and Halavais Lin04. Figure FIGREF1 also shows the cities mentioned most often in our dataset. In particular, it illustrates all 227 cities that have at least 100 bloggers. The bigger the dot on the map, the larger the number of users found in that city. The five top blogger-dense cities, in order, are: Chicago, New York, Portland, Seattle, and Atlanta. We also generate two maps that delineate the gender distribution in the dataset. Overall, the blogging world seems to be dominated by females: out of 153,209 users who self-reported their gender, only 52,725 are men and 100,484 are women. Figures FIGREF1 and FIGREF1 show the percentage of male and female bloggers in each of the 50 states. As seen in this figure, there are more than the average number of male bloggers in states such as California and New York, whereas Utah and Idaho have a higher percentage of women bloggers. Another profile element that can lead to interesting maps is the Industry field BIBREF6 . Using this field, we created different maps that plot the geographical distribution of industries across the country. As an example, Figure FIGREF2 shows the percentage of the users in each state working in the automotive and tourism industries respectively. Linguistic Maps Another use of the information found in our dataset is to build linguistic maps, which reflect the geographic lexical variation across the 50 states BIBREF7 . We generate maps that represent the relative frequency by which a word occurs in the different states. Figure FIGREF3 shows sample maps created for two different words. The figure shows the map generated for one location specific word, Maui, which unsurprisingly is found predominantly in Hawaii, and a map for a more common word, lake, which has a high occurrence rate in Minnesota (Land of 10,000 Lakes) and Utah (home of the Great Salt Lake). Our demo described in Section SECREF4 , can also be used to generate maps for function words, which can be very telling regarding people's personality BIBREF8 . Psycholinguistic and Semantic Maps LIWC. In addition to individual words, we can also create maps for word categories that reflect a certain psycholinguistic or semantic property. Several lexical resources, such as Roget or Linguistic Inquiry and Word Count BIBREF9 , group words into categories. Examples of such categories are Money, which includes words such as remuneration, dollar, and payment; or Positive feelings with words such as happy, cheerful, and celebration. Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories. For instance, figure FIGREF8 shows the maps created for two categories: Positive Feelings and Money. The maps are not surprising, and interestingly they also reflect an inverse correlation between Money and Positive Feelings . Values. We also measure the usage of words related to people's core values as reported by Boyd et al. boyd2015. The sets of words, or themes, were excavated using the Meaning Extraction Method (MEM) BIBREF10 . MEM is a topic modeling approach applied to a corpus of texts created by hundreds of survey respondents from the U.S. who were asked to freely write about their personal values. To illustrate, Figure FIGREF9 shows the geographical distributions of two of these value themes: Religion and Hard Work. Southeastern states often considered as the nation's “Bible Belt” BIBREF11 were found to have generally higher usage of Religion words such as God, bible, and church. Another broad trend was that western-central states (e.g., Wyoming, Nebraska, Iowa) commonly blogged about Hard Work, using words such as hard, work, and job more often than bloggers in other regions. Web Demonstration A prototype, interactive charting demo is available at http://lit.eecs.umich.edu/~geoliwc/. In addition to drawing maps of the geographical distributions on the different LIWC categories, the tool can report the three most and least correlated LIWC categories in the U.S. and compare the distributions of any two categories. Conclusions In this paper, we showed how we can effectively leverage a prodigious blog dataset. Not only does the dataset bring out the extensive linguistic content reflected in the blog posts, but also includes location information and rich metadata. These data allow for the generation of maps that reflect the demographics of the population, variations in language use, and differences in psycholinguistic and semantic categories. These mappings can be valuable to both psychologists and linguists, as well as lexicographers. A prototype demo has been made available together with the code used to collect our dataset. Acknowledgments This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the John Templeton Foundation. We would like to thank our colleagues Hengjing Wang, Jiatao Fan, Xinghai Zhang, and Po-Jung Huang who provided technical help with the implementation of the demo.
How do they obtain psychological dimensions of people?
07d15501a599bae7eb4a9ead63e9df3d55b3dc35
How Ġdo Ġthey Ġobtain Ġpsychological Ġdimensions Ġof Ġpeople ?
using Ġthe ĠMeaning ĠExt raction ĠMethod
<s> How Ġdo Ġthey Ġobtain Ġpsychological Ġdimensions Ġof Ġpeople ? </s> Introduction </s> Bl ogging Ġgained Ġmomentum Ġin Ġ1999 Ġand Ġbecame Ġespecially Ġpopular Ġafter Ġthe Ġlaunch Ġof Ġfreely Ġavailable , Ġhosted Ġplatforms Ġsuch Ġas Ġblogger . com Ġor Ġlive journal . com . ĠBlog ging Ġhas Ġprogressively Ġbeen Ġused Ġby Ġindividuals Ġto Ġshare Ġnews , Ġideas , Ġand Ġinformation , Ġbut Ġit Ġhas Ġalso Ġdeveloped Ġa Ġmainstream Ġrole Ġto Ġthe Ġextent Ġthat Ġit Ġis Ġbeing Ġused Ġby Ġpolitical Ġconsultants Ġand Ġnews Ġservices Ġas Ġa Ġtool Ġfor Ġoutreach Ġand Ġopinion Ġforming Ġas Ġwell Ġas Ġby Ġbusinesses Ġas Ġa Ġmarketing Ġtool Ġto Ġpromote Ġproducts Ġand Ġservices ĠB IB REF 0 Ġ. </s> For Ġthis Ġpaper , Ġwe Ġcompiled Ġa Ġvery Ġlarge Ġge ol ocated Ġcollection Ġof Ġblogs , Ġwritten Ġby Ġindividuals Ġlocated Ġin Ġthe ĠU . S ., Ġwith Ġthe Ġpurpose Ġof Ġcreating Ġinsightful Ġm appings Ġof Ġthe Ġblogging Ġcommunity . ĠIn Ġparticular , Ġduring ĠMay - July Ġ2015 , Ġwe Ġgathered Ġthe Ġprofile Ġinformation Ġfor Ġall Ġthe Ġusers Ġthat Ġhave Ġself - reported Ġtheir Ġlocation Ġin Ġthe ĠU . S ., Ġalong Ġwith Ġa Ġnumber Ġof Ġposts Ġfor Ġall Ġtheir Ġassociated Ġblogs . ĠWe Ġutilize Ġthis Ġblog Ġcollection Ġto Ġgenerate Ġmaps Ġof Ġthe ĠU . S . Ġthat Ġreflect Ġuser Ġdemographics , Ġlanguage Ġuse , Ġand Ġdistributions Ġof Ġpsych ol ingu istic Ġand Ġsemantic Ġword Ġclasses . ĠWe Ġbelieve Ġthat Ġthese Ġmaps Ġcan Ġprovide Ġvaluable Ġinsights Ġand Ġpartial Ġverification Ġof Ġprevious Ġclaims Ġin Ġsupport Ġof Ġresearch Ġin Ġlinguistic Ġgeography ĠB IB REF 1 Ġ, Ġregional Ġpersonality ĠB IB REF 2 Ġ, Ġand Ġlanguage Ġanalysis ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, Ġas Ġwell Ġas Ġpsychology Ġand Ġits Ġrelation Ġto Ġhuman Ġgeography ĠB IB REF 5 Ġ. </s> Data ĠCollection </s> Our Ġpremise Ġis Ġthat Ġwe Ġcan Ġgenerate Ġinformative Ġmaps Ġusing Ġge ol ocated Ġinformation Ġavailable Ġon Ġsocial Ġmedia ; Ġtherefore , Ġwe Ġguide Ġthe Ġblog Ġcollection Ġprocess Ġwith Ġthe Ġconstraint Ġthat Ġwe Ġonly Ġaccept Ġblogs Ġthat Ġhave Ġspecific Ġlocation Ġinformation . ĠMoreover , Ġwe Ġaim Ġto Ġfind Ġblogs Ġbelonging Ġto Ġwriters Ġfrom Ġall Ġ50 ĠU . S . Ġstates , Ġwhich Ġwill Ġallow Ġus Ġto Ġbuild ĠU . S . Ġmaps Ġfor Ġvarious Ġdimensions Ġof Ġinterest . </s> We Ġfirst Ġstarted Ġby Ġcollecting Ġa Ġset Ġof Ġprofiles Ġof Ġbloggers Ġthat Ġmet Ġour Ġlocation Ġspecifications Ġby Ġsearching Ġindividual Ġstates Ġon Ġthe Ġprofile Ġfind er Ġon Ġhttp :// www . blog ger . com . ĠStarting Ġwith Ġthis Ġlist , Ġwe Ġcan Ġlocate Ġthe Ġprofile Ġpage Ġfor Ġa Ġuser , Ġand Ġsubsequently Ġextract Ġadditional Ġinformation , Ġwhich Ġincludes Ġfields Ġsuch Ġas Ġname , Ġemail , Ġoccupation , Ġindustry , Ġand Ġso Ġforth . ĠIt Ġis Ġimportant Ġto Ġnote Ġthat Ġthe Ġprofile Ġfind er Ġonly Ġidentifies Ġusers Ġthat Ġhave Ġan Ġexact Ġmatch Ġto Ġthe Ġlocation Ġspecified Ġin Ġthe Ġquery ; Ġwe Ġthus Ġbuilt Ġand Ġran Ġqueries Ġthat Ġused Ġboth Ġstate Ġabbrevi ations Ġ( e . g ., ĠTX , ĠAL ), Ġas Ġwell Ġas Ġthe Ġstates ' Ġfull Ġnames Ġ( e . g ., ĠTexas , ĠAlabama ). </s> After Ġcompleting Ġall Ġthe Ġprocessing Ġsteps , Ġwe Ġidentified Ġ197 , 5 27 Ġbloggers Ġwith Ġstate Ġlocation Ġinformation . ĠFor Ġeach Ġof Ġthese Ġbloggers , Ġwe Ġfound Ġtheir Ġblogs Ġ( note Ġthat Ġa Ġblogger Ġcan Ġhave Ġmultiple Ġblogs ), Ġfor Ġa Ġtotal Ġof Ġ335 , 698 Ġblogs . ĠFor Ġeach Ġof Ġthese Ġblogs , Ġwe Ġdownloaded Ġthe Ġ21 Ġmost Ġrecent Ġblog Ġpostings , Ġwhich Ġwere Ġcleaned Ġof ĠHTML Ġtags Ġand Ġtoken ized , Ġresulting Ġin Ġa Ġcollection Ġof Ġ4 , 600 , 465 Ġblog Ġposts . </s> Maps Ġfrom ĠBlog s </s> Our Ġdataset Ġprovides Ġm appings Ġbetween Ġlocation , Ġprofile Ġinformation , Ġand Ġlanguage Ġuse , Ġwhich Ġwe Ġcan Ġleverage Ġto Ġgenerate Ġmaps Ġthat Ġreflect Ġdemographic , Ġlinguistic , Ġand Ġpsych ol ingu istic Ġproperties Ġof Ġthe Ġpopulation Ġrepresented Ġin Ġthe Ġdataset . </s> People ĠMaps </s> The Ġfirst Ġmap Ġwe Ġgenerate Ġdepicts Ġthe Ġdistribution Ġof Ġthe Ġbloggers Ġin Ġour Ġdataset Ġacross Ġthe ĠU . S . ĠFigure ĠFIG REF 1 Ġshows Ġthe Ġdensity Ġof Ġusers Ġin Ġour Ġdataset Ġin Ġeach Ġof Ġthe Ġ50 Ġstates . ĠFor Ġinstance , Ġthe Ġdens est Ġstate Ġwas Ġfound Ġto Ġbe ĠCalifornia Ġwith Ġ11 , 701 Ġusers . ĠThe Ġsecond Ġdens est Ġis ĠTexas , Ġwith Ġ9 , 252 Ġusers , Ġfollowed Ġby ĠNew ĠYork , Ġwith Ġ9 , 136 . ĠThe Ġstate Ġwith Ġthe Ġfew est Ġbloggers Ġis ĠDelaware Ġwith Ġ1 , 217 Ġusers . ĠNot Ġsurprisingly , Ġthis Ġdistribution Ġcorrelates Ġwell Ġwith Ġthe Ġpopulation Ġof Ġthese Ġstates , Ġwith Ġa ĠSpear man 's Ġrank Ġcorrelation ĠIN LINE FORM 0 Ġof Ġ0 . 91 Ġand Ġa Ġp - value ĠIN LINE FORM 1 Ġ0 . 0001 , Ġand Ġis Ġvery Ġsimilar Ġto Ġthe Ġone Ġreported Ġin ĠLin Ġand ĠHal ava is ĠLin 04 . </s> Figure ĠFIG REF 1 Ġalso Ġshows Ġthe Ġcities Ġmentioned Ġmost Ġoften Ġin Ġour Ġdataset . ĠIn Ġparticular , Ġit Ġillustrates Ġall Ġ227 Ġcities Ġthat Ġhave Ġat Ġleast Ġ100 Ġbloggers . ĠThe Ġbigger Ġthe Ġdot Ġon Ġthe Ġmap , Ġthe Ġlarger Ġthe Ġnumber Ġof Ġusers Ġfound Ġin Ġthat Ġcity . ĠThe Ġfive Ġtop Ġblogger - d ense Ġcities , Ġin Ġorder , Ġare : ĠChicago , ĠNew ĠYork , ĠPortland , ĠSeattle , Ġand ĠAtlanta . </s> We Ġalso Ġgenerate Ġtwo Ġmaps Ġthat Ġdeline ate Ġthe Ġgender Ġdistribution Ġin Ġthe Ġdataset . ĠOverall , Ġthe Ġblogging Ġworld Ġseems Ġto Ġbe Ġdominated Ġby Ġfemales : Ġout Ġof Ġ153 , 209 Ġusers Ġwho Ġself - reported Ġtheir Ġgender , Ġonly Ġ52 , 725 Ġare Ġmen Ġand Ġ100 , 484 Ġare Ġwomen . ĠFigures ĠFIG REF 1 Ġand ĠFIG REF 1 Ġshow Ġthe Ġpercentage Ġof Ġmale Ġand Ġfemale Ġbloggers Ġin Ġeach Ġof Ġthe Ġ50 Ġstates . ĠAs Ġseen Ġin Ġthis Ġfigure , Ġthere Ġare Ġmore Ġthan Ġthe Ġaverage Ġnumber Ġof Ġmale Ġbloggers Ġin Ġstates Ġsuch Ġas ĠCalifornia Ġand ĠNew ĠYork , Ġwhereas ĠUtah Ġand ĠIdaho Ġhave Ġa Ġhigher Ġpercentage Ġof Ġwomen Ġbloggers . </s> Another Ġprofile Ġelement Ġthat Ġcan Ġlead Ġto Ġinteresting Ġmaps Ġis Ġthe ĠIndustry Ġfield ĠB IB REF 6 Ġ. ĠUsing Ġthis Ġfield , Ġwe Ġcreated Ġdifferent Ġmaps Ġthat Ġplot Ġthe Ġgeographical Ġdistribution Ġof Ġindustries Ġacross Ġthe Ġcountry . ĠAs Ġan Ġexample , ĠFigure ĠFIG REF 2 Ġshows Ġthe Ġpercentage Ġof Ġthe Ġusers Ġin Ġeach Ġstate Ġworking Ġin Ġthe Ġautomotive Ġand Ġtourism Ġindustries Ġrespectively . </s> L ingu istic ĠMaps </s> Another Ġuse Ġof Ġthe Ġinformation Ġfound Ġin Ġour Ġdataset Ġis Ġto Ġbuild Ġlinguistic Ġmaps , Ġwhich Ġreflect Ġthe Ġgeographic Ġlex ical Ġvariation Ġacross Ġthe Ġ50 Ġstates ĠB IB REF 7 Ġ. ĠWe Ġgenerate Ġmaps Ġthat Ġrepresent Ġthe Ġrelative Ġfrequency Ġby Ġwhich Ġa Ġword Ġoccurs Ġin Ġthe Ġdifferent Ġstates . ĠFigure ĠFIG REF 3 Ġshows Ġsample Ġmaps Ġcreated Ġfor Ġtwo Ġdifferent Ġwords . ĠThe Ġfigure Ġshows Ġthe Ġmap Ġgenerated Ġfor Ġone Ġlocation Ġspecific Ġword , ĠMau i , Ġwhich Ġunsurprisingly Ġis Ġfound Ġpredominantly Ġin ĠHawaii , Ġand Ġa Ġmap Ġfor Ġa Ġmore Ġcommon Ġword , Ġlake , Ġwhich Ġhas Ġa Ġhigh Ġoccurrence Ġrate Ġin ĠMinnesota Ġ( Land Ġof Ġ10 , 000 ĠLakes ) Ġand ĠUtah Ġ( home Ġof Ġthe ĠGreat ĠSalt ĠLake ). ĠOur Ġdemo Ġdescribed Ġin ĠSection ĠSEC REF 4 Ġ, Ġcan Ġalso Ġbe Ġused Ġto Ġgenerate Ġmaps Ġfor Ġfunction Ġwords , Ġwhich Ġcan Ġbe Ġvery Ġtelling Ġregarding Ġpeople 's Ġpersonality ĠB IB REF 8 Ġ. </s> Psych ol ingu istic Ġand ĠSem antic ĠMaps </s> LI WC . ĠIn Ġaddition Ġto Ġindividual Ġwords , Ġwe Ġcan Ġalso Ġcreate Ġmaps Ġfor Ġword Ġcategories Ġthat Ġreflect Ġa Ġcertain Ġpsych ol ingu istic Ġor Ġsemantic Ġproperty . ĠSeveral Ġlex ical Ġresources , Ġsuch Ġas ĠRog et Ġor ĠL ingu istic ĠInquiry Ġand ĠWord ĠCount ĠB IB REF 9 Ġ, Ġgroup Ġwords Ġinto Ġcategories . ĠExamples Ġof Ġsuch Ġcategories Ġare ĠMoney , Ġwhich Ġincludes Ġwords Ġsuch Ġas Ġrem uner ation , Ġdollar , Ġand Ġpayment ; Ġor ĠPositive Ġfeelings Ġwith Ġwords Ġsuch Ġas Ġhappy , Ġcheerful , Ġand Ġcelebration . ĠUsing Ġthe Ġdistribution Ġof Ġthe Ġindividual Ġwords Ġin Ġa Ġcategory , Ġwe Ġcan Ġcompile Ġdistributions Ġfor Ġthe Ġentire Ġcategory , Ġand Ġtherefore Ġgenerate Ġmaps Ġfor Ġthese Ġword Ġcategories . ĠFor Ġinstance , Ġfigure ĠFIG REF 8 Ġshows Ġthe Ġmaps Ġcreated Ġfor Ġtwo Ġcategories : ĠPositive ĠFeel ings Ġand ĠMoney . ĠThe Ġmaps Ġare Ġnot Ġsurprising , Ġand Ġinterestingly Ġthey Ġalso Ġreflect Ġan Ġinverse Ġcorrelation Ġbetween ĠMoney Ġand ĠPositive ĠFeel ings Ġ. </s> Values . ĠWe Ġalso Ġmeasure Ġthe Ġusage Ġof Ġwords Ġrelated Ġto Ġpeople 's Ġcore Ġvalues Ġas Ġreported Ġby ĠBoyd Ġet Ġal . Ġboy d 2015 . ĠThe Ġsets Ġof Ġwords , Ġor Ġthemes , Ġwere Ġexcav ated Ġusing Ġthe ĠMeaning ĠExt raction ĠMethod Ġ( M EM ) ĠB IB REF 10 Ġ. ĠMEM Ġis Ġa Ġtopic Ġmodeling Ġapproach Ġapplied Ġto Ġa Ġcorpus Ġof Ġtexts Ġcreated Ġby Ġhundreds Ġof Ġsurvey Ġrespondents Ġfrom Ġthe ĠU . S . Ġwho Ġwere Ġasked Ġto Ġfreely Ġwrite Ġabout Ġtheir Ġpersonal Ġvalues . ĠTo Ġillustrate , ĠFigure ĠFIG REF 9 Ġshows Ġthe Ġgeographical Ġdistributions Ġof Ġtwo Ġof Ġthese Ġvalue Ġthemes : ĠReligion Ġand ĠHard ĠWork . ĠS outheastern Ġstates Ġoften Ġconsidered Ġas Ġthe Ġnation 's ĠâĢ ľ B ible ĠBelt âĢ Ŀ ĠB IB REF 11 Ġwere Ġfound Ġto Ġhave Ġgenerally Ġhigher Ġusage Ġof ĠReligion Ġwords Ġsuch Ġas ĠGod , Ġbible , Ġand Ġchurch . ĠAnother Ġbroad Ġtrend Ġwas Ġthat Ġwestern - central Ġstates Ġ( e . g ., ĠWyoming , ĠNebraska , ĠIowa ) Ġcommonly Ġblog ged Ġabout ĠHard ĠWork , Ġusing Ġwords Ġsuch Ġas Ġhard , Ġwork , Ġand Ġjob Ġmore Ġoften Ġthan Ġbloggers Ġin Ġother Ġregions . </s> Web ĠDemon stration </s> A Ġprototype , Ġinteractive Ġchart ing Ġdemo Ġis Ġavailable Ġat Ġhttp :// lit . e ec s . um ich . edu / ~ ge oli w c /. ĠIn Ġaddition Ġto Ġdrawing Ġmaps Ġof Ġthe Ġgeographical Ġdistributions Ġon Ġthe Ġdifferent ĠLI WC Ġcategories , Ġthe Ġtool Ġcan Ġreport Ġthe Ġthree Ġmost Ġand Ġleast Ġcorrelated ĠLI WC Ġcategories Ġin Ġthe ĠU . S . Ġand Ġcompare Ġthe Ġdistributions Ġof Ġany Ġtwo Ġcategories . </s> Con clusions </s> In Ġthis Ġpaper , Ġwe Ġshowed Ġhow Ġwe Ġcan Ġeffectively Ġleverage Ġa Ġprod igious Ġblog Ġdataset . ĠNot Ġonly Ġdoes Ġthe Ġdataset Ġbring Ġout Ġthe Ġextensive Ġlinguistic Ġcontent Ġreflected Ġin Ġthe Ġblog Ġposts , Ġbut Ġalso Ġincludes Ġlocation Ġinformation Ġand Ġrich Ġmetadata . ĠThese Ġdata Ġallow Ġfor Ġthe Ġgeneration Ġof Ġmaps Ġthat Ġreflect Ġthe Ġdemographics Ġof Ġthe Ġpopulation , Ġvariations Ġin Ġlanguage Ġuse , Ġand Ġdifferences Ġin Ġpsych ol ingu istic Ġand Ġsemantic Ġcategories . ĠThese Ġm appings Ġcan Ġbe Ġvaluable Ġto Ġboth Ġpsychologists Ġand Ġlingu ists , Ġas Ġwell Ġas Ġlex ic ographers . ĠA Ġprototype Ġdemo Ġhas Ġbeen Ġmade Ġavailable Ġtogether Ġwith Ġthe Ġcode Ġused Ġto Ġcollect Ġour Ġdataset . </s> Acknowled gments </s> This Ġmaterial Ġis Ġbased Ġin Ġpart Ġupon Ġwork Ġsupported Ġby Ġthe ĠNational ĠScience ĠFoundation Ġ(# 13 44 257 ) Ġand Ġby Ġthe ĠJohn ĠTemple ton ĠFoundation Ġ(# 48 503 ). ĠAny Ġopinions , Ġfindings , Ġand Ġconclusions Ġor Ġrecommendations Ġexpressed Ġin Ġthis Ġmaterial Ġare Ġthose Ġof Ġthe Ġauthors Ġand Ġdo Ġnot Ġnecessarily Ġreflect Ġthe Ġviews Ġof Ġthe ĠNational ĠScience ĠFoundation Ġor Ġthe ĠJohn ĠTemple ton ĠFoundation . ĠWe Ġwould Ġlike Ġto Ġthank Ġour Ġcolleagues ĠH eng jing ĠWang , ĠJi ata o ĠFan , ĠX ing hai ĠZhang , Ġand ĠPo - J ung ĠHuang Ġwho Ġprovided Ġtechnical Ġhelp Ġwith Ġthe Ġimplementation Ġof Ġthe Ġdemo .