1,873 Twitter conversation threads, roughly 14k tweets
EXTRACTIVE
1,873 Twitter conversation threads, roughly 14k tweets
EXTRACTIVE
We have downloaded 1,873 Twitter conversation threads, roughly 14k tweets, from a publicly available resource that were previously pre-processed and have conversation threads extracted. A conversation in the dataset consists of at least 4 tweets. Even though, according to BIBREF23, Twitter is broadly applicable to public health research, our expectation is that it contains less therapeutic conversations in comparison to specialized on-line support forums.
We have downloaded 1,873 Twitter conversation threads, roughly 14k tweets, from a publicly available resource that were previously pre-processed and have conversation threads extracted. A conversation in the dataset consists of at least 4 tweets. Even though, according to BIBREF23, Twitter is broadly applicable to public health research, our expectation is that it contains less therapeutic conversations in comparison to specialized on-line support forums.
1911.01371
Introduction </s> Recently , Ġpeople Ġhave Ġstarted Ġlooking Ġat Ġonline Ġforums Ġeither Ġas Ġa Ġprimary Ġor Ġsecondary Ġsource Ġof Ġcounseling Ġservices ĠB IB REF 0 . ĠB IB REF 1 Ġreported Ġthat Ġover Ġthe Ġfirst Ġfive Ġyears Ġof Ġoperation Ġ( 2011 - 2016 ), ĠReach Out . com ĠâĢĵ ĠIreland 's Ġonline Ġyouth Ġmental Ġhealth Ġservice ĠâĢĵ Ġ62 % Ġof Ġyoung Ġpeople Ġwould Ġvisit Ġa Ġwebsite Ġfor Ġsupport Ġwhen Ġgoing Ġthrough Ġa Ġtough Ġtime . ĠWith Ġthe Ġexpansion Ġof Ġthe ĠInternet , Ġthere Ġhas Ġbeen Ġa Ġsubstantial Ġgrowth Ġin Ġthe Ġnumber Ġof Ġusers Ġlooking Ġfor Ġpsychological Ġsupport Ġonline . </s> The Ġimportance Ġof Ġthe Ġon - line Ġlife Ġof Ġpatients Ġhas Ġbeen Ġrecognized Ġin Ġresearch Ġas Ġwell . ĠB IB REF 2 Ġstated Ġthat Ġthe Ġonline Ġlife Ġof Ġpatients Ġconstitutes Ġa Ġmajor Ġinfluence Ġon Ġtheir Ġself - definition . ĠFurthermore , Ġaccording Ġto ĠB IB REF 3 , Ġthe Ġsocial Ġnetworking Ġactivities Ġof Ġan Ġindividual , Ġoffer Ġan Ġimportant Ġreflection Ġof Ġtheir Ġpersonality . ĠWhile Ġdealing Ġwith Ġpatients Ġsuffering Ġfrom Ġpsychological Ġproblems , Ġit Ġis Ġimportant Ġthat Ġtherapists Ġdo Ġnot Ġignore Ġthis Ġpivotal Ġsource Ġof Ġinformation Ġwhich Ġcan Ġprovide Ġdeep Ġinsights Ġinto Ġtheir Ġpatients ' Ġmental Ġconditions . </s> Accept ance Ġof Ġon - line Ġsupport Ġgroups Ġ( OS G ) Ġby ĠMental ĠHealth ĠProfession als Ġis Ġstill Ġnot Ġestablished ĠB IB REF 4 . ĠSince ĠOS G Ġcan Ġhave Ġdouble - edged Ġeffects Ġon Ġpatients Ġand Ġthe Ġpresence Ġof Ġprofessionals Ġis Ġoften Ġlimited , Ġwe Ġargue Ġthat Ġtheir Ġproperties Ġshould Ġbe Ġfurther Ġstudied . ĠAccording Ġto ĠB IB REF 5 ĠOS G Ġeffectiveness Ġis Ġhard Ġto Ġassess , Ġwhile Ġsome Ġstudies Ġshowed ĠOS G 's Ġpotential Ġto Ġchange Ġparticipants ' Ġattitudes , Ġno Ġsuch Ġeffect Ġwas Ġobserved Ġin Ġother Ġstudies Ġ( see ĠRelated ĠWork ĠSection Ġfor Ġmore Ġdetails ). ĠFurthermore Ġthe Ġscope Ġof Ġprevious Ġwork Ġon Ġanalysis Ġof Ġusers ' Ġbehaviour Ġin ĠOS G Ġhas Ġbeen Ġlimited Ġby Ġthe Ġfact Ġthat Ġthey Ġrelied Ġon Ġexpert Ġannotation Ġof Ġposts Ġand Ġcomments ĠB IB REF 6 . </s> We Ġpresent Ġa Ġnovel Ġapproach Ġfor Ġautomatically Ġanalys ing Ġonline Ġconversations Ġfor Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors Ġof Ġgroup Ġtherapy Ġdefined Ġby ĠB IB REF 7 Ġas ĠâĢ ľ the Ġactual Ġmechanisms Ġof Ġeffect ing Ġchange Ġin Ġthe Ġpatient âĢ Ŀ . ĠThe Ġauthors Ġhave Ġidentified Ġ11 Ġtherapeutic Ġfactors Ġin Ġgroup Ġtherapy : ĠUnivers ality , ĠAlt ru ism , ĠInst illation Ġof ĠHope , ĠGuid ance , ĠIm part ing Ġinformation , ĠDevelop ing Ġsocial Ġskills , ĠInter personal Ġlearning , ĠCo hesion , ĠCath ars is , ĠEx ist ential Ġfactors , ĠIm itative Ġbehavior Ġand ĠCorrect ive Ġrecap it ulation Ġof Ġfamily Ġof Ġorigin Ġissues . ĠIn Ġthis Ġpaper , Ġwe Ġfocus Ġon Ġ3 Ġtherapeutic Ġfactors : ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope Ġ( listed Ġbelow ), Ġas Ġwe Ġbelieve Ġthat Ġthese Ġcan Ġbe Ġapprox imated Ġby Ġusing Ġestablished ĠN LP Ġtechniques Ġ( e . g . ĠSent iment ĠAnalysis , ĠDialogue ĠAct Ġtagging Ġetc .). </s> Un ivers ality : Ġthe Ġdis conf irmation Ġof Ġa Ġuser 's Ġfeelings Ġof Ġuniqueness Ġof Ġtheir Ġmental Ġhealth Ġcondition . </s> Alt ru ism : Ġothers Ġoffer Ġsupport , Ġreass urance , Ġsuggestions Ġand Ġinsight . </s> Inst illation Ġof ĠHope : Ġinspiration Ġprovided Ġto Ġparticipants Ġby Ġtheir Ġpeers . </s> The Ġselected Ġtherapeutic Ġfactors Ġare Ġanalysed Ġin Ġterms Ġof Ġill oc ution ary Ġforce Ġand Ġattitude . ĠDue Ġto Ġthe Ġmulti - party Ġand Ġasynchronous Ġnature Ġof Ġon - line Ġsocial Ġmedia Ġconversations , Ġprior Ġto Ġthe Ġanalysis , Ġwe Ġextract Ġconversation Ġthreads Ġamong Ġusers ĠâĢĵ Ġan Ġessential Ġprerequisite Ġfor Ġany Ġkind Ġof Ġhigher - level Ġdialogue Ġanalysis ĠB IB REF 10 . ĠAfterwards , Ġthe Ġill oc ution ary Ġforce Ġis Ġidentified Ġusing ĠDialogue ĠAct Ġtagging , Ġwhereas Ġthe Ġattitude Ġby Ġusing ĠSent iment ĠAnalysis . ĠThe Ġquantitative Ġanalysis Ġis Ġthen Ġperformed Ġon Ġthese Ġprocessed Ġconversations . </s> Ide ally , Ġthe Ġanalysis Ġwould Ġrequire Ġexperts Ġto Ġannot ate Ġeach Ġpost Ġand Ġcomment Ġon Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors . ĠHowever , Ġdue Ġto Ġtime Ġand Ġcost Ġdemands Ġof Ġthis Ġtask , Ġit Ġis Ġfeasible Ġto Ġanalyse Ġonly Ġa Ġsmall Ġfraction Ġof Ġthe Ġavailable Ġdata . ĠCompared Ġto Ġprevious Ġstudies Ġ( e . g . ĠB IB REF 6 ) Ġthat Ġanalysed Ġfew Ġtens Ġof Ġconversations Ġand Ġseveral Ġthousand Ġlines Ġof Ġchat ; Ġusing Ġthe Ġproposed Ġapproach ĠâĢĵ Ġapplication Ġof ĠDialogue ĠActs Ġand ĠSent iment ĠAnalysis ĠâĢĵ Ġwe Ġwere Ġable Ġto Ġautomatically Ġanalyse Ġapproximately Ġ300 Ġthousands Ġconversations Ġ( rough ly Ġ1 . 5 Ġmillion Ġcomments ). ĠThe Ġrest Ġof Ġthe Ġpaper Ġis Ġstructured Ġas Ġfollows . ĠIn ĠSection Ġ2 Ġwe Ġintroduce Ġrelated Ġwork . ĠNext , Ġin ĠSection Ġ3 Ġwe Ġdescribe Ġthe Ġpre - processing Ġpipeline Ġand Ġthe Ġmethodology Ġto Ġperform Ġthread Ġextraction Ġon Ġasynchronous Ġmulti - party Ġconversations . ĠIn ĠSection Ġ4 Ġwe Ġprovide Ġthe Ġdescribe Ġthe Ġfinal Ġdataset Ġused Ġfor Ġthe Ġanalysis , Ġand Ġin ĠSection Ġ5 Ġwe Ġpresent Ġthe Ġresults Ġof Ġour Ġanalysis . ĠFinally , Ġin ĠSection Ġ6 Ġwe Ġprovide Ġconcluding Ġremarks Ġand Ġfuture Ġresearch Ġdirections . </s> Related ĠWork </s> On - line Ġsupport Ġgroups Ġhave Ġbeen Ġanalyzed Ġfor Ġvarious Ġfactors Ġbefore . ĠFor Ġinstance , ĠB IB REF 11 Ġanalysed Ġstress Ġreduction Ġin Ġon - line Ġsupport Ġgroup Ġchat - rooms , Ġand Ġthe Ġeffects Ġof Ġon - line Ġsocial Ġinteractions . ĠSuch Ġstudies Ġmostly Ġrelied Ġon Ġquestion naires Ġand Ġwere Ġbased Ġon Ġa Ġsmall Ġnumber Ġof Ġusers . ĠNevertheless , Ġin ĠB IB REF 11 , Ġthe Ġauthor Ġshowed Ġthat Ġsocial Ġsupport Ġfacilitates Ġcoping Ġwith Ġdistress , Ġimproves Ġmood Ġand Ġexped ites Ġrecovery Ġfrom Ġit . ĠThese Ġfindings Ġhighlight Ġthat , Ġoverall , Ġon - line Ġdiscussion Ġboards Ġappear Ġto Ġbe Ġtherapeutic Ġand Ġconstructive Ġfor Ġindividuals Ġsuffering Ġalcohol - abuse . </s> Application Ġof ĠN LP Ġto Ġthe Ġanalysis Ġof Ġmental Ġhealth - related Ġconversation Ġhas Ġbeen Ġstudied Ġas Ġwell Ġ( e . g . ĠB IB REF 12 , ĠB IB REF 13 ). ĠB IB REF 6 Ġapplied Ġsentiment - analysis Ġcombined Ġwith Ġextensive Ġturn - level Ġannotation Ġto Ġinvestigate Ġstress Ġreduction Ġin Ġon - line Ġsupport Ġgroup Ġchat - rooms , Ġshowing Ġthat Ġsentiment - analysis Ġis Ġa Ġgood Ġpredictor Ġof Ġentrance Ġstress Ġlevel . ĠFurthermore , Ġsimilar Ġto Ġour Ġsetting , Ġthey Ġapplied Ġautomatic Ġthread - ext raction Ġto Ġdetermine Ġconversation Ġthreads . </s> BI B REF 14 Ġhave Ġshown Ġthat Ġon - line Ġsupport Ġgroup Ġtherapy Ġincreased Ġthe Ġquality Ġof Ġlife Ġof Ġpatients Ġwith Ġmetast atic Ġbreast Ġcancer . ĠSince Ġmany Ġoriginal Ġposters Ġreported Ġthe Ġbenefits Ġof Ġgroup Ġtherapy Ġon Ġpatients ĠB IB REF 15 , ĠB IB REF 2 , ĠB IB REF 16 , ĠB IB REF 17 , ĠB IB REF 18 , ĠB IB REF 7 , Ġwe Ġevaluate Ġthe Ġeffect Ġof Ġthe Ġuser Ġinteraction Ġusing Ġsentiment Ġscores Ġof Ġcomments Ġin Ġon - line Ġsupport Ġgroups . </s> According Ġto ĠB IB REF 6 , Ġusers Ġwith Ġhigh Ġincoming Ġstress Ġtend Ġto Ġrequest Ġless Ġinformation Ġfrom Ġothers , Ġas Ġa Ġpercentage Ġof Ġtheir Ġtime , Ġand Ġshare Ġmuch Ġmore Ġinformation , Ġin Ġabsolute Ġterms . ĠIn Ġaddition , Ġhigh Ġinformation Ġsharing Ġhas Ġbeen Ġshown Ġto Ġbe Ġa Ġgood Ġpredictor Ġof Ġstress Ġreduction Ġat Ġthe Ġend Ġof Ġthe Ġchat ĠB IB REF 6 . ĠRegarding Ġinformation Ġsharing , Ġwe Ġrely Ġon ĠDialogue ĠActs ĠB IB REF 19 Ġto Ġmodel Ġthe Ġspeaker 's Ġintention Ġin Ġproducing Ġan Ġutter ance . ĠIn Ġparticular , Ġwe Ġare Ġinterested Ġin ĠDialogue ĠAct Ġlabel Ġthat Ġis Ġdefined Ġto Ġrepresent Ġdescriptive , Ġnarrative , Ġor Ġpersonal Ġinformation ĠâĢĵ Ġthe Ġstatement . </s> Dialogue ĠActs Ġhave Ġbeen Ġapplied Ġto Ġthe Ġanalysis Ġof Ġspoken ĠB IB REF 20 , ĠB IB REF 21 Ġas Ġwell Ġas Ġon - line Ġwritten Ġsynchron ous Ġconversations ĠB IB REF 22 . ĠWe Ġapply ĠDialogue ĠAct Ġtag Ġset Ġdefined Ġin ĠB IB REF 22 Ġto Ġthe Ġanalysis Ġof Ġour Ġon - line Ġasynchronous Ġconversations . ĠWe Ġargue Ġthat ĠDialogue ĠActs Ġcan Ġbe Ġused Ġto Ġanalyse Ġuser Ġbehaviour Ġin Ġsocial Ġmedia Ġand Ġverify Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors . </s> Method ology </s> We Ġselect Ġthe Ġthree Ġtherapeutic Ġfactors ĠâĢĵ ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope ĠâĢĵ Ġthat Ġcan Ġbe Ġbest Ġapprox imated Ġusing ĠN LP Ġtechniques : ĠSent iment ĠAnalysis Ġand ĠDialogue ĠAct Ġtagging . ĠWe Ġdiscuss Ġeach Ġone Ġof Ġthe Ġselected Ġtherapeutic Ġfactors Ġand Ġthe Ġidentified Ġnecessary Ġconditions . ĠThe Ġlisted Ġconditions , Ġhowever , Ġare Ġnot Ġsufficient Ġto Ġattribute Ġthe Ġpresence Ġof Ġa Ġtherapeutic Ġfactor Ġwith Ġhigh Ġconfidence , Ġwhich Ġonly Ġcan Ġbe Ġobtained Ġusing Ġexpert Ġannotation . ĠOur Ġanalysis Ġfocuses Ġon Ġthe Ġstructure Ġof Ġconversations ; Ġthough Ġcontent Ġplays Ġan Ġimportant Ġrole Ġas Ġwell . </s> Un ivers ality Ġconsists Ġin Ġthe Ġdis conf irmation Ġof Ġpatients ' Ġbelief Ġof Ġuniqueness Ġof Ġtheir Ġdisease . ĠThis Ġtherapeutic Ġfactor Ġis Ġshown Ġto Ġbe Ġa Ġpowerful Ġsource Ġof Ġrelief Ġfor Ġthe Ġpatient , Ġaccording Ġto ĠB IB REF 7 . ĠFrom Ġthis Ġdefinition , Ġwe Ġcan Ġdraw Ġthe Ġfollowing Ġconditions Ġthat Ġare Ġapplicable Ġto Ġour Ġenvironment : </s> improve ment Ġof Ġoriginal Ġposter 's Ġsentiment : Ġwe Ġhypothes ize Ġthat Ġthe Ġdiscovery Ġthat Ġother Ġpeople Ġpassed Ġthrough Ġsimilar Ġissues Ġleads Ġto Ġa Ġhigher Ġsentiment Ġscore ; </s> posts Ġcontaining Ġnegative Ġpersonal Ġexperiences : Ġto Ġdis conf irm Ġthe Ġbelief Ġof Ġuniqueness Ġusers Ġhave Ġto Ġshare Ġtheir Ġstory ; </s> comments Ġcontaining Ġnegative Ġstatements : Ġto Ġdis conf irm Ġthe Ġpatient 's Ġfeelings Ġof Ġuniqueness , Ġthe Ġcommenting Ġuser Ġmust Ġtell Ġa Ġsimilar Ġnegative Ġpersonal Ġexperience . ĠThis Ġcondition Ġrequires Ġtwo Ġsub - cond itions : Ġhigh Ġpresence Ġof Ġstatements Ġin Ġcomments Ġand Ġthe Ġpresence Ġof Ġnegative Ġcomments Ġrep lying Ġto Ġnegative Ġposts . </s> Inst illation Ġof ĠHope Ġis Ġbased Ġon Ġinspiration Ġprovided Ġto Ġparticipants Ġby Ġtheir Ġpeers . ĠThrough Ġthe Ġinspiration Ġprovided Ġby Ġtheir Ġpeers , Ġpatients Ġcan Ġincrease Ġtheir Ġexpectation Ġon Ġthe Ġtherapy Ġoutcome . ĠB IB REF 7 Ġin Ġseveral Ġstudies Ġhave Ġdemonstrated Ġthat Ġa Ġhigh Ġexpectation Ġof Ġhelp Ġbefore Ġthe Ġstart Ġof Ġa Ġtherapy Ġis Ġsignificantly Ġcorrelated Ġwith Ġa Ġpositive Ġtherapy Ġoutcome . ĠThe Ġauthor Ġstates Ġthat Ġmany Ġpatients Ġpointed Ġout Ġthe Ġimportance Ġof Ġhaving Ġobserved Ġthe Ġimprovement Ġof Ġothers . ĠTherefore , Ġthe Ġthree Ġmain Ġconditions Ġare Ġthe Ġfollowing : </s> improve ment Ġof Ġoriginal Ġposter 's Ġsentiment : Ġwe Ġhypothes ize Ġthat Ġinst illation Ġof Ġhope Ġleads Ġto Ġa Ġhigher Ġsentiment Ġscore ; </s> posts Ġcontaining Ġnegative Ġpersonal Ġexperiences : Ġhope Ġcan Ġbe Ġinst illed Ġin Ġsomeone Ġwho Ġshares Ġa Ġnegative Ġpersonal Ġexperience ; </s> comments Ġcontaining Ġpositive Ġpersonal Ġexperiences : Ġin Ġorder Ġto Ġinst ill Ġhope , Ġcommenting Ġposters Ġmust Ġshow Ġto Ġoriginal Ġposters Ġan Ġoverall Ġpositive Ġpersonal Ġexperience . ĠTo Ġdetect Ġpositive Ġpersonal Ġexperience , Ġwe Ġrequire Ġthe Ġpresence Ġof Ġstatements Ġin Ġcomments Ġand Ġa Ġpositive Ġsentiment Ġof Ġcomments Ġrep lying Ġto Ġnegative Ġposts . </s> Alt ru ism Ġconsists Ġof Ġpeers Ġoffering Ġsupport , Ġreass urance , Ġsuggestions Ġand Ġinsight , Ġsince Ġthey Ġshare Ġsimilar Ġproblems Ġwith Ġone Ġanother ĠB IB REF 7 . ĠThe Ġexperience Ġof Ġfinding Ġthat Ġa Ġpatient Ġcan Ġbe Ġof Ġvalue Ġto Ġothers Ġis Ġrefreshing Ġand Ġboosts Ġself - esteem ĠB IB REF 7 . ĠHowever , Ġin Ġthe Ġcurrent Ġstudy Ġwe Ġfocus Ġon Ġtesting Ġwhether Ġcommenting Ġposters Ġare Ġaltru ists Ġor Ġnot . ĠWe Ġdo Ġnot Ġtest Ġwhether Ġthe Ġaltru istic Ġbehavior Ġleads Ġto Ġan Ġimprovement Ġon Ġthe Ġaltru ist Ġitself . ĠFor Ġthese Ġreasons , Ġwe Ġdefine Ġthree Ġmain Ġconditions : </s> improve ment Ġof Ġoriginal Ġposter 's Ġsentiment : Ġwe Ġhypothes ize Ġthat Ġsupportive Ġand Ġreassuring Ġstatements Ġimprove Ġthe Ġsentiment Ġscore Ġof Ġthe Ġoriginal Ġposter ; </s> posts Ġcontains Ġnegative Ġpersonal Ġexperiences : Ġusers Ġoffer Ġsupport , Ġreass urance Ġand Ġsuggestion Ġwhen Ġfacing Ġa Ġnegative Ġpersonal Ġexperience Ġof Ġthe Ġoriginal Ġposter ; </s> comments Ġcontaining Ġpositive Ġstatements : Ġeither Ġsupportive Ġor Ġreassuring Ġstatements Ġshow Ġby Ġdefinition Ġa Ġpositive Ġintended Ġemotional Ġcommunication . ĠThus Ġcomments Ġto Ġthe Ġpost Ġshould Ġconsist Ġof Ġpositive Ġsentiment Ġstatements . </s> C onsequ ently , Ġa Ġconversation Ġcontaining Ġthe Ġaforementioned Ġtherapeutic Ġfactors Ġshould Ġsatisfy Ġthe Ġfollowing Ġconditions Ġin Ġterms Ġof ĠN LP : ĠSent iment ĠAnalysis Ġand ĠDialogue ĠActs . </s> original Ġposters Ġhave Ġa Ġhigher Ġsentiment Ġscore Ġat Ġthe Ġend Ġof Ġthe Ġthread Ġthan Ġat Ġthe Ġbeginning ; </s> the Ġoriginal Ġpost Ġconsists Ġmostly Ġof Ġpolar ised Ġstatements ; </s> the Ġpresence Ġof Ġa Ġsignificant Ġamount Ġof Ġstatements Ġin Ġcomments , Ġsince Ġboth Ġsupport Ġand Ġsharing Ġsimilar Ġnegative Ġexperiences Ġcan Ġbe Ġrepresented Ġas Ġstatements ; </s> both Ġnegative Ġand Ġpositive Ġstatements Ġin Ġcomments Ġlead Ġto Ġhigher Ġfinal Ġsentiment Ġscore Ġof Ġthe Ġoriginal Ġposter . </s> Dat as ets </s> We Ġverify Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors Ġin Ġtwo Ġsocial Ġmedia Ġdatasets : ĠOS G Ġand ĠTwitter . ĠThe Ġfirst Ġdataset Ġis Ġcrawled Ġfrom Ġan Ġon - line Ġsupport Ġgroups Ġwebsite , Ġand Ġthe Ġsecond Ġdataset Ġconsists Ġof Ġa Ġsmall Ġsample Ġof ĠTwitter Ġconversation Ġthreads . ĠSince Ġthe Ġformer Ġconsists Ġof Ġmulti - thread ed Ġconversations , Ġwe Ġapply Ġa Ġpre - processing Ġto Ġextract Ġconversation Ġthreads Ġto Ġprovide Ġa Ġfair Ġcomparison Ġwith Ġthe ĠTwitter Ġdataset . ĠAn Ġexample Ġconversation Ġfrom Ġeach Ġdata Ġsource Ġis Ġpresented Ġin ĠFigure ĠFIG REF 19 . </s> Dat as ets Ġ: :: ĠTwitter </s> We Ġhave Ġdownloaded Ġ1 , 8 73 ĠTwitter Ġconversation Ġthreads , Ġroughly Ġ14 k Ġtweets , Ġfrom Ġa Ġpublicly Ġavailable Ġresource Ġthat Ġwere Ġpreviously Ġpre - process ed Ġand Ġhave Ġconversation Ġthreads Ġextracted . ĠA Ġconversation Ġin Ġthe Ġdataset Ġconsists Ġof Ġat Ġleast Ġ4 Ġtweets . ĠEven Ġthough , Ġaccording Ġto ĠB IB REF 23 , ĠTwitter Ġis Ġbroadly Ġapplicable Ġto Ġpublic Ġhealth Ġresearch , Ġour Ġexpectation Ġis Ġthat Ġit Ġcontains Ġless Ġtherapeutic Ġconversations Ġin Ġcomparison Ġto Ġspecialized Ġon - line Ġsupport Ġforums . </s> Dat as ets Ġ: :: ĠOS G </s> Our Ġdata Ġhas Ġbeen Ġdeveloped Ġby Ġcrawling Ġand Ġpre - processing Ġan ĠOS G Ġweb Ġforum . ĠThe Ġforum Ġhas Ġa Ġgreat Ġvariety Ġof Ġdifferent Ġgroups Ġsuch Ġas Ġdepression , Ġanxiety , Ġstress , Ġrelationship , Ġcancer , Ġsexually Ġtransmitted Ġdiseases , Ġetc . ĠEach Ġconversation Ġstarts Ġwith Ġone Ġpost Ġand Ġcan Ġcontain Ġmultiple Ġcomments . ĠEach Ġpost Ġor Ġcomment Ġis Ġrepresented Ġby Ġa Ġposter , Ġa Ġtimestamp , Ġa Ġlist Ġof Ġusers Ġit Ġis Ġreferencing Ġto , Ġthread Ġid , Ġa Ġcomment Ġid Ġand Ġa Ġconversation Ġid . ĠThe Ġthread Ġid Ġis Ġthe Ġsame Ġfor Ġcomments Ġrep lying Ġto Ġeach Ġother , Ġotherwise Ġit Ġis Ġdifferent . ĠThe Ġthread Ġid Ġis Ġincreasing Ġwith Ġtime . ĠThus , Ġit Ġprovides Ġordering Ġamong Ġthreads ; Ġwhereas Ġthe Ġtimestamp Ġprovides Ġordering Ġin Ġthe Ġthread . </s> Each Ġconversation Ġcan Ġbelong Ġto Ġmultiple Ġgroups . ĠConsequently , Ġthe Ġdataset Ġneeds Ġto Ġbe Ġprocessed Ġto Ġremove Ġdupl icates . ĠThe Ġdataset Ġresulting Ġafter Ġde - du pl ication Ġcontains Ġ295 Ġthousand Ġconversations , Ġeach Ġconversation Ġcontains Ġon Ġaverage Ġ6 Ġcomments . ĠIn Ġtotal , Ġthere Ġare Ġ1 . 5 Ġmillion Ġcomments . ĠSince Ġthe Ġcreated Ġdataset Ġis Ġmulti - thread ed , Ġwe Ġneed Ġto Ġextract Ġconversation Ġthreads , Ġto Ġeliminate Ġpaths Ġnot Ġrelevant Ġto Ġthe Ġoriginal Ġpost . </s> Dat as ets Ġ: :: ĠOS G Ġ: :: ĠConversation ĠThread ĠExt raction </s> The Ġthread Ġextraction Ġalgorithm Ġis Ġhe uristic - based Ġand Ġconsists Ġof Ġtwo Ġsteps : Ġ( 1 ) Ġcreation Ġof Ġa Ġtree , Ġbased Ġon Ġa Ġpost Ġwritten Ġby Ġa Ġuser Ġand Ġthe Ġrelated Ġcomments Ġand Ġ( 2 ) Ġtransformation Ġof Ġthe Ġtree Ġinto Ġa Ġlist Ġof Ġthreads . </s> The Ġtree Ġcreation Ġis Ġan Ġextension Ġof Ġthe Ġapproach Ġof ĠB IB REF 24 , Ġwhere Ġfirst Ġa Ġgraph Ġof Ġconversation Ġis Ġconstructed . ĠIn Ġthe Ġapproach , Ġdirect Ġreplies Ġto Ġa Ġpost Ġare Ġattached Ġto Ġthe Ġfirst Ġnesting Ġlevel Ġand Ġsubsequent Ġcomments Ġto Ġincreasing Ġnesting Ġlevels . ĠIn Ġour Ġapproach , Ġwe Ġalso Ġexploit Ġcomments ' Ġfeatures . </s> The Ġtree Ġcreation Ġis Ġperformed Ġwithout Ġprocessing Ġthe Ġcontent Ġof Ġcomments , Ġwhich Ġallows Ġus Ġto Ġprocess Ġposts Ġand Ġcomments Ġof Ġany Ġlength Ġefficiently . ĠThe Ġhe uristic Ġused Ġin Ġthe Ġprocess Ġis Ġbased Ġon Ġthree Ġsimpl ifying Ġassumptions : </s> Unless Ġthere Ġis Ġa Ġspecific Ġreference Ġto Ġanother Ġcomment Ġor Ġa Ġuser , Ġcomments Ġare Ġattached Ġto Ġthe Ġoriginal Ġpost . </s> When Ġrep lying , Ġthe Ġcommenting Ġposter Ġis Ġalways Ġrep lying Ġto Ġthe Ġoriginal Ġpost Ġor Ġsome Ġother Ġcomment . ĠUnless Ġspecified Ġotherwise , Ġit Ġis Ġassumed Ġthat Ġit Ġis Ġa Ġresponse Ġto Ġthe Ġprevious Ġ( in Ġtime ) Ġpost / comment . </s> Sub sequent Ġcomments Ġby Ġthe Ġsame Ġposter Ġare Ġpart Ġof Ġthe Ġsame Ġthread . </s> To Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġthread Ġextraction Ġalgorithm , Ġ2 Ġannot ators Ġhave Ġmanually Ġconstructed Ġthe Ġtrees Ġfor Ġ100 Ġconversations . ĠThe Ġperformance Ġof Ġthe Ġalgorithm Ġon Ġthis Ġset Ġof Ġ100 Ġconversations Ġis Ġevaluated Ġusing Ġaccuracy Ġand Ġstandard ĠInformation ĠRet ri eval Ġevaluation Ġmetrics Ġof Ġprecision , Ġrecall , Ġand ĠF $ _ 1 $ Ġmeasure . ĠThe Ġresults Ġare Ġreported Ġin ĠTable ĠT AB REF 28 Ġtogether Ġwith Ġrandom Ġand Ġmajority Ġbas elines . ĠThe Ġturn - level Ġpercent Ġagreement Ġbetween Ġthe Ġ2 Ġannot ators Ġis Ġ97 . 99 % Ġand ĠCohen 's ĠKappa ĠCo efficient Ġis Ġ83 . 80 %. </s> Dat as ets Ġ: :: ĠData ĠRepresent ation </s> For Ġboth Ġdata Ġsources , ĠTwitter Ġand ĠOS G Ġwith Ġextracted Ġthreads , Ġposts Ġand Ġcomments Ġare Ġtoken ized Ġand Ġsentence Ġsplit . ĠEach Ġsentence Ġis Ġpassed Ġthrough ĠSent iment ĠAnalysis Ġand ĠDialogue ĠAct Ġtagging . ĠSince Ġa Ġpost Ġor Ġa Ġcomment Ġcan Ġcontain Ġmultiple Ġsentences , Ġtherefore Ġmultiple ĠDialogue ĠActs , Ġit Ġis Ġrepresented Ġas Ġas Ġa Ġone - hot Ġencoding , Ġwhere Ġeach Ġposition Ġrepresents Ġa ĠDialogue ĠAct . </s> For ĠSent iment ĠAnalysis Ġwe Ġuse Ġa Ġlex icon - based Ġsentiment Ġanalys er Ġintroduced Ġby ĠB IB REF 25 . ĠFor ĠDialogue ĠAct Ġtagging , Ġon Ġthe Ġother Ġhand , Ġwe Ġmake Ġuse Ġof Ġa Ġmodel Ġtrained Ġon ĠN PS Chat Ġcorpus ĠB IB REF 22 Ġfollowing Ġthe Ġapproach Ġof ĠB IB REF 26 . </s> Analysis </s> As Ġwe Ġmentioned Ġin ĠSection Ġ3 , Ġthe Ġpresence Ġof Ġeach Ġof Ġthe Ġtherapeutic Ġconditions Ġunder Ġanalysis Ġis Ġa Ġnecessary Ġfor Ġa Ġconversation Ġto Ġbe Ġconsidered Ġto Ġhave Ġtherapeutic Ġfactors . ĠIn Ġthis Ġsection Ġwe Ġpresent Ġthe Ġresults Ġof Ġour Ġanalysis Ġwith Ġrespect Ġto Ġthese Ġconditions . </s> Analysis Ġ: :: ĠChange Ġin ĠSent iment Ġscore Ġof ĠOriginal ĠPost ers </s> The Ġfirst Ġcondition Ġwhich Ġwe Ġtest Ġis Ġthe Ġsentiment Ġchange Ġin Ġconversation Ġthreads , Ġcomparing Ġthe Ġinitial Ġand Ġfinal Ġsentiment Ġscores Ġ( i . e . Ġposts ' Ġscores ) Ġof Ġthe Ġoriginal Ġposter . ĠThe Ġresults Ġof Ġthe Ġanalysis Ġare Ġpresented Ġin ĠFigure ĠFIG REF 33 . ĠIn Ġthe Ġfigure Ġwe Ġcan Ġobserve Ġthat Ġthe Ġdistribution Ġof Ġthe Ġsentiment Ġchange Ġin Ġthe Ġtwo Ġdatasets Ġis Ġdifferent . ĠWhile Ġin ĠTwitter Ġthe Ġamount Ġof Ġconversations Ġthat Ġlead Ġto Ġthe Ġincrease Ġof Ġsentiment Ġscore Ġis Ġroughly Ġequal Ġto Ġthe Ġamount Ġof Ġconversations Ġthat Ġlead Ġto Ġthe Ġdecrease Ġof Ġsentiment Ġscore ; Ġthe Ġsituation Ġis Ġdifferent Ġfor ĠOS G . ĠIn ĠOS G , Ġthe Ġamount Ġof Ġconversations Ġthat Ġlead Ġto Ġthe Ġincrease Ġof Ġsentiment Ġscore Ġis Ġconsiderably Ġhigher . </s> Figure ĠFIG REF 34 Ġprovides Ġa Ġmore Ġfine Ġgr ained Ġanalysis , Ġwhere Ġwe Ġadditionally Ġanalyse Ġthe Ġsentiment Ġchange Ġin Ġnominal Ġpol arity Ġterms ĠâĢĵ Ġnegative Ġand Ġpositive . ĠIn ĠOS G , Ġthe Ġnumber Ġof Ġusers Ġthat Ġchanged Ġpol arity Ġfrom Ġnegative Ġto Ġpositive Ġis Ġmore Ġthan Ġthe Ġdouble Ġof Ġthe Ġusers Ġthat Ġhave Ġchanged Ġthe Ġpol arity Ġfrom Ġpositive Ġto Ġnegative . ĠIn ĠTwitter , Ġon Ġthe Ġother Ġhand , Ġthe Ġusers Ġmostly Ġchanged Ġpol arity Ġfrom Ġpositive Ġto Ġnegative . ĠResults Ġof Ġthe Ġanalysis Ġsuggest Ġthat Ġin ĠOS G Ġ, Ġsentiment Ġincreases Ġand Ġusers Ġtend Ġto Ġchange Ġpol arity Ġfrom Ġnegative Ġto Ġpositive , Ġwhereas Ġin ĠTwitter Ġsentiment Ġtends Ġto Ġdecrease . ĠVer ification Ġof Ġthis Ġcondition Ġalone Ġindicates Ġthat Ġthe Ġratio Ġof Ġpotentially Ġtherapeutic Ġconversations Ġin ĠTwitter Ġis Ġlower . </s> Analysis Ġ: :: ĠStructure Ġof ĠPosts Ġand ĠComments </s> Table ĠT AB REF 36 Ġpresents Ġthe Ġdistribution Ġof Ġautomatically Ġpredicted Ġper - sent ence ĠDialogue ĠActs Ġin Ġthe Ġdatasets . ĠThe Ġmost Ġfrequent Ġtag Ġis Ġstatement Ġin Ġboth . ĠIn ĠTable ĠT AB REF 37 , Ġon Ġthe Ġother Ġhand , Ġwe Ġpresent Ġthe Ġdistribution Ġof Ġpost Ġand Ġcomment Ġstructures Ġin Ġterms Ġof Ġautomatically Ġpredicted ĠDialogue ĠAct Ġtags . ĠThe Ġstructure Ġis Ġan Ġun ordered Ġset Ġof Ġtags Ġin Ġthe Ġpost Ġor Ġcomment . ĠFrom Ġthe Ġtable Ġwe Ġcan Ġobserve Ġthat Ġthe Ġdistribution Ġof Ġtag Ġsets Ġis Ġsimilar Ġbetween Ġposts Ġand Ġcomments . ĠIn Ġboth Ġcases Ġthe Ġmost Ġcommon Ġset Ġis Ġstatement Ġonly . ĠHowever , Ġconversations Ġcontaining Ġonly Ġstatement , Ġemphasis Ġor Ġquestion Ġposts Ġand Ġcomments Ġpredominantly Ġappear Ġin ĠTwitter . ĠWhich Ġis Ġexpected Ġdue Ġto Ġthe Ġshorter Ġlength Ġof ĠTwitter Ġposts Ġand Ġcomments . </s> We Ġcan Ġalso Ġobserve Ġthat Ġthe Ġoriginal Ġposters Ġtend Ġto Ġask Ġmore Ġquestions Ġthan Ġthe Ġcommenting Ġposters ĠâĢĵ Ġ19 . 83 % Ġfor Ġposts Ġvs . Ġ11 . 21 % Ġfor Ġcomments Ġ( sum med ). ĠThis Ġsuggests Ġthat Ġthe Ġoriginal Ġposters Ġfrequently Ġask Ġeither Ġfor Ġsuggestion Ġor Ġconfirmation Ġof Ġtheir Ġpoints Ġof Ġview Ġor Ġtheir Ġdis conf irmation . ĠHowever , Ġthe Ġhigh Ġpresence Ġof Ġpersonal Ġexperiences Ġis Ġsupported Ġby Ġthe Ġhigh Ġnumber Ġof Ġposts Ġcontaining Ġonly Ġstatements . </s> High Ġnumber Ġof Ġstatement Ġtags Ġin Ġcomments Ġsuggests Ġthat Ġusers Ġreply Ġeither Ġwith Ġsupporting Ġor Ġem pathic Ġstatements Ġor Ġpersonal Ġexperience . ĠHowever , Ġ6 . 39 % Ġof Ġcomments Ġcontain Ġaccept Ġand Ġreject Ġtags , Ġwhich Ġmark Ġthe Ġdegree Ġto Ġwhich Ġa Ġspeaker Ġaccepts Ġsome Ġprevious Ġproposal , Ġplan , Ġopinion , Ġor Ġstatement ĠB IB REF 20 . ĠThe Ġdescribed ĠDialogue ĠAct Ġtags Ġare Ġoften Ġused Ġwhen Ġcommenting Ġposters Ġdiscuss Ġoriginal Ġposter 's Ġpoint Ġof Ġview . ĠFor Ġinstance , ĠâĢ ľ It 's Ġtrue . ĠI Ġfelt Ġthe Ġsame . âĢ Ŀ ĠâĢĵ Ġ{ Accept , ĠStatement } Ġor ĠâĢ ľ Well Ġno . ĠYou 're Ġnot Ġalone âĢ Ŀ ĠâĢĵ Ġ{ Re ject , ĠStatement }. ĠThe Ġdatasets Ġdiffer Ġwith Ġrespect Ġto Ġthe Ġdistribution Ġof Ġthese ĠDialogue ĠActs Ġtags , Ġthey Ġappear Ġmore Ġfrequently Ġin ĠOS G . </s> Analysis Ġ: :: ĠSent iment Ġof ĠPosts Ġand ĠComments </s> Table ĠT AB REF 39 Ġpresents Ġthe Ġdistribution Ġof Ġsentiment Ġpol arity Ġin Ġpost Ġand Ġcomment Ġstatements Ġ( i . e . Ġsentences Ġtagged Ġas Ġstatement ). ĠFor ĠOS G , Ġthe Ġpredominant Ġsentiment Ġlabel Ġof Ġstatements Ġis Ġpositive Ġand Ġit Ġis Ġthe Ġhighest Ġfor Ġboth Ġposts Ġand Ġcomments . ĠHowever , Ġthe Ġdifference Ġbetween Ġthe Ġamounts Ġof Ġpositive Ġand Ġnegative Ġstatements Ġis Ġhigher Ġfor Ġthe Ġrep lying Ġcomments Ġ( 34 . 5 % Ġvs . Ġ42 . 5 %). ĠFor ĠTwitter , Ġon Ġthe Ġother Ġhand , Ġthe Ġpredominant Ġsentiment Ġlabel Ġof Ġstatements Ġis Ġneutral Ġand Ġthe Ġpol arity Ġdistribution Ġbetween Ġposts Ġand Ġcomments Ġis Ġvery Ġclose . ĠOne Ġparticular Ġobservation Ġis Ġthat Ġthe Ġratio Ġof Ġnegative Ġstatements Ġis Ġhigher Ġin ĠOS G Ġfor Ġboth Ġposts Ġand Ġcomments Ġthan Ġin ĠTwitter , Ġwhich Ġsupports Ġthe Ġidea Ġof Ġsharing Ġnegative Ġexperiences . </s> Further Ġwe Ġanalyze Ġwhether Ġthe Ġsentiment Ġof Ġa Ġcomment Ġ( i . e . Ġthe Ġrep lying Ġuser ) Ġis Ġaffected Ġby Ġthe Ġsentiment Ġof Ġthe Ġoriginal Ġpost Ġ( i . e . Ġthe Ġuser Ġbeing Ġreplied Ġto ), Ġwhich Ġwill Ġimply Ġthat Ġthe Ġusers Ġadapt Ġtheir Ġbehaviour Ġwith Ġrespect Ġto Ġthe Ġpost 's Ġsentiment . ĠFor Ġthe Ġanalysis , Ġwe Ġsplit Ġthe Ġdatasets Ġinto Ġthree Ġbuckets Ġaccording Ġto Ġthe Ġposts ' Ġsentiment Ġscore ĠâĢĵ Ġnegative , Ġneutral , Ġor Ġpositive , Ġand Ġrepresent Ġeach Ġconversation Ġin Ġterms Ġof Ġpercentages Ġof Ġcomments Ġ( repl ies ) Ġwith Ġeach Ġsentiment Ġlabel . ĠThe Ġbuckets Ġare Ġthen Ġcompared Ġusing Ġt - test Ġfor Ġstatistically Ġsignificant Ġdifferences . </s> Table ĠT AB REF 40 Ġpresents Ġthe Ġdistribution Ġof Ġsentiment Ġlabels Ġwith Ġrespect Ġto Ġthe Ġpost 's Ġsentiment Ġscore . ĠThe Ġpatterns Ġof Ġdistribution Ġare Ġsimilar Ġacross Ġthe Ġdatasets . ĠWe Ġcan Ġobserve Ġthat Ġoverall , Ġreplies Ġtend Ġto Ġhave Ġa Ġpositive Ġsentiment , Ġwhich Ġsuggests Ġthat Ġrep lying Ġposters Ġtend Ġto Ġhave Ġa Ġpositive Ġattitude . ĠHowever , Ġthe Ġratio Ġof Ġpositive Ġcomments Ġis Ġhigher Ġfor ĠOS G Ġthan Ġfor ĠTwitter . </s> The Ġresults Ġof Ġthe ĠWelch 's Ġt - test Ġon ĠOS G Ġdata Ġreveal Ġthat Ġthere Ġare Ġstatistically Ġsignificant Ġdifferences Ġin Ġthe Ġdistribution Ġof Ġrep lying Ġcomments ' Ġsentiment Ġbetween Ġconversations Ġwith Ġpositive Ġand Ġnegative Ġstarting Ġposts . ĠA Ġpositive Ġpost Ġtends Ġto Ġget Ġsignificantly Ġmore Ġpositive Ġreplies . ĠSimilarly , Ġa Ġnegative Ġpost Ġtends Ġto Ġget Ġsignificantly Ġmore Ġnegative Ġreplies Ġ( both Ġwith Ġ$ p Ġ< Ġ0 . 01 $ ). </s> Table ĠT AB REF 41 Ġpresents Ġthe Ġdistribution Ġof Ġthe Ġsentiment Ġlabels Ġof Ġthe Ġfinal Ġtext Ġprovided Ġby Ġthe Ġoriginal Ġposter Ġwith Ġrespect Ġto Ġthe Ġsentiment Ġpol arity Ġof Ġthe Ġcomments . ĠThe Ġresults Ġindicate Ġthat ĠOS G Ġparticipants Ġare Ġmore Ġsupportive , Ġas Ġthe Ġmajority Ġof Ġconversations Ġend Ġin Ġa Ġpositive Ġfinal Ġsentiment Ġregardless Ġof Ġthe Ġsentiment Ġof Ġcomments . ĠWe Ġcan Ġalso Ġobserve Ġthat Ġnegative Ġcomments Ġin ĠOS G Ġlead Ġto Ġpositive Ġsentiment , Ġwhich Ġsupports Ġthe Ġidea Ġof Ġsharing Ġthe Ġnegative Ġexperiences , Ġthus Ġpresence Ġof Ġtherapeutic Ġfactors . ĠFor ĠTwitter , Ġon Ġthe Ġother Ġhand , Ġonly Ġpositive Ġcomments Ġlead Ġto Ġthe Ġpositive Ġfinal Ġsentiments , Ġwhereas Ġother Ġcomments Ġlead Ġpredominantly Ġto Ġneutral Ġfinal Ġsentiments . </s> Our Ġanalysis Ġin Ġterms Ġof Ġsentiment Ġand ĠDialogue ĠActs Ġsupports Ġthe Ġpresence Ġof Ġthe Ġthree Ġselected Ġtherapeutic Ġfactors ĠâĢĵ ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope ĠâĢĵ Ġin ĠOS G Ġmore Ġthan Ġin ĠTwitter . ĠThe Ġmain Ġcontributors Ġto Ġthis Ġconclusion Ġare Ġthe Ġfacts Ġthat Ġthere Ġis Ġmore Ġpositive Ġchange Ġin Ġthe Ġsentiment Ġof Ġthe Ġoriginal Ġposters Ġin ĠOS G Ġ( people Ġseeking Ġsupport ) Ġand Ġthat Ġin ĠOS G Ġeven Ġnegative Ġand Ġneutral Ġcomments Ġare Ġlikely Ġto Ġlead Ġto Ġpositive Ġchanges . </s> Conclusion </s> In Ġthis Ġwork , Ġwe Ġpropose Ġa Ġmethodology Ġto Ġautomatically Ġanalyse Ġonline Ġsocial Ġplatforms Ġfor Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors Ġ( i . e . ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope ). ĠWe Ġevaluate Ġour Ġapproach Ġon Ġtwo Ġon - line Ġplatforms , ĠTwitter Ġand Ġan ĠOS G Ġweb Ġforum . ĠWe Ġapply ĠN LP Ġtechniques Ġof ĠSent iment ĠAnalysis Ġand ĠDialogue ĠAct Ġtagging Ġto Ġautomatically Ġverify Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors , Ġwhich Ġallows Ġus Ġto Ġanalyse Ġlarger Ġamounts Ġof Ġconvers ational Ġdata Ġ( as Ġcompared Ġto Ġprevious Ġstudies ). </s> Our Ġanalysis Ġindicates Ġthat ĠOS G Ġconversations Ġsatisfy Ġhigher Ġnumber Ġof Ġconditions Ġapprox imating Ġtherapeutic Ġfactors Ġthan ĠTwitter Ġconversations . ĠGiven Ġthis Ġoutcome , Ġwe Ġpost ulate Ġthat Ġusers Ġwho Ġjoin Ġsupport Ġgroup Ġwebsites Ġspontaneously Ġseem Ġto Ġbenefit Ġfrom Ġit . ĠIndeed , Ġas Ġshown Ġin ĠSection ĠSEC REF 5 , Ġthe Ġoriginal Ġposters Ġwho Ġinteract Ġwith Ġothers Ġby Ġrep lying Ġto Ġcomments , Ġhave Ġbenefited Ġfrom Ġan Ġimprovement Ġof Ġtheir Ġemotional Ġstate . </s> We Ġwould Ġlike Ġto Ġre em phas ise Ġthat Ġthe Ġconditions Ġfor Ġthe Ġtherapeutic Ġfactors Ġare Ġnecessary Ġbut Ġnot Ġsufficient ; Ġsince Ġour Ġanalysis Ġfocuses Ġon Ġthe Ġstructure Ġof Ġconversations , Ġbeing Ġag nostic Ġto Ġthe Ġcontent . ĠN LP , Ġhowever , Ġallows Ġus Ġto Ġstrengthen Ġour Ġapprox im ations Ġeven Ġfurther . ĠThus , Ġthe Ġfurther Ġextension Ġof Ġour Ġwork Ġis Ġalso Ġaug mentation Ġof Ġour Ġstudy Ġwith Ġother Ġlanguage Ġanalysis Ġmetrics Ġand Ġtheir Ġcorrelation Ġwith Ġhuman Ġannotation . </s> It Ġshould Ġbe Ġnoted Ġthat Ġthe Ġproposed Ġapproach Ġis Ġan Ġapproximation Ġof Ġthe Ġtedious Ġtasks Ġof Ġannotation Ġof Ġconversations Ġby Ġexperts Ġvers ed Ġin Ġthe Ġtherapeutic Ġfactors Ġand Ġtheir Ġassociated Ġtheories . ĠEven Ġthough Ġwe Ġcan Ġuse ĠSent iment ĠAnalysis Ġto Ġdetect Ġthe Ġexistence Ġof Ġtherapeutic Ġfactors , Ġwe Ġcannot Ġdifferentiate Ġbetween ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope , Ġas Ġthis Ġrequires Ġdifferentiation Ġbetween Ġemotional Ġstate Ġof Ġthe Ġuser Ġand Ġthe Ġintended Ġemotional Ġcommunication . ĠThus , Ġthe Ġnatural Ġextensions Ġof Ġthis Ġwork Ġare Ġdifferentiation Ġbetween Ġdifferent Ġtherapeutic Ġfactors Ġand Ġcomparison Ġof Ġthe Ġproposed Ġanalysis Ġto Ġthe Ġhuman Ġevaluation . </s> Although Ġwe Ġacknowledge Ġthat Ġthe Ġproposed Ġmethodology Ġdoes Ġnot Ġserve Ġas Ġa Ġreplacement Ġof Ġmanual Ġanalysis Ġof ĠOS G Ġfor Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors , Ġwe Ġbelieve Ġthat Ġit Ġcould Ġfacilitate Ġand Ġsupplement Ġthis Ġprocess . ĠThe Ġmethod Ġcan Ġserve Ġas Ġa Ġtool Ġfor Ġgeneral Ġpractitioners Ġand Ġpsychologists Ġwho Ġcan Ġuse Ġit Ġas Ġan Ġadditional Ġsource Ġof Ġinformation Ġregarding Ġtheir Ġpatients âĢ Ļ Ġcondition Ġand , Ġin Ġturn , Ġoffer Ġa Ġmore Ġpersonal ised Ġsupport Ġthat Ġis Ġbetter Ġtailored Ġto Ġindividual Ġtherapeutic Ġneeds .
Introduction Recently, people have started looking at online forums either as a primary or secondary source of counseling services BIBREF0. BIBREF1 reported that over the first five years of operation (2011-2016), ReachOut.com – Ireland's online youth mental health service – 62% of young people would visit a website for support when going through a tough time. With the expansion of the Internet, there has been a substantial growth in the number of users looking for psychological support online. The importance of the on-line life of patients has been recognized in research as well. BIBREF2 stated that the online life of patients constitutes a major influence on their self-definition. Furthermore, according to BIBREF3, the social networking activities of an individual, offer an important reflection of their personality. While dealing with patients suffering from psychological problems, it is important that therapists do not ignore this pivotal source of information which can provide deep insights into their patients' mental conditions. Acceptance of on-line support groups (OSG) by Mental Health Professionals is still not established BIBREF4. Since OSG can have double-edged effects on patients and the presence of professionals is often limited, we argue that their properties should be further studied. According to BIBREF5 OSG effectiveness is hard to assess, while some studies showed OSG's potential to change participants' attitudes, no such effect was observed in other studies (see Related Work Section for more details). Furthermore the scope of previous work on analysis of users' behaviour in OSG has been limited by the fact that they relied on expert annotation of posts and comments BIBREF6. We present a novel approach for automatically analysing online conversations for the presence of therapeutic factors of group therapy defined by BIBREF7 as “the actual mechanisms of effecting change in the patient”. The authors have identified 11 therapeutic factors in group therapy: Universality, Altruism, Instillation of Hope, Guidance, Imparting information, Developing social skills, Interpersonal learning, Cohesion, Catharsis, Existential factors, Imitative behavior and Corrective recapitulation of family of origin issues. In this paper, we focus on 3 therapeutic factors: Universality, Altruism and Instillation of Hope (listed below), as we believe that these can be approximated by using established NLP techniques (e.g. Sentiment Analysis, Dialogue Act tagging etc.). Universality: the disconfirmation of a user's feelings of uniqueness of their mental health condition. Altruism: others offer support, reassurance, suggestions and insight. Instillation of Hope: inspiration provided to participants by their peers. The selected therapeutic factors are analysed in terms of illocutionary force and attitude. Due to the multi-party and asynchronous nature of on-line social media conversations, prior to the analysis, we extract conversation threads among users – an essential prerequisite for any kind of higher-level dialogue analysis BIBREF10. Afterwards, the illocutionary force is identified using Dialogue Act tagging, whereas the attitude by using Sentiment Analysis. The quantitative analysis is then performed on these processed conversations. Ideally, the analysis would require experts to annotate each post and comment on the presence of therapeutic factors. However, due to time and cost demands of this task, it is feasible to analyse only a small fraction of the available data. Compared to previous studies (e.g. BIBREF6) that analysed few tens of conversations and several thousand lines of chat; using the proposed approach – application of Dialogue Acts and Sentiment Analysis – we were able to automatically analyse approximately 300 thousands conversations (roughly 1.5 million comments). The rest of the paper is structured as follows. In Section 2 we introduce related work. Next, in Section 3 we describe the pre-processing pipeline and the methodology to perform thread extraction on asynchronous multi-party conversations. In Section 4 we provide the describe the final dataset used for the analysis, and in Section 5 we present the results of our analysis. Finally, in Section 6 we provide concluding remarks and future research directions. Related Work On-line support groups have been analyzed for various factors before. For instance, BIBREF11 analysed stress reduction in on-line support group chat-rooms, and the effects of on-line social interactions. Such studies mostly relied on questionnaires and were based on a small number of users. Nevertheless, in BIBREF11, the author showed that social support facilitates coping with distress, improves mood and expedites recovery from it. These findings highlight that, overall, on-line discussion boards appear to be therapeutic and constructive for individuals suffering alcohol-abuse. Application of NLP to the analysis of mental health-related conversation has been studied as well (e.g. BIBREF12, BIBREF13). BIBREF6 applied sentiment-analysis combined with extensive turn-level annotation to investigate stress reduction in on-line support group chat-rooms, showing that sentiment-analysis is a good predictor of entrance stress level. Furthermore, similar to our setting, they applied automatic thread-extraction to determine conversation threads. BIBREF14 have shown that on-line support group therapy increased the quality of life of patients with metastatic breast cancer. Since many original posters reported the benefits of group therapy on patients BIBREF15, BIBREF2, BIBREF16, BIBREF17, BIBREF18, BIBREF7, we evaluate the effect of the user interaction using sentiment scores of comments in on-line support groups. According to BIBREF6, users with high incoming stress tend to request less information from others, as a percentage of their time, and share much more information, in absolute terms. In addition, high information sharing has been shown to be a good predictor of stress reduction at the end of the chat BIBREF6. Regarding information sharing, we rely on Dialogue Acts BIBREF19 to model the speaker's intention in producing an utterance. In particular, we are interested in Dialogue Act label that is defined to represent descriptive, narrative, or personal information – the statement. Dialogue Acts have been applied to the analysis of spoken BIBREF20, BIBREF21 as well as on-line written synchronous conversations BIBREF22. We apply Dialogue Act tag set defined in BIBREF22 to the analysis of our on-line asynchronous conversations. We argue that Dialogue Acts can be used to analyse user behaviour in social media and verify the presence of therapeutic factors. Methodology We select the three therapeutic factors – Universality, Altruism and Instillation of Hope – that can be best approximated using NLP techniques: Sentiment Analysis and Dialogue Act tagging. We discuss each one of the selected therapeutic factors and the identified necessary conditions. The listed conditions, however, are not sufficient to attribute the presence of a therapeutic factor with high confidence, which only can be obtained using expert annotation. Our analysis focuses on the structure of conversations; though content plays an important role as well. Universality consists in the disconfirmation of patients' belief of uniqueness of their disease. This therapeutic factor is shown to be a powerful source of relief for the patient, according to BIBREF7. From this definition, we can draw the following conditions that are applicable to our environment: improvement of original poster's sentiment: we hypothesize that the discovery that other people passed through similar issues leads to a higher sentiment score; posts containing negative personal experiences: to disconfirm the belief of uniqueness users have to share their story; comments containing negative statements: to disconfirm the patient's feelings of uniqueness, the commenting user must tell a similar negative personal experience. This condition requires two sub-conditions: high presence of statements in comments and the presence of negative comments replying to negative posts. Instillation of Hope is based on inspiration provided to participants by their peers. Through the inspiration provided by their peers, patients can increase their expectation on the therapy outcome. BIBREF7 in several studies have demonstrated that a high expectation of help before the start of a therapy is significantly correlated with a positive therapy outcome. The author states that many patients pointed out the importance of having observed the improvement of others. Therefore, the three main conditions are the following: improvement of original poster's sentiment: we hypothesize that instillation of hope leads to a higher sentiment score; posts containing negative personal experiences: hope can be instilled in someone who shares a negative personal experience; comments containing positive personal experiences: in order to instill hope, commenting posters must show to original posters an overall positive personal experience. To detect positive personal experience, we require the presence of statements in comments and a positive sentiment of comments replying to negative posts. Altruism consists of peers offering support, reassurance, suggestions and insight, since they share similar problems with one another BIBREF7. The experience of finding that a patient can be of value to others is refreshing and boosts self-esteem BIBREF7. However, in the current study we focus on testing whether commenting posters are altruists or not. We do not test whether the altruistic behavior leads to an improvement on the altruist itself. For these reasons, we define three main conditions: improvement of original poster's sentiment: we hypothesize that supportive and reassuring statements improve the sentiment score of the original poster; posts contains negative personal experiences: users offer support, reassurance and suggestion when facing a negative personal experience of the original poster; comments containing positive statements: either supportive or reassuring statements show by definition a positive intended emotional communication. Thus comments to the post should consist of positive sentiment statements. Consequently, a conversation containing the aforementioned therapeutic factors should satisfy the following conditions in terms of NLP: Sentiment Analysis and Dialogue Acts. original posters have a higher sentiment score at the end of the thread than at the beginning; the original post consists mostly of polarised statements; the presence of a significant amount of statements in comments, since both support and sharing similar negative experiences can be represented as statements; both negative and positive statements in comments lead to higher final sentiment score of the original poster. Datasets We verify the presence of therapeutic factors in two social media datasets: OSG and Twitter. The first dataset is crawled from an on-line support groups website, and the second dataset consists of a small sample of Twitter conversation threads. Since the former consists of multi-threaded conversations, we apply a pre-processing to extract conversation threads to provide a fair comparison with the Twitter dataset. An example conversation from each data source is presented in Figure FIGREF19. Datasets ::: Twitter We have downloaded 1,873 Twitter conversation threads, roughly 14k tweets, from a publicly available resource that were previously pre-processed and have conversation threads extracted. A conversation in the dataset consists of at least 4 tweets. Even though, according to BIBREF23, Twitter is broadly applicable to public health research, our expectation is that it contains less therapeutic conversations in comparison to specialized on-line support forums. Datasets ::: OSG Our data has been developed by crawling and pre-processing an OSG web forum. The forum has a great variety of different groups such as depression, anxiety, stress, relationship, cancer, sexually transmitted diseases, etc. Each conversation starts with one post and can contain multiple comments. Each post or comment is represented by a poster, a timestamp, a list of users it is referencing to, thread id, a comment id and a conversation id. The thread id is the same for comments replying to each other, otherwise it is different. The thread id is increasing with time. Thus, it provides ordering among threads; whereas the timestamp provides ordering in the thread. Each conversation can belong to multiple groups. Consequently, the dataset needs to be processed to remove duplicates. The dataset resulting after de-duplication contains 295 thousand conversations, each conversation contains on average 6 comments. In total, there are 1.5 million comments. Since the created dataset is multi-threaded, we need to extract conversation threads, to eliminate paths not relevant to the original post. Datasets ::: OSG ::: Conversation Thread Extraction The thread extraction algorithm is heuristic-based and consists of two steps: (1) creation of a tree, based on a post written by a user and the related comments and (2) transformation of the tree into a list of threads. The tree creation is an extension of the approach of BIBREF24, where first a graph of conversation is constructed. In the approach, direct replies to a post are attached to the first nesting level and subsequent comments to increasing nesting levels. In our approach, we also exploit comments' features. The tree creation is performed without processing the content of comments, which allows us to process posts and comments of any length efficiently. The heuristic used in the process is based on three simplifying assumptions: Unless there is a specific reference to another comment or a user, comments are attached to the original post. When replying, the commenting poster is always replying to the original post or some other comment. Unless specified otherwise, it is assumed that it is a response to the previous (in time) post/comment. Subsequent comments by the same poster are part of the same thread. To evaluate the performance of the thread extraction algorithm, 2 annotators have manually constructed the trees for 100 conversations. The performance of the algorithm on this set of 100 conversations is evaluated using accuracy and standard Information Retrieval evaluation metrics of precision, recall, and F$_1$ measure. The results are reported in Table TABREF28 together with random and majority baselines. The turn-level percent agreement between the 2 annotators is 97.99% and Cohen's Kappa Coefficient is 83.80%. Datasets ::: Data Representation For both data sources, Twitter and OSG with extracted threads, posts and comments are tokenized and sentence split. Each sentence is passed through Sentiment Analysis and Dialogue Act tagging. Since a post or a comment can contain multiple sentences, therefore multiple Dialogue Acts, it is represented as as a one-hot encoding, where each position represents a Dialogue Act. For Sentiment Analysis we use a lexicon-based sentiment analyser introduced by BIBREF25. For Dialogue Act tagging, on the other hand, we make use of a model trained on NPSChat corpus BIBREF22 following the approach of BIBREF26. Analysis As we mentioned in Section 3, the presence of each of the therapeutic conditions under analysis is a necessary for a conversation to be considered to have therapeutic factors. In this section we present the results of our analysis with respect to these conditions. Analysis ::: Change in Sentiment score of Original Posters The first condition which we test is the sentiment change in conversation threads, comparing the initial and final sentiment scores (i.e. posts' scores) of the original poster. The results of the analysis are presented in Figure FIGREF33. In the figure we can observe that the distribution of the sentiment change in the two datasets is different. While in Twitter the amount of conversations that lead to the increase of sentiment score is roughly equal to the amount of conversations that lead to the decrease of sentiment score; the situation is different for OSG. In OSG, the amount of conversations that lead to the increase of sentiment score is considerably higher. Figure FIGREF34 provides a more fine grained analysis, where we additionally analyse the sentiment change in nominal polarity terms – negative and positive. In OSG, the number of users that changed polarity from negative to positive is more than the double of the users that have changed the polarity from positive to negative. In Twitter, on the other hand, the users mostly changed polarity from positive to negative. Results of the analysis suggest that in OSG , sentiment increases and users tend to change polarity from negative to positive, whereas in Twitter sentiment tends to decrease. Verification of this condition alone indicates that the ratio of potentially therapeutic conversations in Twitter is lower. Analysis ::: Structure of Posts and Comments Table TABREF36 presents the distribution of automatically predicted per-sentence Dialogue Acts in the datasets. The most frequent tag is statement in both. In Table TABREF37, on the other hand, we present the distribution of post and comment structures in terms of automatically predicted Dialogue Act tags. The structure is an unordered set of tags in the post or comment. From the table we can observe that the distribution of tag sets is similar between posts and comments. In both cases the most common set is statement only. However, conversations containing only statement, emphasis or question posts and comments predominantly appear in Twitter. Which is expected due to the shorter length of Twitter posts and comments. We can also observe that the original posters tend to ask more questions than the commenting posters – 19.83% for posts vs. 11.21% for comments (summed). This suggests that the original posters frequently ask either for suggestion or confirmation of their points of view or their disconfirmation. However, the high presence of personal experiences is supported by the high number of posts containing only statements. High number of statement tags in comments suggests that users reply either with supporting or empathic statements or personal experience. However, 6.39% of comments contain accept and reject tags, which mark the degree to which a speaker accepts some previous proposal, plan, opinion, or statement BIBREF20. The described Dialogue Act tags are often used when commenting posters discuss original poster's point of view. For instance, “It's true. I felt the same.” – {Accept, Statement} or “Well no. You're not alone” – {Reject, Statement}. The datasets differ with respect to the distribution of these Dialogue Acts tags, they appear more frequently in OSG. Analysis ::: Sentiment of Posts and Comments Table TABREF39 presents the distribution of sentiment polarity in post and comment statements (i.e. sentences tagged as statement). For OSG, the predominant sentiment label of statements is positive and it is the highest for both posts and comments. However, the difference between the amounts of positive and negative statements is higher for the replying comments (34.5% vs. 42.5%). For Twitter, on the other hand, the predominant sentiment label of statements is neutral and the polarity distribution between posts and comments is very close. One particular observation is that the ratio of negative statements is higher in OSG for both posts and comments than in Twitter, which supports the idea of sharing negative experiences. Further we analyze whether the sentiment of a comment (i.e. the replying user) is affected by the sentiment of the original post (i.e. the user being replied to), which will imply that the users adapt their behaviour with respect to the post's sentiment. For the analysis, we split the datasets into three buckets according to the posts' sentiment score – negative, neutral, or positive, and represent each conversation in terms of percentages of comments (replies) with each sentiment label. The buckets are then compared using t-test for statistically significant differences. Table TABREF40 presents the distribution of sentiment labels with respect to the post's sentiment score. The patterns of distribution are similar across the datasets. We can observe that overall, replies tend to have a positive sentiment, which suggests that replying posters tend to have a positive attitude. However, the ratio of positive comments is higher for OSG than for Twitter. The results of the Welch's t-test on OSG data reveal that there are statistically significant differences in the distribution of replying comments' sentiment between conversations with positive and negative starting posts. A positive post tends to get significantly more positive replies. Similarly, a negative post tends to get significantly more negative replies (both with $p < 0.01$). Table TABREF41 presents the distribution of the sentiment labels of the final text provided by the original poster with respect to the sentiment polarity of the comments. The results indicate that OSG participants are more supportive, as the majority of conversations end in a positive final sentiment regardless of the sentiment of comments. We can also observe that negative comments in OSG lead to positive sentiment, which supports the idea of sharing the negative experiences, thus presence of therapeutic factors. For Twitter, on the other hand, only positive comments lead to the positive final sentiments, whereas other comments lead predominantly to neutral final sentiments. Our analysis in terms of sentiment and Dialogue Acts supports the presence of the three selected therapeutic factors – Universality, Altruism and Instillation of Hope – in OSG more than in Twitter. The main contributors to this conclusion are the facts that there is more positive change in the sentiment of the original posters in OSG (people seeking support) and that in OSG even negative and neutral comments are likely to lead to positive changes. Conclusion In this work, we propose a methodology to automatically analyse online social platforms for the presence of therapeutic factors (i.e. Universality, Altruism and Instillation of Hope). We evaluate our approach on two on-line platforms, Twitter and an OSG web forum. We apply NLP techniques of Sentiment Analysis and Dialogue Act tagging to automatically verify the presence of therapeutic factors, which allows us to analyse larger amounts of conversational data (as compared to previous studies). Our analysis indicates that OSG conversations satisfy higher number of conditions approximating therapeutic factors than Twitter conversations. Given this outcome, we postulate that users who join support group websites spontaneously seem to benefit from it. Indeed, as shown in Section SECREF5, the original posters who interact with others by replying to comments, have benefited from an improvement of their emotional state. We would like to reemphasise that the conditions for the therapeutic factors are necessary but not sufficient; since our analysis focuses on the structure of conversations, being agnostic to the content. NLP, however, allows us to strengthen our approximations even further. Thus, the further extension of our work is also augmentation of our study with other language analysis metrics and their correlation with human annotation. It should be noted that the proposed approach is an approximation of the tedious tasks of annotation of conversations by experts versed in the therapeutic factors and their associated theories. Even though we can use Sentiment Analysis to detect the existence of therapeutic factors, we cannot differentiate between Altruism and Instillation of Hope, as this requires differentiation between emotional state of the user and the intended emotional communication. Thus, the natural extensions of this work are differentiation between different therapeutic factors and comparison of the proposed analysis to the human evaluation. Although we acknowledge that the proposed methodology does not serve as a replacement of manual analysis of OSG for the presence of therapeutic factors, we believe that it could facilitate and supplement this process. The method can serve as a tool for general practitioners and psychologists who can use it as an additional source of information regarding their patients’ condition and, in turn, offer a more personalised support that is better tailored to individual therapeutic needs.
How large is the Twitter dataset?
33554065284110859a8ea3ca7346474ab2cab100
How Ġlarge Ġis Ġthe ĠTwitter Ġdataset ?
1 , 8 73 ĠTwitter Ġconversation Ġthreads , Ġroughly Ġ14 k Ġtweets
<s> How Ġlarge Ġis Ġthe ĠTwitter Ġdataset ? </s> Introduction </s> Recently , Ġpeople Ġhave Ġstarted Ġlooking Ġat Ġonline Ġforums Ġeither Ġas Ġa Ġprimary Ġor Ġsecondary Ġsource Ġof Ġcounseling Ġservices ĠB IB REF 0 . ĠB IB REF 1 Ġreported Ġthat Ġover Ġthe Ġfirst Ġfive Ġyears Ġof Ġoperation Ġ( 2011 - 2016 ), ĠReach Out . com ĠâĢĵ ĠIreland 's Ġonline Ġyouth Ġmental Ġhealth Ġservice ĠâĢĵ Ġ62 % Ġof Ġyoung Ġpeople Ġwould Ġvisit Ġa Ġwebsite Ġfor Ġsupport Ġwhen Ġgoing Ġthrough Ġa Ġtough Ġtime . ĠWith Ġthe Ġexpansion Ġof Ġthe ĠInternet , Ġthere Ġhas Ġbeen Ġa Ġsubstantial Ġgrowth Ġin Ġthe Ġnumber Ġof Ġusers Ġlooking Ġfor Ġpsychological Ġsupport Ġonline . </s> The Ġimportance Ġof Ġthe Ġon - line Ġlife Ġof Ġpatients Ġhas Ġbeen Ġrecognized Ġin Ġresearch Ġas Ġwell . ĠB IB REF 2 Ġstated Ġthat Ġthe Ġonline Ġlife Ġof Ġpatients Ġconstitutes Ġa Ġmajor Ġinfluence Ġon Ġtheir Ġself - definition . ĠFurthermore , Ġaccording Ġto ĠB IB REF 3 , Ġthe Ġsocial Ġnetworking Ġactivities Ġof Ġan Ġindividual , Ġoffer Ġan Ġimportant Ġreflection Ġof Ġtheir Ġpersonality . ĠWhile Ġdealing Ġwith Ġpatients Ġsuffering Ġfrom Ġpsychological Ġproblems , Ġit Ġis Ġimportant Ġthat Ġtherapists Ġdo Ġnot Ġignore Ġthis Ġpivotal Ġsource Ġof Ġinformation Ġwhich Ġcan Ġprovide Ġdeep Ġinsights Ġinto Ġtheir Ġpatients ' Ġmental Ġconditions . </s> Accept ance Ġof Ġon - line Ġsupport Ġgroups Ġ( OS G ) Ġby ĠMental ĠHealth ĠProfession als Ġis Ġstill Ġnot Ġestablished ĠB IB REF 4 . ĠSince ĠOS G Ġcan Ġhave Ġdouble - edged Ġeffects Ġon Ġpatients Ġand Ġthe Ġpresence Ġof Ġprofessionals Ġis Ġoften Ġlimited , Ġwe Ġargue Ġthat Ġtheir Ġproperties Ġshould Ġbe Ġfurther Ġstudied . ĠAccording Ġto ĠB IB REF 5 ĠOS G Ġeffectiveness Ġis Ġhard Ġto Ġassess , Ġwhile Ġsome Ġstudies Ġshowed ĠOS G 's Ġpotential Ġto Ġchange Ġparticipants ' Ġattitudes , Ġno Ġsuch Ġeffect Ġwas Ġobserved Ġin Ġother Ġstudies Ġ( see ĠRelated ĠWork ĠSection Ġfor Ġmore Ġdetails ). ĠFurthermore Ġthe Ġscope Ġof Ġprevious Ġwork Ġon Ġanalysis Ġof Ġusers ' Ġbehaviour Ġin ĠOS G Ġhas Ġbeen Ġlimited Ġby Ġthe Ġfact Ġthat Ġthey Ġrelied Ġon Ġexpert Ġannotation Ġof Ġposts Ġand Ġcomments ĠB IB REF 6 . </s> We Ġpresent Ġa Ġnovel Ġapproach Ġfor Ġautomatically Ġanalys ing Ġonline Ġconversations Ġfor Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors Ġof Ġgroup Ġtherapy Ġdefined Ġby ĠB IB REF 7 Ġas ĠâĢ ľ the Ġactual Ġmechanisms Ġof Ġeffect ing Ġchange Ġin Ġthe Ġpatient âĢ Ŀ . ĠThe Ġauthors Ġhave Ġidentified Ġ11 Ġtherapeutic Ġfactors Ġin Ġgroup Ġtherapy : ĠUnivers ality , ĠAlt ru ism , ĠInst illation Ġof ĠHope , ĠGuid ance , ĠIm part ing Ġinformation , ĠDevelop ing Ġsocial Ġskills , ĠInter personal Ġlearning , ĠCo hesion , ĠCath ars is , ĠEx ist ential Ġfactors , ĠIm itative Ġbehavior Ġand ĠCorrect ive Ġrecap it ulation Ġof Ġfamily Ġof Ġorigin Ġissues . ĠIn Ġthis Ġpaper , Ġwe Ġfocus Ġon Ġ3 Ġtherapeutic Ġfactors : ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope Ġ( listed Ġbelow ), Ġas Ġwe Ġbelieve Ġthat Ġthese Ġcan Ġbe Ġapprox imated Ġby Ġusing Ġestablished ĠN LP Ġtechniques Ġ( e . g . ĠSent iment ĠAnalysis , ĠDialogue ĠAct Ġtagging Ġetc .). </s> Un ivers ality : Ġthe Ġdis conf irmation Ġof Ġa Ġuser 's Ġfeelings Ġof Ġuniqueness Ġof Ġtheir Ġmental Ġhealth Ġcondition . </s> Alt ru ism : Ġothers Ġoffer Ġsupport , Ġreass urance , Ġsuggestions Ġand Ġinsight . </s> Inst illation Ġof ĠHope : Ġinspiration Ġprovided Ġto Ġparticipants Ġby Ġtheir Ġpeers . </s> The Ġselected Ġtherapeutic Ġfactors Ġare Ġanalysed Ġin Ġterms Ġof Ġill oc ution ary Ġforce Ġand Ġattitude . ĠDue Ġto Ġthe Ġmulti - party Ġand Ġasynchronous Ġnature Ġof Ġon - line Ġsocial Ġmedia Ġconversations , Ġprior Ġto Ġthe Ġanalysis , Ġwe Ġextract Ġconversation Ġthreads Ġamong Ġusers ĠâĢĵ Ġan Ġessential Ġprerequisite Ġfor Ġany Ġkind Ġof Ġhigher - level Ġdialogue Ġanalysis ĠB IB REF 10 . ĠAfterwards , Ġthe Ġill oc ution ary Ġforce Ġis Ġidentified Ġusing ĠDialogue ĠAct Ġtagging , Ġwhereas Ġthe Ġattitude Ġby Ġusing ĠSent iment ĠAnalysis . ĠThe Ġquantitative Ġanalysis Ġis Ġthen Ġperformed Ġon Ġthese Ġprocessed Ġconversations . </s> Ide ally , Ġthe Ġanalysis Ġwould Ġrequire Ġexperts Ġto Ġannot ate Ġeach Ġpost Ġand Ġcomment Ġon Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors . ĠHowever , Ġdue Ġto Ġtime Ġand Ġcost Ġdemands Ġof Ġthis Ġtask , Ġit Ġis Ġfeasible Ġto Ġanalyse Ġonly Ġa Ġsmall Ġfraction Ġof Ġthe Ġavailable Ġdata . ĠCompared Ġto Ġprevious Ġstudies Ġ( e . g . ĠB IB REF 6 ) Ġthat Ġanalysed Ġfew Ġtens Ġof Ġconversations Ġand Ġseveral Ġthousand Ġlines Ġof Ġchat ; Ġusing Ġthe Ġproposed Ġapproach ĠâĢĵ Ġapplication Ġof ĠDialogue ĠActs Ġand ĠSent iment ĠAnalysis ĠâĢĵ Ġwe Ġwere Ġable Ġto Ġautomatically Ġanalyse Ġapproximately Ġ300 Ġthousands Ġconversations Ġ( rough ly Ġ1 . 5 Ġmillion Ġcomments ). ĠThe Ġrest Ġof Ġthe Ġpaper Ġis Ġstructured Ġas Ġfollows . ĠIn ĠSection Ġ2 Ġwe Ġintroduce Ġrelated Ġwork . ĠNext , Ġin ĠSection Ġ3 Ġwe Ġdescribe Ġthe Ġpre - processing Ġpipeline Ġand Ġthe Ġmethodology Ġto Ġperform Ġthread Ġextraction Ġon Ġasynchronous Ġmulti - party Ġconversations . ĠIn ĠSection Ġ4 Ġwe Ġprovide Ġthe Ġdescribe Ġthe Ġfinal Ġdataset Ġused Ġfor Ġthe Ġanalysis , Ġand Ġin ĠSection Ġ5 Ġwe Ġpresent Ġthe Ġresults Ġof Ġour Ġanalysis . ĠFinally , Ġin ĠSection Ġ6 Ġwe Ġprovide Ġconcluding Ġremarks Ġand Ġfuture Ġresearch Ġdirections . </s> Related ĠWork </s> On - line Ġsupport Ġgroups Ġhave Ġbeen Ġanalyzed Ġfor Ġvarious Ġfactors Ġbefore . ĠFor Ġinstance , ĠB IB REF 11 Ġanalysed Ġstress Ġreduction Ġin Ġon - line Ġsupport Ġgroup Ġchat - rooms , Ġand Ġthe Ġeffects Ġof Ġon - line Ġsocial Ġinteractions . ĠSuch Ġstudies Ġmostly Ġrelied Ġon Ġquestion naires Ġand Ġwere Ġbased Ġon Ġa Ġsmall Ġnumber Ġof Ġusers . ĠNevertheless , Ġin ĠB IB REF 11 , Ġthe Ġauthor Ġshowed Ġthat Ġsocial Ġsupport Ġfacilitates Ġcoping Ġwith Ġdistress , Ġimproves Ġmood Ġand Ġexped ites Ġrecovery Ġfrom Ġit . ĠThese Ġfindings Ġhighlight Ġthat , Ġoverall , Ġon - line Ġdiscussion Ġboards Ġappear Ġto Ġbe Ġtherapeutic Ġand Ġconstructive Ġfor Ġindividuals Ġsuffering Ġalcohol - abuse . </s> Application Ġof ĠN LP Ġto Ġthe Ġanalysis Ġof Ġmental Ġhealth - related Ġconversation Ġhas Ġbeen Ġstudied Ġas Ġwell Ġ( e . g . ĠB IB REF 12 , ĠB IB REF 13 ). ĠB IB REF 6 Ġapplied Ġsentiment - analysis Ġcombined Ġwith Ġextensive Ġturn - level Ġannotation Ġto Ġinvestigate Ġstress Ġreduction Ġin Ġon - line Ġsupport Ġgroup Ġchat - rooms , Ġshowing Ġthat Ġsentiment - analysis Ġis Ġa Ġgood Ġpredictor Ġof Ġentrance Ġstress Ġlevel . ĠFurthermore , Ġsimilar Ġto Ġour Ġsetting , Ġthey Ġapplied Ġautomatic Ġthread - ext raction Ġto Ġdetermine Ġconversation Ġthreads . </s> BI B REF 14 Ġhave Ġshown Ġthat Ġon - line Ġsupport Ġgroup Ġtherapy Ġincreased Ġthe Ġquality Ġof Ġlife Ġof Ġpatients Ġwith Ġmetast atic Ġbreast Ġcancer . ĠSince Ġmany Ġoriginal Ġposters Ġreported Ġthe Ġbenefits Ġof Ġgroup Ġtherapy Ġon Ġpatients ĠB IB REF 15 , ĠB IB REF 2 , ĠB IB REF 16 , ĠB IB REF 17 , ĠB IB REF 18 , ĠB IB REF 7 , Ġwe Ġevaluate Ġthe Ġeffect Ġof Ġthe Ġuser Ġinteraction Ġusing Ġsentiment Ġscores Ġof Ġcomments Ġin Ġon - line Ġsupport Ġgroups . </s> According Ġto ĠB IB REF 6 , Ġusers Ġwith Ġhigh Ġincoming Ġstress Ġtend Ġto Ġrequest Ġless Ġinformation Ġfrom Ġothers , Ġas Ġa Ġpercentage Ġof Ġtheir Ġtime , Ġand Ġshare Ġmuch Ġmore Ġinformation , Ġin Ġabsolute Ġterms . ĠIn Ġaddition , Ġhigh Ġinformation Ġsharing Ġhas Ġbeen Ġshown Ġto Ġbe Ġa Ġgood Ġpredictor Ġof Ġstress Ġreduction Ġat Ġthe Ġend Ġof Ġthe Ġchat ĠB IB REF 6 . ĠRegarding Ġinformation Ġsharing , Ġwe Ġrely Ġon ĠDialogue ĠActs ĠB IB REF 19 Ġto Ġmodel Ġthe Ġspeaker 's Ġintention Ġin Ġproducing Ġan Ġutter ance . ĠIn Ġparticular , Ġwe Ġare Ġinterested Ġin ĠDialogue ĠAct Ġlabel Ġthat Ġis Ġdefined Ġto Ġrepresent Ġdescriptive , Ġnarrative , Ġor Ġpersonal Ġinformation ĠâĢĵ Ġthe Ġstatement . </s> Dialogue ĠActs Ġhave Ġbeen Ġapplied Ġto Ġthe Ġanalysis Ġof Ġspoken ĠB IB REF 20 , ĠB IB REF 21 Ġas Ġwell Ġas Ġon - line Ġwritten Ġsynchron ous Ġconversations ĠB IB REF 22 . ĠWe Ġapply ĠDialogue ĠAct Ġtag Ġset Ġdefined Ġin ĠB IB REF 22 Ġto Ġthe Ġanalysis Ġof Ġour Ġon - line Ġasynchronous Ġconversations . ĠWe Ġargue Ġthat ĠDialogue ĠActs Ġcan Ġbe Ġused Ġto Ġanalyse Ġuser Ġbehaviour Ġin Ġsocial Ġmedia Ġand Ġverify Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors . </s> Method ology </s> We Ġselect Ġthe Ġthree Ġtherapeutic Ġfactors ĠâĢĵ ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope ĠâĢĵ Ġthat Ġcan Ġbe Ġbest Ġapprox imated Ġusing ĠN LP Ġtechniques : ĠSent iment ĠAnalysis Ġand ĠDialogue ĠAct Ġtagging . ĠWe Ġdiscuss Ġeach Ġone Ġof Ġthe Ġselected Ġtherapeutic Ġfactors Ġand Ġthe Ġidentified Ġnecessary Ġconditions . ĠThe Ġlisted Ġconditions , Ġhowever , Ġare Ġnot Ġsufficient Ġto Ġattribute Ġthe Ġpresence Ġof Ġa Ġtherapeutic Ġfactor Ġwith Ġhigh Ġconfidence , Ġwhich Ġonly Ġcan Ġbe Ġobtained Ġusing Ġexpert Ġannotation . ĠOur Ġanalysis Ġfocuses Ġon Ġthe Ġstructure Ġof Ġconversations ; Ġthough Ġcontent Ġplays Ġan Ġimportant Ġrole Ġas Ġwell . </s> Un ivers ality Ġconsists Ġin Ġthe Ġdis conf irmation Ġof Ġpatients ' Ġbelief Ġof Ġuniqueness Ġof Ġtheir Ġdisease . ĠThis Ġtherapeutic Ġfactor Ġis Ġshown Ġto Ġbe Ġa Ġpowerful Ġsource Ġof Ġrelief Ġfor Ġthe Ġpatient , Ġaccording Ġto ĠB IB REF 7 . ĠFrom Ġthis Ġdefinition , Ġwe Ġcan Ġdraw Ġthe Ġfollowing Ġconditions Ġthat Ġare Ġapplicable Ġto Ġour Ġenvironment : </s> improve ment Ġof Ġoriginal Ġposter 's Ġsentiment : Ġwe Ġhypothes ize Ġthat Ġthe Ġdiscovery Ġthat Ġother Ġpeople Ġpassed Ġthrough Ġsimilar Ġissues Ġleads Ġto Ġa Ġhigher Ġsentiment Ġscore ; </s> posts Ġcontaining Ġnegative Ġpersonal Ġexperiences : Ġto Ġdis conf irm Ġthe Ġbelief Ġof Ġuniqueness Ġusers Ġhave Ġto Ġshare Ġtheir Ġstory ; </s> comments Ġcontaining Ġnegative Ġstatements : Ġto Ġdis conf irm Ġthe Ġpatient 's Ġfeelings Ġof Ġuniqueness , Ġthe Ġcommenting Ġuser Ġmust Ġtell Ġa Ġsimilar Ġnegative Ġpersonal Ġexperience . ĠThis Ġcondition Ġrequires Ġtwo Ġsub - cond itions : Ġhigh Ġpresence Ġof Ġstatements Ġin Ġcomments Ġand Ġthe Ġpresence Ġof Ġnegative Ġcomments Ġrep lying Ġto Ġnegative Ġposts . </s> Inst illation Ġof ĠHope Ġis Ġbased Ġon Ġinspiration Ġprovided Ġto Ġparticipants Ġby Ġtheir Ġpeers . ĠThrough Ġthe Ġinspiration Ġprovided Ġby Ġtheir Ġpeers , Ġpatients Ġcan Ġincrease Ġtheir Ġexpectation Ġon Ġthe Ġtherapy Ġoutcome . ĠB IB REF 7 Ġin Ġseveral Ġstudies Ġhave Ġdemonstrated Ġthat Ġa Ġhigh Ġexpectation Ġof Ġhelp Ġbefore Ġthe Ġstart Ġof Ġa Ġtherapy Ġis Ġsignificantly Ġcorrelated Ġwith Ġa Ġpositive Ġtherapy Ġoutcome . ĠThe Ġauthor Ġstates Ġthat Ġmany Ġpatients Ġpointed Ġout Ġthe Ġimportance Ġof Ġhaving Ġobserved Ġthe Ġimprovement Ġof Ġothers . ĠTherefore , Ġthe Ġthree Ġmain Ġconditions Ġare Ġthe Ġfollowing : </s> improve ment Ġof Ġoriginal Ġposter 's Ġsentiment : Ġwe Ġhypothes ize Ġthat Ġinst illation Ġof Ġhope Ġleads Ġto Ġa Ġhigher Ġsentiment Ġscore ; </s> posts Ġcontaining Ġnegative Ġpersonal Ġexperiences : Ġhope Ġcan Ġbe Ġinst illed Ġin Ġsomeone Ġwho Ġshares Ġa Ġnegative Ġpersonal Ġexperience ; </s> comments Ġcontaining Ġpositive Ġpersonal Ġexperiences : Ġin Ġorder Ġto Ġinst ill Ġhope , Ġcommenting Ġposters Ġmust Ġshow Ġto Ġoriginal Ġposters Ġan Ġoverall Ġpositive Ġpersonal Ġexperience . ĠTo Ġdetect Ġpositive Ġpersonal Ġexperience , Ġwe Ġrequire Ġthe Ġpresence Ġof Ġstatements Ġin Ġcomments Ġand Ġa Ġpositive Ġsentiment Ġof Ġcomments Ġrep lying Ġto Ġnegative Ġposts . </s> Alt ru ism Ġconsists Ġof Ġpeers Ġoffering Ġsupport , Ġreass urance , Ġsuggestions Ġand Ġinsight , Ġsince Ġthey Ġshare Ġsimilar Ġproblems Ġwith Ġone Ġanother ĠB IB REF 7 . ĠThe Ġexperience Ġof Ġfinding Ġthat Ġa Ġpatient Ġcan Ġbe Ġof Ġvalue Ġto Ġothers Ġis Ġrefreshing Ġand Ġboosts Ġself - esteem ĠB IB REF 7 . ĠHowever , Ġin Ġthe Ġcurrent Ġstudy Ġwe Ġfocus Ġon Ġtesting Ġwhether Ġcommenting Ġposters Ġare Ġaltru ists Ġor Ġnot . ĠWe Ġdo Ġnot Ġtest Ġwhether Ġthe Ġaltru istic Ġbehavior Ġleads Ġto Ġan Ġimprovement Ġon Ġthe Ġaltru ist Ġitself . ĠFor Ġthese Ġreasons , Ġwe Ġdefine Ġthree Ġmain Ġconditions : </s> improve ment Ġof Ġoriginal Ġposter 's Ġsentiment : Ġwe Ġhypothes ize Ġthat Ġsupportive Ġand Ġreassuring Ġstatements Ġimprove Ġthe Ġsentiment Ġscore Ġof Ġthe Ġoriginal Ġposter ; </s> posts Ġcontains Ġnegative Ġpersonal Ġexperiences : Ġusers Ġoffer Ġsupport , Ġreass urance Ġand Ġsuggestion Ġwhen Ġfacing Ġa Ġnegative Ġpersonal Ġexperience Ġof Ġthe Ġoriginal Ġposter ; </s> comments Ġcontaining Ġpositive Ġstatements : Ġeither Ġsupportive Ġor Ġreassuring Ġstatements Ġshow Ġby Ġdefinition Ġa Ġpositive Ġintended Ġemotional Ġcommunication . ĠThus Ġcomments Ġto Ġthe Ġpost Ġshould Ġconsist Ġof Ġpositive Ġsentiment Ġstatements . </s> C onsequ ently , Ġa Ġconversation Ġcontaining Ġthe Ġaforementioned Ġtherapeutic Ġfactors Ġshould Ġsatisfy Ġthe Ġfollowing Ġconditions Ġin Ġterms Ġof ĠN LP : ĠSent iment ĠAnalysis Ġand ĠDialogue ĠActs . </s> original Ġposters Ġhave Ġa Ġhigher Ġsentiment Ġscore Ġat Ġthe Ġend Ġof Ġthe Ġthread Ġthan Ġat Ġthe Ġbeginning ; </s> the Ġoriginal Ġpost Ġconsists Ġmostly Ġof Ġpolar ised Ġstatements ; </s> the Ġpresence Ġof Ġa Ġsignificant Ġamount Ġof Ġstatements Ġin Ġcomments , Ġsince Ġboth Ġsupport Ġand Ġsharing Ġsimilar Ġnegative Ġexperiences Ġcan Ġbe Ġrepresented Ġas Ġstatements ; </s> both Ġnegative Ġand Ġpositive Ġstatements Ġin Ġcomments Ġlead Ġto Ġhigher Ġfinal Ġsentiment Ġscore Ġof Ġthe Ġoriginal Ġposter . </s> Dat as ets </s> We Ġverify Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors Ġin Ġtwo Ġsocial Ġmedia Ġdatasets : ĠOS G Ġand ĠTwitter . ĠThe Ġfirst Ġdataset Ġis Ġcrawled Ġfrom Ġan Ġon - line Ġsupport Ġgroups Ġwebsite , Ġand Ġthe Ġsecond Ġdataset Ġconsists Ġof Ġa Ġsmall Ġsample Ġof ĠTwitter Ġconversation Ġthreads . ĠSince Ġthe Ġformer Ġconsists Ġof Ġmulti - thread ed Ġconversations , Ġwe Ġapply Ġa Ġpre - processing Ġto Ġextract Ġconversation Ġthreads Ġto Ġprovide Ġa Ġfair Ġcomparison Ġwith Ġthe ĠTwitter Ġdataset . ĠAn Ġexample Ġconversation Ġfrom Ġeach Ġdata Ġsource Ġis Ġpresented Ġin ĠFigure ĠFIG REF 19 . </s> Dat as ets Ġ: :: ĠTwitter </s> We Ġhave Ġdownloaded Ġ1 , 8 73 ĠTwitter Ġconversation Ġthreads , Ġroughly Ġ14 k Ġtweets , Ġfrom Ġa Ġpublicly Ġavailable Ġresource Ġthat Ġwere Ġpreviously Ġpre - process ed Ġand Ġhave Ġconversation Ġthreads Ġextracted . ĠA Ġconversation Ġin Ġthe Ġdataset Ġconsists Ġof Ġat Ġleast Ġ4 Ġtweets . ĠEven Ġthough , Ġaccording Ġto ĠB IB REF 23 , ĠTwitter Ġis Ġbroadly Ġapplicable Ġto Ġpublic Ġhealth Ġresearch , Ġour Ġexpectation Ġis Ġthat Ġit Ġcontains Ġless Ġtherapeutic Ġconversations Ġin Ġcomparison Ġto Ġspecialized Ġon - line Ġsupport Ġforums . </s> Dat as ets Ġ: :: ĠOS G </s> Our Ġdata Ġhas Ġbeen Ġdeveloped Ġby Ġcrawling Ġand Ġpre - processing Ġan ĠOS G Ġweb Ġforum . ĠThe Ġforum Ġhas Ġa Ġgreat Ġvariety Ġof Ġdifferent Ġgroups Ġsuch Ġas Ġdepression , Ġanxiety , Ġstress , Ġrelationship , Ġcancer , Ġsexually Ġtransmitted Ġdiseases , Ġetc . ĠEach Ġconversation Ġstarts Ġwith Ġone Ġpost Ġand Ġcan Ġcontain Ġmultiple Ġcomments . ĠEach Ġpost Ġor Ġcomment Ġis Ġrepresented Ġby Ġa Ġposter , Ġa Ġtimestamp , Ġa Ġlist Ġof Ġusers Ġit Ġis Ġreferencing Ġto , Ġthread Ġid , Ġa Ġcomment Ġid Ġand Ġa Ġconversation Ġid . ĠThe Ġthread Ġid Ġis Ġthe Ġsame Ġfor Ġcomments Ġrep lying Ġto Ġeach Ġother , Ġotherwise Ġit Ġis Ġdifferent . ĠThe Ġthread Ġid Ġis Ġincreasing Ġwith Ġtime . ĠThus , Ġit Ġprovides Ġordering Ġamong Ġthreads ; Ġwhereas Ġthe Ġtimestamp Ġprovides Ġordering Ġin Ġthe Ġthread . </s> Each Ġconversation Ġcan Ġbelong Ġto Ġmultiple Ġgroups . ĠConsequently , Ġthe Ġdataset Ġneeds Ġto Ġbe Ġprocessed Ġto Ġremove Ġdupl icates . ĠThe Ġdataset Ġresulting Ġafter Ġde - du pl ication Ġcontains Ġ295 Ġthousand Ġconversations , Ġeach Ġconversation Ġcontains Ġon Ġaverage Ġ6 Ġcomments . ĠIn Ġtotal , Ġthere Ġare Ġ1 . 5 Ġmillion Ġcomments . ĠSince Ġthe Ġcreated Ġdataset Ġis Ġmulti - thread ed , Ġwe Ġneed Ġto Ġextract Ġconversation Ġthreads , Ġto Ġeliminate Ġpaths Ġnot Ġrelevant Ġto Ġthe Ġoriginal Ġpost . </s> Dat as ets Ġ: :: ĠOS G Ġ: :: ĠConversation ĠThread ĠExt raction </s> The Ġthread Ġextraction Ġalgorithm Ġis Ġhe uristic - based Ġand Ġconsists Ġof Ġtwo Ġsteps : Ġ( 1 ) Ġcreation Ġof Ġa Ġtree , Ġbased Ġon Ġa Ġpost Ġwritten Ġby Ġa Ġuser Ġand Ġthe Ġrelated Ġcomments Ġand Ġ( 2 ) Ġtransformation Ġof Ġthe Ġtree Ġinto Ġa Ġlist Ġof Ġthreads . </s> The Ġtree Ġcreation Ġis Ġan Ġextension Ġof Ġthe Ġapproach Ġof ĠB IB REF 24 , Ġwhere Ġfirst Ġa Ġgraph Ġof Ġconversation Ġis Ġconstructed . ĠIn Ġthe Ġapproach , Ġdirect Ġreplies Ġto Ġa Ġpost Ġare Ġattached Ġto Ġthe Ġfirst Ġnesting Ġlevel Ġand Ġsubsequent Ġcomments Ġto Ġincreasing Ġnesting Ġlevels . ĠIn Ġour Ġapproach , Ġwe Ġalso Ġexploit Ġcomments ' Ġfeatures . </s> The Ġtree Ġcreation Ġis Ġperformed Ġwithout Ġprocessing Ġthe Ġcontent Ġof Ġcomments , Ġwhich Ġallows Ġus Ġto Ġprocess Ġposts Ġand Ġcomments Ġof Ġany Ġlength Ġefficiently . ĠThe Ġhe uristic Ġused Ġin Ġthe Ġprocess Ġis Ġbased Ġon Ġthree Ġsimpl ifying Ġassumptions : </s> Unless Ġthere Ġis Ġa Ġspecific Ġreference Ġto Ġanother Ġcomment Ġor Ġa Ġuser , Ġcomments Ġare Ġattached Ġto Ġthe Ġoriginal Ġpost . </s> When Ġrep lying , Ġthe Ġcommenting Ġposter Ġis Ġalways Ġrep lying Ġto Ġthe Ġoriginal Ġpost Ġor Ġsome Ġother Ġcomment . ĠUnless Ġspecified Ġotherwise , Ġit Ġis Ġassumed Ġthat Ġit Ġis Ġa Ġresponse Ġto Ġthe Ġprevious Ġ( in Ġtime ) Ġpost / comment . </s> Sub sequent Ġcomments Ġby Ġthe Ġsame Ġposter Ġare Ġpart Ġof Ġthe Ġsame Ġthread . </s> To Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġthread Ġextraction Ġalgorithm , Ġ2 Ġannot ators Ġhave Ġmanually Ġconstructed Ġthe Ġtrees Ġfor Ġ100 Ġconversations . ĠThe Ġperformance Ġof Ġthe Ġalgorithm Ġon Ġthis Ġset Ġof Ġ100 Ġconversations Ġis Ġevaluated Ġusing Ġaccuracy Ġand Ġstandard ĠInformation ĠRet ri eval Ġevaluation Ġmetrics Ġof Ġprecision , Ġrecall , Ġand ĠF $ _ 1 $ Ġmeasure . ĠThe Ġresults Ġare Ġreported Ġin ĠTable ĠT AB REF 28 Ġtogether Ġwith Ġrandom Ġand Ġmajority Ġbas elines . ĠThe Ġturn - level Ġpercent Ġagreement Ġbetween Ġthe Ġ2 Ġannot ators Ġis Ġ97 . 99 % Ġand ĠCohen 's ĠKappa ĠCo efficient Ġis Ġ83 . 80 %. </s> Dat as ets Ġ: :: ĠData ĠRepresent ation </s> For Ġboth Ġdata Ġsources , ĠTwitter Ġand ĠOS G Ġwith Ġextracted Ġthreads , Ġposts Ġand Ġcomments Ġare Ġtoken ized Ġand Ġsentence Ġsplit . ĠEach Ġsentence Ġis Ġpassed Ġthrough ĠSent iment ĠAnalysis Ġand ĠDialogue ĠAct Ġtagging . ĠSince Ġa Ġpost Ġor Ġa Ġcomment Ġcan Ġcontain Ġmultiple Ġsentences , Ġtherefore Ġmultiple ĠDialogue ĠActs , Ġit Ġis Ġrepresented Ġas Ġas Ġa Ġone - hot Ġencoding , Ġwhere Ġeach Ġposition Ġrepresents Ġa ĠDialogue ĠAct . </s> For ĠSent iment ĠAnalysis Ġwe Ġuse Ġa Ġlex icon - based Ġsentiment Ġanalys er Ġintroduced Ġby ĠB IB REF 25 . ĠFor ĠDialogue ĠAct Ġtagging , Ġon Ġthe Ġother Ġhand , Ġwe Ġmake Ġuse Ġof Ġa Ġmodel Ġtrained Ġon ĠN PS Chat Ġcorpus ĠB IB REF 22 Ġfollowing Ġthe Ġapproach Ġof ĠB IB REF 26 . </s> Analysis </s> As Ġwe Ġmentioned Ġin ĠSection Ġ3 , Ġthe Ġpresence Ġof Ġeach Ġof Ġthe Ġtherapeutic Ġconditions Ġunder Ġanalysis Ġis Ġa Ġnecessary Ġfor Ġa Ġconversation Ġto Ġbe Ġconsidered Ġto Ġhave Ġtherapeutic Ġfactors . ĠIn Ġthis Ġsection Ġwe Ġpresent Ġthe Ġresults Ġof Ġour Ġanalysis Ġwith Ġrespect Ġto Ġthese Ġconditions . </s> Analysis Ġ: :: ĠChange Ġin ĠSent iment Ġscore Ġof ĠOriginal ĠPost ers </s> The Ġfirst Ġcondition Ġwhich Ġwe Ġtest Ġis Ġthe Ġsentiment Ġchange Ġin Ġconversation Ġthreads , Ġcomparing Ġthe Ġinitial Ġand Ġfinal Ġsentiment Ġscores Ġ( i . e . Ġposts ' Ġscores ) Ġof Ġthe Ġoriginal Ġposter . ĠThe Ġresults Ġof Ġthe Ġanalysis Ġare Ġpresented Ġin ĠFigure ĠFIG REF 33 . ĠIn Ġthe Ġfigure Ġwe Ġcan Ġobserve Ġthat Ġthe Ġdistribution Ġof Ġthe Ġsentiment Ġchange Ġin Ġthe Ġtwo Ġdatasets Ġis Ġdifferent . ĠWhile Ġin ĠTwitter Ġthe Ġamount Ġof Ġconversations Ġthat Ġlead Ġto Ġthe Ġincrease Ġof Ġsentiment Ġscore Ġis Ġroughly Ġequal Ġto Ġthe Ġamount Ġof Ġconversations Ġthat Ġlead Ġto Ġthe Ġdecrease Ġof Ġsentiment Ġscore ; Ġthe Ġsituation Ġis Ġdifferent Ġfor ĠOS G . ĠIn ĠOS G , Ġthe Ġamount Ġof Ġconversations Ġthat Ġlead Ġto Ġthe Ġincrease Ġof Ġsentiment Ġscore Ġis Ġconsiderably Ġhigher . </s> Figure ĠFIG REF 34 Ġprovides Ġa Ġmore Ġfine Ġgr ained Ġanalysis , Ġwhere Ġwe Ġadditionally Ġanalyse Ġthe Ġsentiment Ġchange Ġin Ġnominal Ġpol arity Ġterms ĠâĢĵ Ġnegative Ġand Ġpositive . ĠIn ĠOS G , Ġthe Ġnumber Ġof Ġusers Ġthat Ġchanged Ġpol arity Ġfrom Ġnegative Ġto Ġpositive Ġis Ġmore Ġthan Ġthe Ġdouble Ġof Ġthe Ġusers Ġthat Ġhave Ġchanged Ġthe Ġpol arity Ġfrom Ġpositive Ġto Ġnegative . ĠIn ĠTwitter , Ġon Ġthe Ġother Ġhand , Ġthe Ġusers Ġmostly Ġchanged Ġpol arity Ġfrom Ġpositive Ġto Ġnegative . ĠResults Ġof Ġthe Ġanalysis Ġsuggest Ġthat Ġin ĠOS G Ġ, Ġsentiment Ġincreases Ġand Ġusers Ġtend Ġto Ġchange Ġpol arity Ġfrom Ġnegative Ġto Ġpositive , Ġwhereas Ġin ĠTwitter Ġsentiment Ġtends Ġto Ġdecrease . ĠVer ification Ġof Ġthis Ġcondition Ġalone Ġindicates Ġthat Ġthe Ġratio Ġof Ġpotentially Ġtherapeutic Ġconversations Ġin ĠTwitter Ġis Ġlower . </s> Analysis Ġ: :: ĠStructure Ġof ĠPosts Ġand ĠComments </s> Table ĠT AB REF 36 Ġpresents Ġthe Ġdistribution Ġof Ġautomatically Ġpredicted Ġper - sent ence ĠDialogue ĠActs Ġin Ġthe Ġdatasets . ĠThe Ġmost Ġfrequent Ġtag Ġis Ġstatement Ġin Ġboth . ĠIn ĠTable ĠT AB REF 37 , Ġon Ġthe Ġother Ġhand , Ġwe Ġpresent Ġthe Ġdistribution Ġof Ġpost Ġand Ġcomment Ġstructures Ġin Ġterms Ġof Ġautomatically Ġpredicted ĠDialogue ĠAct Ġtags . ĠThe Ġstructure Ġis Ġan Ġun ordered Ġset Ġof Ġtags Ġin Ġthe Ġpost Ġor Ġcomment . ĠFrom Ġthe Ġtable Ġwe Ġcan Ġobserve Ġthat Ġthe Ġdistribution Ġof Ġtag Ġsets Ġis Ġsimilar Ġbetween Ġposts Ġand Ġcomments . ĠIn Ġboth Ġcases Ġthe Ġmost Ġcommon Ġset Ġis Ġstatement Ġonly . ĠHowever , Ġconversations Ġcontaining Ġonly Ġstatement , Ġemphasis Ġor Ġquestion Ġposts Ġand Ġcomments Ġpredominantly Ġappear Ġin ĠTwitter . ĠWhich Ġis Ġexpected Ġdue Ġto Ġthe Ġshorter Ġlength Ġof ĠTwitter Ġposts Ġand Ġcomments . </s> We Ġcan Ġalso Ġobserve Ġthat Ġthe Ġoriginal Ġposters Ġtend Ġto Ġask Ġmore Ġquestions Ġthan Ġthe Ġcommenting Ġposters ĠâĢĵ Ġ19 . 83 % Ġfor Ġposts Ġvs . Ġ11 . 21 % Ġfor Ġcomments Ġ( sum med ). ĠThis Ġsuggests Ġthat Ġthe Ġoriginal Ġposters Ġfrequently Ġask Ġeither Ġfor Ġsuggestion Ġor Ġconfirmation Ġof Ġtheir Ġpoints Ġof Ġview Ġor Ġtheir Ġdis conf irmation . ĠHowever , Ġthe Ġhigh Ġpresence Ġof Ġpersonal Ġexperiences Ġis Ġsupported Ġby Ġthe Ġhigh Ġnumber Ġof Ġposts Ġcontaining Ġonly Ġstatements . </s> High Ġnumber Ġof Ġstatement Ġtags Ġin Ġcomments Ġsuggests Ġthat Ġusers Ġreply Ġeither Ġwith Ġsupporting Ġor Ġem pathic Ġstatements Ġor Ġpersonal Ġexperience . ĠHowever , Ġ6 . 39 % Ġof Ġcomments Ġcontain Ġaccept Ġand Ġreject Ġtags , Ġwhich Ġmark Ġthe Ġdegree Ġto Ġwhich Ġa Ġspeaker Ġaccepts Ġsome Ġprevious Ġproposal , Ġplan , Ġopinion , Ġor Ġstatement ĠB IB REF 20 . ĠThe Ġdescribed ĠDialogue ĠAct Ġtags Ġare Ġoften Ġused Ġwhen Ġcommenting Ġposters Ġdiscuss Ġoriginal Ġposter 's Ġpoint Ġof Ġview . ĠFor Ġinstance , ĠâĢ ľ It 's Ġtrue . ĠI Ġfelt Ġthe Ġsame . âĢ Ŀ ĠâĢĵ Ġ{ Accept , ĠStatement } Ġor ĠâĢ ľ Well Ġno . ĠYou 're Ġnot Ġalone âĢ Ŀ ĠâĢĵ Ġ{ Re ject , ĠStatement }. ĠThe Ġdatasets Ġdiffer Ġwith Ġrespect Ġto Ġthe Ġdistribution Ġof Ġthese ĠDialogue ĠActs Ġtags , Ġthey Ġappear Ġmore Ġfrequently Ġin ĠOS G . </s> Analysis Ġ: :: ĠSent iment Ġof ĠPosts Ġand ĠComments </s> Table ĠT AB REF 39 Ġpresents Ġthe Ġdistribution Ġof Ġsentiment Ġpol arity Ġin Ġpost Ġand Ġcomment Ġstatements Ġ( i . e . Ġsentences Ġtagged Ġas Ġstatement ). ĠFor ĠOS G , Ġthe Ġpredominant Ġsentiment Ġlabel Ġof Ġstatements Ġis Ġpositive Ġand Ġit Ġis Ġthe Ġhighest Ġfor Ġboth Ġposts Ġand Ġcomments . ĠHowever , Ġthe Ġdifference Ġbetween Ġthe Ġamounts Ġof Ġpositive Ġand Ġnegative Ġstatements Ġis Ġhigher Ġfor Ġthe Ġrep lying Ġcomments Ġ( 34 . 5 % Ġvs . Ġ42 . 5 %). ĠFor ĠTwitter , Ġon Ġthe Ġother Ġhand , Ġthe Ġpredominant Ġsentiment Ġlabel Ġof Ġstatements Ġis Ġneutral Ġand Ġthe Ġpol arity Ġdistribution Ġbetween Ġposts Ġand Ġcomments Ġis Ġvery Ġclose . ĠOne Ġparticular Ġobservation Ġis Ġthat Ġthe Ġratio Ġof Ġnegative Ġstatements Ġis Ġhigher Ġin ĠOS G Ġfor Ġboth Ġposts Ġand Ġcomments Ġthan Ġin ĠTwitter , Ġwhich Ġsupports Ġthe Ġidea Ġof Ġsharing Ġnegative Ġexperiences . </s> Further Ġwe Ġanalyze Ġwhether Ġthe Ġsentiment Ġof Ġa Ġcomment Ġ( i . e . Ġthe Ġrep lying Ġuser ) Ġis Ġaffected Ġby Ġthe Ġsentiment Ġof Ġthe Ġoriginal Ġpost Ġ( i . e . Ġthe Ġuser Ġbeing Ġreplied Ġto ), Ġwhich Ġwill Ġimply Ġthat Ġthe Ġusers Ġadapt Ġtheir Ġbehaviour Ġwith Ġrespect Ġto Ġthe Ġpost 's Ġsentiment . ĠFor Ġthe Ġanalysis , Ġwe Ġsplit Ġthe Ġdatasets Ġinto Ġthree Ġbuckets Ġaccording Ġto Ġthe Ġposts ' Ġsentiment Ġscore ĠâĢĵ Ġnegative , Ġneutral , Ġor Ġpositive , Ġand Ġrepresent Ġeach Ġconversation Ġin Ġterms Ġof Ġpercentages Ġof Ġcomments Ġ( repl ies ) Ġwith Ġeach Ġsentiment Ġlabel . ĠThe Ġbuckets Ġare Ġthen Ġcompared Ġusing Ġt - test Ġfor Ġstatistically Ġsignificant Ġdifferences . </s> Table ĠT AB REF 40 Ġpresents Ġthe Ġdistribution Ġof Ġsentiment Ġlabels Ġwith Ġrespect Ġto Ġthe Ġpost 's Ġsentiment Ġscore . ĠThe Ġpatterns Ġof Ġdistribution Ġare Ġsimilar Ġacross Ġthe Ġdatasets . ĠWe Ġcan Ġobserve Ġthat Ġoverall , Ġreplies Ġtend Ġto Ġhave Ġa Ġpositive Ġsentiment , Ġwhich Ġsuggests Ġthat Ġrep lying Ġposters Ġtend Ġto Ġhave Ġa Ġpositive Ġattitude . ĠHowever , Ġthe Ġratio Ġof Ġpositive Ġcomments Ġis Ġhigher Ġfor ĠOS G Ġthan Ġfor ĠTwitter . </s> The Ġresults Ġof Ġthe ĠWelch 's Ġt - test Ġon ĠOS G Ġdata Ġreveal Ġthat Ġthere Ġare Ġstatistically Ġsignificant Ġdifferences Ġin Ġthe Ġdistribution Ġof Ġrep lying Ġcomments ' Ġsentiment Ġbetween Ġconversations Ġwith Ġpositive Ġand Ġnegative Ġstarting Ġposts . ĠA Ġpositive Ġpost Ġtends Ġto Ġget Ġsignificantly Ġmore Ġpositive Ġreplies . ĠSimilarly , Ġa Ġnegative Ġpost Ġtends Ġto Ġget Ġsignificantly Ġmore Ġnegative Ġreplies Ġ( both Ġwith Ġ$ p Ġ< Ġ0 . 01 $ ). </s> Table ĠT AB REF 41 Ġpresents Ġthe Ġdistribution Ġof Ġthe Ġsentiment Ġlabels Ġof Ġthe Ġfinal Ġtext Ġprovided Ġby Ġthe Ġoriginal Ġposter Ġwith Ġrespect Ġto Ġthe Ġsentiment Ġpol arity Ġof Ġthe Ġcomments . ĠThe Ġresults Ġindicate Ġthat ĠOS G Ġparticipants Ġare Ġmore Ġsupportive , Ġas Ġthe Ġmajority Ġof Ġconversations Ġend Ġin Ġa Ġpositive Ġfinal Ġsentiment Ġregardless Ġof Ġthe Ġsentiment Ġof Ġcomments . ĠWe Ġcan Ġalso Ġobserve Ġthat Ġnegative Ġcomments Ġin ĠOS G Ġlead Ġto Ġpositive Ġsentiment , Ġwhich Ġsupports Ġthe Ġidea Ġof Ġsharing Ġthe Ġnegative Ġexperiences , Ġthus Ġpresence Ġof Ġtherapeutic Ġfactors . ĠFor ĠTwitter , Ġon Ġthe Ġother Ġhand , Ġonly Ġpositive Ġcomments Ġlead Ġto Ġthe Ġpositive Ġfinal Ġsentiments , Ġwhereas Ġother Ġcomments Ġlead Ġpredominantly Ġto Ġneutral Ġfinal Ġsentiments . </s> Our Ġanalysis Ġin Ġterms Ġof Ġsentiment Ġand ĠDialogue ĠActs Ġsupports Ġthe Ġpresence Ġof Ġthe Ġthree Ġselected Ġtherapeutic Ġfactors ĠâĢĵ ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope ĠâĢĵ Ġin ĠOS G Ġmore Ġthan Ġin ĠTwitter . ĠThe Ġmain Ġcontributors Ġto Ġthis Ġconclusion Ġare Ġthe Ġfacts Ġthat Ġthere Ġis Ġmore Ġpositive Ġchange Ġin Ġthe Ġsentiment Ġof Ġthe Ġoriginal Ġposters Ġin ĠOS G Ġ( people Ġseeking Ġsupport ) Ġand Ġthat Ġin ĠOS G Ġeven Ġnegative Ġand Ġneutral Ġcomments Ġare Ġlikely Ġto Ġlead Ġto Ġpositive Ġchanges . </s> Conclusion </s> In Ġthis Ġwork , Ġwe Ġpropose Ġa Ġmethodology Ġto Ġautomatically Ġanalyse Ġonline Ġsocial Ġplatforms Ġfor Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors Ġ( i . e . ĠUnivers ality , ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope ). ĠWe Ġevaluate Ġour Ġapproach Ġon Ġtwo Ġon - line Ġplatforms , ĠTwitter Ġand Ġan ĠOS G Ġweb Ġforum . ĠWe Ġapply ĠN LP Ġtechniques Ġof ĠSent iment ĠAnalysis Ġand ĠDialogue ĠAct Ġtagging Ġto Ġautomatically Ġverify Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors , Ġwhich Ġallows Ġus Ġto Ġanalyse Ġlarger Ġamounts Ġof Ġconvers ational Ġdata Ġ( as Ġcompared Ġto Ġprevious Ġstudies ). </s> Our Ġanalysis Ġindicates Ġthat ĠOS G Ġconversations Ġsatisfy Ġhigher Ġnumber Ġof Ġconditions Ġapprox imating Ġtherapeutic Ġfactors Ġthan ĠTwitter Ġconversations . ĠGiven Ġthis Ġoutcome , Ġwe Ġpost ulate Ġthat Ġusers Ġwho Ġjoin Ġsupport Ġgroup Ġwebsites Ġspontaneously Ġseem Ġto Ġbenefit Ġfrom Ġit . ĠIndeed , Ġas Ġshown Ġin ĠSection ĠSEC REF 5 , Ġthe Ġoriginal Ġposters Ġwho Ġinteract Ġwith Ġothers Ġby Ġrep lying Ġto Ġcomments , Ġhave Ġbenefited Ġfrom Ġan Ġimprovement Ġof Ġtheir Ġemotional Ġstate . </s> We Ġwould Ġlike Ġto Ġre em phas ise Ġthat Ġthe Ġconditions Ġfor Ġthe Ġtherapeutic Ġfactors Ġare Ġnecessary Ġbut Ġnot Ġsufficient ; Ġsince Ġour Ġanalysis Ġfocuses Ġon Ġthe Ġstructure Ġof Ġconversations , Ġbeing Ġag nostic Ġto Ġthe Ġcontent . ĠN LP , Ġhowever , Ġallows Ġus Ġto Ġstrengthen Ġour Ġapprox im ations Ġeven Ġfurther . ĠThus , Ġthe Ġfurther Ġextension Ġof Ġour Ġwork Ġis Ġalso Ġaug mentation Ġof Ġour Ġstudy Ġwith Ġother Ġlanguage Ġanalysis Ġmetrics Ġand Ġtheir Ġcorrelation Ġwith Ġhuman Ġannotation . </s> It Ġshould Ġbe Ġnoted Ġthat Ġthe Ġproposed Ġapproach Ġis Ġan Ġapproximation Ġof Ġthe Ġtedious Ġtasks Ġof Ġannotation Ġof Ġconversations Ġby Ġexperts Ġvers ed Ġin Ġthe Ġtherapeutic Ġfactors Ġand Ġtheir Ġassociated Ġtheories . ĠEven Ġthough Ġwe Ġcan Ġuse ĠSent iment ĠAnalysis Ġto Ġdetect Ġthe Ġexistence Ġof Ġtherapeutic Ġfactors , Ġwe Ġcannot Ġdifferentiate Ġbetween ĠAlt ru ism Ġand ĠInst illation Ġof ĠHope , Ġas Ġthis Ġrequires Ġdifferentiation Ġbetween Ġemotional Ġstate Ġof Ġthe Ġuser Ġand Ġthe Ġintended Ġemotional Ġcommunication . ĠThus , Ġthe Ġnatural Ġextensions Ġof Ġthis Ġwork Ġare Ġdifferentiation Ġbetween Ġdifferent Ġtherapeutic Ġfactors Ġand Ġcomparison Ġof Ġthe Ġproposed Ġanalysis Ġto Ġthe Ġhuman Ġevaluation . </s> Although Ġwe Ġacknowledge Ġthat Ġthe Ġproposed Ġmethodology Ġdoes Ġnot Ġserve Ġas Ġa Ġreplacement Ġof Ġmanual Ġanalysis Ġof ĠOS G Ġfor Ġthe Ġpresence Ġof Ġtherapeutic Ġfactors , Ġwe Ġbelieve Ġthat Ġit Ġcould Ġfacilitate Ġand Ġsupplement Ġthis Ġprocess . ĠThe Ġmethod Ġcan Ġserve Ġas Ġa Ġtool Ġfor Ġgeneral Ġpractitioners Ġand Ġpsychologists Ġwho Ġcan Ġuse Ġit Ġas Ġan Ġadditional Ġsource Ġof Ġinformation Ġregarding Ġtheir Ġpatients âĢ Ļ Ġcondition Ġand , Ġin Ġturn , Ġoffer Ġa Ġmore Ġpersonal ised Ġsupport Ġthat Ġis Ġbetter Ġtailored Ġto Ġindividual Ġtherapeutic Ġneeds .