No
BOOLEAN
1703.04001
Introduction </s> From Ġa Ġgroup Ġof Ġsmall Ġusers Ġat Ġthe Ġtime Ġof Ġits Ġinception Ġin Ġ2009 , ĠQu ora Ġhas Ġevolved Ġin Ġthe Ġlast Ġfew Ġyears Ġinto Ġone Ġof Ġthe Ġlargest Ġcommunity Ġdriven ĠQ & A Ġsites Ġwith Ġdiverse Ġuser Ġcommunities . ĠWith Ġthe Ġhelp Ġof Ġefficient Ġcontent Ġmoderation / review Ġpolicies Ġand Ġactive Ġin - house Ġreview Ġteam , Ġefficient ĠQu ora Ġbots , Ġthis Ġsite Ġhas Ġemerged Ġinto Ġone Ġof Ġthe Ġlargest Ġand Ġreliable Ġsources Ġof ĠQ & A Ġon Ġthe ĠInternet . ĠOn ĠQu ora , Ġusers Ġcan Ġpost Ġquestions , Ġfollow Ġquestions , Ġshare Ġquestions , Ġtag Ġthem Ġwith Ġrelevant Ġtopics , Ġfollow Ġtopics , Ġfollow Ġusers Ġapart Ġfrom Ġanswering , Ġcommenting , Ġup v oting / down v oting Ġetc . ĠThe Ġintegrated Ġsocial Ġstructure Ġat Ġthe Ġbackbone Ġof Ġit Ġand Ġthe Ġtopical Ġorganization Ġof Ġits Ġrich Ġcontent Ġhave Ġmade ĠQu ora Ġunique Ġwith Ġrespect Ġto Ġother ĠQ & A Ġsites Ġlike ĠStack ĠOver flow , ĠYahoo ! ĠAnswers Ġetc . Ġand Ġthese Ġare Ġsome Ġof Ġthe Ġprime Ġreasons Ġbehind Ġits Ġpopularity Ġin Ġrecent Ġtimes . ĠQuality Ġquestion Ġposting Ġand Ġgetting Ġthem Ġanswered Ġare Ġthe Ġkey Ġobjectives Ġof Ġany ĠQ & A Ġsite . ĠIn Ġthis Ġstudy Ġwe Ġfocus Ġon Ġthe Ġanswer ability Ġof Ġquestions Ġon ĠQu ora , Ġi . e ., Ġwhether Ġa Ġposted Ġquestion Ġshall Ġeventually Ġget Ġanswered . ĠIn ĠQu ora , Ġthe Ġquestions Ġwith Ġno Ġanswers Ġare Ġreferred Ġto Ġas ĠâĢ ľ open Ġquestions âĢ Ŀ . ĠThese Ġopen Ġquestions Ġneed Ġto Ġbe Ġstudied Ġseparately Ġto Ġunderstand Ġthe Ġreason Ġbehind Ġtheir Ġnot Ġbeing Ġanswered Ġor Ġto Ġbe Ġprecise , Ġare Ġthere Ġany Ġcharacteristic Ġdifferences Ġbetween Ġ` open ' Ġquestions Ġand Ġthe Ġanswered Ġones . ĠFor Ġexample , Ġthe Ġquestion ĠâĢ ľ What Ġare Ġthe Ġmost Ġpromising Ġadvances Ġin Ġthe Ġtreatment Ġof Ġtraumatic Ġbrain Ġinjuries ? âĢ Ŀ Ġwas Ġposted Ġon ĠQu ora Ġon Ġ23 rd ĠJune , Ġ2011 Ġand Ġgot Ġits Ġfirst Ġanswer Ġafter Ġalmost Ġ2 Ġyears Ġon Ġ22 nd ĠApril , Ġ2013 . ĠThe Ġreason Ġthat Ġthis Ġquestion Ġremained Ġopen Ġso Ġlong Ġmight Ġbe Ġthe Ġhardness Ġof Ġanswering Ġit Ġand Ġthe Ġlack Ġof Ġvisibility Ġand Ġexperts Ġin Ġthe Ġdomain . ĠTherefore , Ġit Ġis Ġimportant Ġto Ġidentify Ġthe Ġopen Ġquestions Ġand Ġtake Ġmeasures Ġbased Ġon Ġthe Ġtypes Ġ- Ġpoor Ġquality Ġquestions Ġcan Ġbe Ġremoved Ġfrom ĠQu ora Ġand Ġthe Ġgood Ġquality Ġquestions Ġcan Ġbe Ġpromoted Ġso Ġthat Ġthey Ġget Ġmore Ġvisibility Ġand Ġare Ġeventually Ġrouted Ġto Ġtopical Ġexperts Ġfor Ġbetter Ġanswers . </s> Character ization Ġof Ġthe Ġquestions Ġbased Ġon Ġquestion Ġquality Ġrequires Ġexpert Ġhuman Ġinterventions Ġoften Ġjudging Ġif Ġa Ġquestion Ġwould Ġremain Ġopen Ġbased Ġon Ġfactors Ġlike Ġif Ġit Ġis Ġsubjective , Ġcontroversial , Ġopen - ended , Ġvague / im pre cise , Ġill - formed , Ġoff - topic , Ġambiguous , Ġun interesting Ġetc . ĠCollect ing Ġjudgment Ġdata Ġfor Ġthousands Ġof Ġquestion Ġposts Ġis Ġa Ġvery Ġexpensive Ġprocess . ĠTherefore , Ġsuch Ġan Ġexperiment Ġcan Ġbe Ġdone Ġonly Ġfor Ġa Ġsmall Ġset Ġof Ġquestions Ġand Ġit Ġwould Ġbe Ġpractically Ġimpossible Ġto Ġscale Ġit Ġup Ġfor Ġthe Ġentire Ġcollection Ġof Ġposts Ġon Ġthe ĠQ & A Ġsite . ĠIn Ġthis Ġwork , Ġwe Ġshow Ġthat Ġappropriate Ġquant ification Ġof Ġvarious Ġlinguistic Ġactivities Ġcan Ġnaturally Ġcorrespond Ġto Ġmany Ġof Ġthe Ġjudgment Ġfactors Ġmentioned Ġabove Ġ( see Ġtable Ġ2 Ġfor Ġa Ġcollection Ġof Ġexamples ). ĠThese Ġquantities Ġencoding Ġsuch Ġlinguistic Ġactivities Ġcan Ġbe Ġeasily Ġmeasured Ġfor Ġeach Ġquestion Ġpost Ġand Ġthus Ġhelps Ġus Ġto Ġhave Ġan Ġalternative Ġmechanism Ġto Ġcharacterize Ġthe Ġanswer ability Ġon Ġthe ĠQ & A Ġsite . </s> There Ġare Ġseveral Ġresearch Ġworks Ġdone Ġin ĠQ & A Ġfocusing Ġon Ġcontent Ġof Ġposts . ĠB IB REF 0 Ġexploit Ġcommunity Ġfeedback Ġto Ġidentify Ġhigh Ġquality Ġcontent Ġon ĠYahoo ! ĠAnswers . ĠB IB REF 1 Ġuse Ġtextual Ġfeatures Ġto Ġpredict Ġanswer Ġquality Ġon ĠYahoo ! ĠAnswers . ĠB IB REF 2 Ġ, Ġinvestigate Ġpredict ors Ġof Ġanswer Ġquality Ġthrough Ġa Ġcomparative , Ġcontrolled Ġfield Ġstudy Ġof Ġuser Ġresponses . ĠB IB REF 3 Ġstudy Ġthe Ġproblem Ġof Ġhow Ġlong Ġquestions Ġremain Ġunanswered . ĠB IB REF 4 Ġpropose Ġa Ġprediction Ġmodel Ġon Ġhow Ġmany Ġanswers Ġa Ġquestion Ġshall Ġreceive . ĠB IB REF 5 Ġanalyze Ġand Ġpredict Ġunanswered Ġquestions Ġon ĠYahoo ĠAnswers . ĠB IB REF 6 Ġstudy Ġquestion Ġquality Ġin ĠYahoo ! ĠAnswers . </s> Dat as et Ġdescription </s> We Ġobtained Ġour ĠQu ora Ġdataset ĠB IB REF 7 Ġthrough Ġweb - based Ġcraw ls Ġbetween ĠJune Ġ2014 Ġto ĠAugust Ġ2014 . ĠThis Ġcrawling Ġexercise Ġhas Ġresulted Ġin Ġthe Ġaccumulation Ġof Ġa Ġmassive ĠQ & A Ġdataset Ġspanning Ġover Ġa Ġperiod Ġof Ġover Ġfour Ġyears Ġstarting Ġfrom ĠJanuary Ġ2010 Ġto ĠMay Ġ2014 . ĠWe Ġinitiated Ġcrawling Ġwith Ġ100 Ġquestions Ġrandomly Ġselected Ġfrom Ġdifferent Ġtopics Ġso Ġthat Ġdifferent Ġgenre Ġof Ġquestions Ġcan Ġbe Ġcovered . ĠThe Ġcrawling Ġof Ġthe Ġquestions Ġfollow Ġa ĠB FS Ġpattern Ġthrough Ġthe Ġrelated Ġquestion Ġlinks . ĠWe Ġobtained Ġ8 22 , 040 Ġunique Ġquestions Ġacross Ġ80 , 253 Ġdifferent Ġtopics Ġwith Ġa Ġtotal Ġof Ġ1 , 833 , 125 Ġanswers Ġto Ġthese Ġquestions . ĠFor Ġeach Ġquestion , Ġwe Ġseparately Ġcrawl Ġtheir Ġrevision Ġlogs Ġthat Ġcontain Ġdifferent Ġtypes Ġof Ġedit Ġinformation Ġfor Ġthe Ġquestion Ġand Ġthe Ġactivity Ġlog Ġof Ġthe Ġquestion Ġask er . </s> L ingu istic Ġactivities Ġon ĠQu ora </s> In Ġthis Ġsection , Ġwe Ġidentify Ġvarious Ġlinguistic Ġactivities Ġon ĠQu ora Ġand Ġpropose Ġquant ifications Ġof Ġthe Ġlanguage Ġusage Ġpatterns Ġin Ġthis ĠQ & A Ġsite . ĠIn Ġparticular , Ġwe Ġshow Ġthat Ġthere Ġexists Ġsignificant Ġdifferences Ġin Ġthe Ġlinguistic Ġstructure Ġof Ġthe Ġopen Ġand Ġthe Ġanswered Ġquestions . ĠNote Ġthat Ġmost Ġof Ġthe Ġmeasures Ġthat Ġwe Ġdefine Ġare Ġsimple , Ġintuitive Ġand Ġcan Ġbe Ġeasily Ġobtained Ġautomatically Ġfrom Ġthe Ġdata Ġ( without Ġmanual Ġintervention ). ĠTherefore Ġthe Ġframework Ġis Ġpractical , Ġinexpensive Ġand Ġhighly Ġscalable . </s> Content Ġof Ġa Ġquestion Ġtext Ġis Ġimportant Ġto Ġattract Ġpeople Ġand Ġmake Ġthem Ġengage Ġmore Ġtoward Ġit . ĠThe Ġlinguistic Ġstructure Ġ( i . e ., Ġthe Ġusage Ġof ĠPOS Ġtags , Ġthe Ġuse Ġof ĠOut - of - V oc abulary Ġwords , Ġcharacter Ġusage Ġetc .) Ġone Ġadop ts Ġare Ġkey Ġfactors Ġfor Ġanswer ability Ġof Ġquestions . ĠWe Ġshall Ġdiscuss Ġthe Ġlinguistic Ġstructure Ġthat Ġoften Ġrepresents Ġthe Ġwriting Ġstyle Ġof Ġa Ġquestion Ġask er . </s> In Ġfig Ġ1 Ġ( a ), Ġwe Ġobserve Ġthat Ġask ers Ġof Ġopen Ġquestions Ġgenerally Ġuse Ġmore Ġno . Ġof Ġwords Ġcompared Ġto Ġanswered Ġquestions . ĠTo Ġunderstand Ġthe Ġnature Ġof Ġwords Ġ( standard ĠEnglish Ġwords Ġor Ġchat - like Ġwords Ġfrequently Ġused Ġin Ġsocial Ġmedia ) Ġused Ġin Ġthe Ġtext , Ġwe Ġcompare Ġthe Ġwords Ġwith ĠGNU ĠAs pell Ġdictionary Ġto Ġsee Ġwhether Ġthey Ġare Ġpresent Ġin Ġthe Ġdictionary Ġor Ġnot . ĠWe Ġobserve Ġthat Ġboth Ġopen Ġquestions Ġand Ġanswered Ġquestions Ġfollow Ġsimilar Ġdistribution Ġ( see Ġfig Ġ1 Ġ( b )). ĠPart - of - Spe ech Ġ( POS ) Ġtags Ġare Ġindicators Ġof Ġgram matical Ġaspects Ġof Ġtexts . ĠTo Ġobserve Ġhow Ġthe ĠPart - of - Spe ech Ġtags Ġare Ġdistributed Ġin Ġthe Ġquestion Ġtexts , Ġwe Ġdefine Ġa Ġdiversity Ġmetric . ĠWe Ġuse Ġthe Ġstandard ĠCM U ĠPOS Ġtag ger ĠB IB REF 8 Ġfor Ġidentifying Ġthe ĠPOS Ġtags Ġof Ġthe Ġconstituent Ġwords Ġin Ġthe Ġquestion . ĠWe Ġdefine Ġthe ĠPOS Ġtag Ġdiversity Ġ( POS Div ) Ġof Ġa Ġquestion Ġ$ q _ i $ Ġas Ġfollows : Ġ$ POS Div ( q _ i ) Ġ= Ġ- \ sum Ġ_ { j Ġ\ in Ġpos _{ set }} p _ j \ times Ġ\ log Ġ( p _ j ) $ Ġwhere Ġ$ p _ j $ Ġis Ġthe Ġprobability Ġof Ġthe Ġ$ j ^{ th } $ ĠPOS Ġin Ġthe Ġset Ġof ĠPOS Ġtags . ĠFig Ġ1 Ġ( c ) Ġshows Ġthat Ġthe Ġanswered Ġquestions Ġhave Ġlower ĠPOS Ġtag Ġdiversity Ġcompared Ġto Ġopen Ġquestions . ĠQuestion Ġtexts Ġundergo Ġseveral Ġedits Ġso Ġthat Ġtheir Ġread ability Ġand Ġthe Ġengagement Ġtoward Ġthem Ġare Ġenhanced . ĠIt Ġis Ġinteresting Ġto Ġidentify Ġhow Ġfar Ġsuch Ġedits Ġcan Ġmake Ġthe Ġquestion Ġdifferent Ġfrom Ġthe Ġoriginal Ġversion Ġof Ġit . ĠTo Ġcapture Ġthis Ġphenomena , Ġwe Ġhave Ġadopted ĠR OU GE - LCS Ġrecall ĠB IB REF 9 Ġfrom Ġthe Ġdomain Ġof Ġtext Ġsummar ization . ĠHigher Ġthe Ġrecall Ġvalue , Ġlesser Ġare Ġthe Ġchanges Ġin Ġthe Ġquestion Ġtext . ĠFrom Ġfig Ġ1 Ġ( d ), Ġwe Ġobserve Ġthat Ġopen Ġquestions Ġtend Ġto Ġhave Ġhigher Ġrecall Ġcompared Ġto Ġthe Ġanswered Ġones Ġwhich Ġsuggests Ġthat Ġthey Ġhave Ġnot Ġgone Ġthrough Ġmuch Ġof Ġtext Ġediting Ġthus Ġallowing Ġfor Ġalmost Ġno Ġscope Ġof Ġread ability Ġenhancement . </s> Psych ol ingu istic Ġanalysis : </s> The Ġway Ġan Ġindividual Ġtalks Ġor Ġwrites , Ġgive Ġus Ġclue Ġto Ġhis / her Ġlinguistic , Ġemotional , Ġand Ġcognitive Ġstates . ĠA Ġquestion Ġask er 's Ġlinguistic , Ġemotional , Ġcognitive Ġstates Ġare Ġalso Ġrevealed Ġthrough Ġthe Ġlanguage Ġhe / she Ġuses Ġin Ġthe Ġquestion Ġtext . ĠIn Ġorder Ġto Ġcapture Ġsuch Ġpsych ol ingu istic Ġaspects Ġof Ġthe Ġask er , Ġwe Ġuse ĠL ingu istic ĠInquiry Ġand ĠWord ĠCount Ġ( LI WC ) ĠB IB REF 10 Ġthat Ġanaly zes Ġvarious Ġemotional , Ġcognitive , Ġand Ġstructural Ġcomponents Ġpresent Ġin Ġindividuals ' Ġwritten Ġtexts . ĠLI WC Ġtakes Ġa Ġtext Ġdocument Ġas Ġinput Ġand Ġoutputs Ġa Ġscore Ġfor Ġthe Ġinput Ġfor Ġeach Ġof Ġthe ĠLI WC Ġcategories Ġsuch Ġas Ġlinguistic Ġ( part - of - speech Ġof Ġthe Ġwords , Ġfunction Ġwords Ġetc .) Ġand Ġpsychological Ġcategories Ġ( social , Ġanger , Ġpositive Ġemotion , Ġnegative Ġemotion , Ġsadness Ġetc .) Ġbased Ġon Ġthe Ġwriting Ġstyle Ġand Ġpsych ometric Ġproperties Ġof Ġthe Ġdocument . ĠIn Ġtable Ġ1 Ġ, Ġwe Ġperform Ġa Ġcomparative Ġanalysis Ġof Ġthe Ġask er 's Ġpsych ol ingu istic Ġstate Ġwhile Ġasking Ġan Ġopen Ġquestion Ġand Ġan Ġanswered Ġquestion . </s> As kers Ġof Ġopen Ġquestions Ġuse Ġmore Ġfunction Ġwords , Ġimperson al Ġpronouns , Ġarticles Ġon Ġan Ġaverage Ġwhereas Ġask er Ġof Ġanswered Ġquestions Ġuse Ġmore Ġpersonal Ġpronouns , Ġconj unctions Ġand Ġad verbs Ġto Ġdescribe Ġtheir Ġquestions . ĠEssentially , Ġopen Ġquestions Ġlack Ġcontent Ġwords Ġcompared Ġto Ġanswered Ġquestions Ġwhich , Ġin Ġturn , Ġaffects Ġthe Ġread ability Ġof Ġthe Ġquestion . ĠAs Ġfar Ġas Ġthe Ġpsychological Ġaspects Ġare Ġconcerned , Ġanswered Ġquestion Ġask ers Ġtend Ġto Ġuse Ġmore Ġsocial , Ġfamily , Ġhuman Ġrelated Ġwords Ġon Ġaverage Ġcompared Ġto Ġan Ġopen Ġquestion Ġask er . ĠThe Ġopen Ġquestion Ġask ers Ġexpress Ġmore Ġpositive Ġemotions Ġwhereas Ġthe Ġanswered Ġquestion Ġask er Ġtend Ġto Ġexpress Ġmore Ġnegative Ġemotions Ġin Ġtheir Ġtexts . ĠAlso , Ġanswered Ġquestion Ġask ers Ġare Ġmore Ġemotionally Ġinvolved Ġand Ġtheir Ġquestions Ġreveal Ġhigher Ġusage Ġof Ġanger , Ġsadness , Ġanxiety Ġrelated Ġwords Ġcompared Ġto Ġthat Ġof Ġopen Ġquestions . ĠOpen Ġquestions , Ġon Ġthe Ġother Ġhand , Ġcontains Ġmore Ġsexual , Ġbody , Ġhealth Ġrelated Ġwords Ġwhich Ġmight Ġbe Ġreasons Ġwhy Ġthey Ġdo Ġnot Ġattract Ġanswers . </s> In Ġtable Ġ2 Ġ, Ġwe Ġshow Ġa Ġcollection Ġof Ġexamples Ġof Ġopen Ġquestions Ġto Ġillustrate Ġthat Ġmany Ġof Ġthe Ġabove Ġquantities Ġbased Ġon Ġthe Ġlinguistic Ġactivities Ġdescribed Ġin Ġthis Ġsection Ġnaturally Ġcorrespond Ġto Ġthe Ġfactors Ġthat Ġhuman Ġjudges Ġconsider Ġresponsible Ġfor Ġa Ġquestion Ġremaining Ġunanswered . ĠThis Ġis Ġone Ġof Ġthe Ġprime Ġreasons Ġwhy Ġthese Ġquantities Ġqualify Ġas Ġappropriate Ġindicators Ġof Ġanswer ability . </s> Pred iction Ġmodel </s> In Ġthis Ġsection , Ġwe Ġdescribe Ġthe Ġprediction Ġframework Ġin Ġdetail . ĠOur Ġgoal Ġis Ġto Ġpredict Ġwhether Ġa Ġgiven Ġquestion Ġafter Ġa Ġtime Ġperiod Ġ$ t $ Ġwill Ġbe Ġanswered Ġor Ġnot . </s> L ingu istic Ġstyles Ġof Ġthe Ġquestion Ġask er </s> The Ġcontent Ġand Ġway Ġof Ġposing Ġa Ġquestion Ġis Ġimportant Ġto Ġattract Ġanswers . ĠWe Ġhave Ġobserved Ġin Ġthe Ġprevious Ġsection Ġthat Ġthese Ġlinguistic Ġas Ġwell Ġas Ġpsych ol ingu istic Ġaspects Ġof Ġthe Ġquestion Ġask er Ġare Ġdiscriminatory Ġfactors . ĠFor Ġthe Ġprediction , Ġwe Ġuse Ġthe Ġfollowing Ġfeatures :
Introduction From a group of small users at the time of its inception in 2009, Quora has evolved in the last few years into one of the largest community driven Q&A sites with diverse user communities. With the help of efficient content moderation/review policies and active in-house review team, efficient Quora bots, this site has emerged into one of the largest and reliable sources of Q&A on the Internet. On Quora, users can post questions, follow questions, share questions, tag them with relevant topics, follow topics, follow users apart from answering, commenting, upvoting/downvoting etc. The integrated social structure at the backbone of it and the topical organization of its rich content have made Quora unique with respect to other Q&A sites like Stack Overflow, Yahoo! Answers etc. and these are some of the prime reasons behind its popularity in recent times. Quality question posting and getting them answered are the key objectives of any Q&A site. In this study we focus on the answerability of questions on Quora, i.e., whether a posted question shall eventually get answered. In Quora, the questions with no answers are referred to as “open questions”. These open questions need to be studied separately to understand the reason behind their not being answered or to be precise, are there any characteristic differences between `open' questions and the answered ones. For example, the question “What are the most promising advances in the treatment of traumatic brain injuries?” was posted on Quora on 23rd June, 2011 and got its first answer after almost 2 years on 22nd April, 2013. The reason that this question remained open so long might be the hardness of answering it and the lack of visibility and experts in the domain. Therefore, it is important to identify the open questions and take measures based on the types - poor quality questions can be removed from Quora and the good quality questions can be promoted so that they get more visibility and are eventually routed to topical experts for better answers. Characterization of the questions based on question quality requires expert human interventions often judging if a question would remain open based on factors like if it is subjective, controversial, open-ended, vague/imprecise, ill-formed, off-topic, ambiguous, uninteresting etc. Collecting judgment data for thousands of question posts is a very expensive process. Therefore, such an experiment can be done only for a small set of questions and it would be practically impossible to scale it up for the entire collection of posts on the Q&A site. In this work, we show that appropriate quantification of various linguistic activities can naturally correspond to many of the judgment factors mentioned above (see table 2 for a collection of examples). These quantities encoding such linguistic activities can be easily measured for each question post and thus helps us to have an alternative mechanism to characterize the answerability on the Q&A site. There are several research works done in Q&A focusing on content of posts. BIBREF0 exploit community feedback to identify high quality content on Yahoo! Answers. BIBREF1 use textual features to predict answer quality on Yahoo! Answers. BIBREF2 , investigate predictors of answer quality through a comparative, controlled field study of user responses. BIBREF3 study the problem of how long questions remain unanswered. BIBREF4 propose a prediction model on how many answers a question shall receive. BIBREF5 analyze and predict unanswered questions on Yahoo Answers. BIBREF6 study question quality in Yahoo! Answers. Dataset description We obtained our Quora dataset BIBREF7 through web-based crawls between June 2014 to August 2014. This crawling exercise has resulted in the accumulation of a massive Q&A dataset spanning over a period of over four years starting from January 2010 to May 2014. We initiated crawling with 100 questions randomly selected from different topics so that different genre of questions can be covered. The crawling of the questions follow a BFS pattern through the related question links. We obtained 822,040 unique questions across 80,253 different topics with a total of 1,833,125 answers to these questions. For each question, we separately crawl their revision logs that contain different types of edit information for the question and the activity log of the question asker. Linguistic activities on Quora In this section, we identify various linguistic activities on Quora and propose quantifications of the language usage patterns in this Q&A site. In particular, we show that there exists significant differences in the linguistic structure of the open and the answered questions. Note that most of the measures that we define are simple, intuitive and can be easily obtained automatically from the data (without manual intervention). Therefore the framework is practical, inexpensive and highly scalable. Content of a question text is important to attract people and make them engage more toward it. The linguistic structure (i.e., the usage of POS tags, the use of Out-of-Vocabulary words, character usage etc.) one adopts are key factors for answerability of questions. We shall discuss the linguistic structure that often represents the writing style of a question asker. In fig 1 (a), we observe that askers of open questions generally use more no. of words compared to answered questions. To understand the nature of words (standard English words or chat-like words frequently used in social media) used in the text, we compare the words with GNU Aspell dictionary to see whether they are present in the dictionary or not. We observe that both open questions and answered questions follow similar distribution (see fig 1 (b)). Part-of-Speech (POS) tags are indicators of grammatical aspects of texts. To observe how the Part-of-Speech tags are distributed in the question texts, we define a diversity metric. We use the standard CMU POS tagger BIBREF8 for identifying the POS tags of the constituent words in the question. We define the POS tag diversity (POSDiv) of a question $q_i$ as follows: $POSDiv(q_i) = -\sum _{j \in pos_{set}}p_j\times \log (p_j)$ where $p_j$ is the probability of the $j^{th}$ POS in the set of POS tags. Fig 1 (c) shows that the answered questions have lower POS tag diversity compared to open questions. Question texts undergo several edits so that their readability and the engagement toward them are enhanced. It is interesting to identify how far such edits can make the question different from the original version of it. To capture this phenomena, we have adopted ROUGE-LCS recall BIBREF9 from the domain of text summarization. Higher the recall value, lesser are the changes in the question text. From fig 1 (d), we observe that open questions tend to have higher recall compared to the answered ones which suggests that they have not gone through much of text editing thus allowing for almost no scope of readability enhancement. Psycholinguistic analysis: The way an individual talks or writes, give us clue to his/her linguistic, emotional, and cognitive states. A question asker's linguistic, emotional, cognitive states are also revealed through the language he/she uses in the question text. In order to capture such psycholinguistic aspects of the asker, we use Linguistic Inquiry and Word Count (LIWC) BIBREF10 that analyzes various emotional, cognitive, and structural components present in individuals' written texts. LIWC takes a text document as input and outputs a score for the input for each of the LIWC categories such as linguistic (part-of-speech of the words, function words etc.) and psychological categories (social, anger, positive emotion, negative emotion, sadness etc.) based on the writing style and psychometric properties of the document. In table 1 , we perform a comparative analysis of the asker's psycholinguistic state while asking an open question and an answered question. Askers of open questions use more function words, impersonal pronouns, articles on an average whereas asker of answered questions use more personal pronouns, conjunctions and adverbs to describe their questions. Essentially, open questions lack content words compared to answered questions which, in turn, affects the readability of the question. As far as the psychological aspects are concerned, answered question askers tend to use more social, family, human related words on average compared to an open question asker. The open question askers express more positive emotions whereas the answered question asker tend to express more negative emotions in their texts. Also, answered question askers are more emotionally involved and their questions reveal higher usage of anger, sadness, anxiety related words compared to that of open questions. Open questions, on the other hand, contains more sexual, body, health related words which might be reasons why they do not attract answers. In table 2 , we show a collection of examples of open questions to illustrate that many of the above quantities based on the linguistic activities described in this section naturally correspond to the factors that human judges consider responsible for a question remaining unanswered. This is one of the prime reasons why these quantities qualify as appropriate indicators of answerability. Prediction model In this section, we describe the prediction framework in detail. Our goal is to predict whether a given question after a time period $t$ will be answered or not. Linguistic styles of the question asker The content and way of posing a question is important to attract answers. We have observed in the previous section that these linguistic as well as psycholinguistic aspects of the question asker are discriminatory factors. For the prediction, we use the following features:
Do the answered questions measure for the usefulness of the answer?
9af3142630b350c93875441e1e1767312df76d17
Do Ġthe Ġanswered Ġquestions Ġmeasure Ġfor Ġthe Ġusefulness Ġof Ġthe Ġanswer ?
No
<s> Do Ġthe Ġanswered Ġquestions Ġmeasure Ġfor Ġthe Ġusefulness Ġof Ġthe Ġanswer ? </s> Introduction </s> From Ġa Ġgroup Ġof Ġsmall Ġusers Ġat Ġthe Ġtime Ġof Ġits Ġinception Ġin Ġ2009 , ĠQu ora Ġhas Ġevolved Ġin Ġthe Ġlast Ġfew Ġyears Ġinto Ġone Ġof Ġthe Ġlargest Ġcommunity Ġdriven ĠQ & A Ġsites Ġwith Ġdiverse Ġuser Ġcommunities . ĠWith Ġthe Ġhelp Ġof Ġefficient Ġcontent Ġmoderation / review Ġpolicies Ġand Ġactive Ġin - house Ġreview Ġteam , Ġefficient ĠQu ora Ġbots , Ġthis Ġsite Ġhas Ġemerged Ġinto Ġone Ġof Ġthe Ġlargest Ġand Ġreliable Ġsources Ġof ĠQ & A Ġon Ġthe ĠInternet . ĠOn ĠQu ora , Ġusers Ġcan Ġpost Ġquestions , Ġfollow Ġquestions , Ġshare Ġquestions , Ġtag Ġthem Ġwith Ġrelevant Ġtopics , Ġfollow Ġtopics , Ġfollow Ġusers Ġapart Ġfrom Ġanswering , Ġcommenting , Ġup v oting / down v oting Ġetc . ĠThe Ġintegrated Ġsocial Ġstructure Ġat Ġthe Ġbackbone Ġof Ġit Ġand Ġthe Ġtopical Ġorganization Ġof Ġits Ġrich Ġcontent Ġhave Ġmade ĠQu ora Ġunique Ġwith Ġrespect Ġto Ġother ĠQ & A Ġsites Ġlike ĠStack ĠOver flow , ĠYahoo ! ĠAnswers Ġetc . Ġand Ġthese Ġare Ġsome Ġof Ġthe Ġprime Ġreasons Ġbehind Ġits Ġpopularity Ġin Ġrecent Ġtimes . ĠQuality Ġquestion Ġposting Ġand Ġgetting Ġthem Ġanswered Ġare Ġthe Ġkey Ġobjectives Ġof Ġany ĠQ & A Ġsite . ĠIn Ġthis Ġstudy Ġwe Ġfocus Ġon Ġthe Ġanswer ability Ġof Ġquestions Ġon ĠQu ora , Ġi . e ., Ġwhether Ġa Ġposted Ġquestion Ġshall Ġeventually Ġget Ġanswered . ĠIn ĠQu ora , Ġthe Ġquestions Ġwith Ġno Ġanswers Ġare Ġreferred Ġto Ġas ĠâĢ ľ open Ġquestions âĢ Ŀ . ĠThese Ġopen Ġquestions Ġneed Ġto Ġbe Ġstudied Ġseparately Ġto Ġunderstand Ġthe Ġreason Ġbehind Ġtheir Ġnot Ġbeing Ġanswered Ġor Ġto Ġbe Ġprecise , Ġare Ġthere Ġany Ġcharacteristic Ġdifferences Ġbetween Ġ` open ' Ġquestions Ġand Ġthe Ġanswered Ġones . ĠFor Ġexample , Ġthe Ġquestion ĠâĢ ľ What Ġare Ġthe Ġmost Ġpromising Ġadvances Ġin Ġthe Ġtreatment Ġof Ġtraumatic Ġbrain Ġinjuries ? âĢ Ŀ Ġwas Ġposted Ġon ĠQu ora Ġon Ġ23 rd ĠJune , Ġ2011 Ġand Ġgot Ġits Ġfirst Ġanswer Ġafter Ġalmost Ġ2 Ġyears Ġon Ġ22 nd ĠApril , Ġ2013 . ĠThe Ġreason Ġthat Ġthis Ġquestion Ġremained Ġopen Ġso Ġlong Ġmight Ġbe Ġthe Ġhardness Ġof Ġanswering Ġit Ġand Ġthe Ġlack Ġof Ġvisibility Ġand Ġexperts Ġin Ġthe Ġdomain . ĠTherefore , Ġit Ġis Ġimportant Ġto Ġidentify Ġthe Ġopen Ġquestions Ġand Ġtake Ġmeasures Ġbased Ġon Ġthe Ġtypes Ġ- Ġpoor Ġquality Ġquestions Ġcan Ġbe Ġremoved Ġfrom ĠQu ora Ġand Ġthe Ġgood Ġquality Ġquestions Ġcan Ġbe Ġpromoted Ġso Ġthat Ġthey Ġget Ġmore Ġvisibility Ġand Ġare Ġeventually Ġrouted Ġto Ġtopical Ġexperts Ġfor Ġbetter Ġanswers . </s> Character ization Ġof Ġthe Ġquestions Ġbased Ġon Ġquestion Ġquality Ġrequires Ġexpert Ġhuman Ġinterventions Ġoften Ġjudging Ġif Ġa Ġquestion Ġwould Ġremain Ġopen Ġbased Ġon Ġfactors Ġlike Ġif Ġit Ġis Ġsubjective , Ġcontroversial , Ġopen - ended , Ġvague / im pre cise , Ġill - formed , Ġoff - topic , Ġambiguous , Ġun interesting Ġetc . ĠCollect ing Ġjudgment Ġdata Ġfor Ġthousands Ġof Ġquestion Ġposts Ġis Ġa Ġvery Ġexpensive Ġprocess . ĠTherefore , Ġsuch Ġan Ġexperiment Ġcan Ġbe Ġdone Ġonly Ġfor Ġa Ġsmall Ġset Ġof Ġquestions Ġand Ġit Ġwould Ġbe Ġpractically Ġimpossible Ġto Ġscale Ġit Ġup Ġfor Ġthe Ġentire Ġcollection Ġof Ġposts Ġon Ġthe ĠQ & A Ġsite . ĠIn Ġthis Ġwork , Ġwe Ġshow Ġthat Ġappropriate Ġquant ification Ġof Ġvarious Ġlinguistic Ġactivities Ġcan Ġnaturally Ġcorrespond Ġto Ġmany Ġof Ġthe Ġjudgment Ġfactors Ġmentioned Ġabove Ġ( see Ġtable Ġ2 Ġfor Ġa Ġcollection Ġof Ġexamples ). ĠThese Ġquantities Ġencoding Ġsuch Ġlinguistic Ġactivities Ġcan Ġbe Ġeasily Ġmeasured Ġfor Ġeach Ġquestion Ġpost Ġand Ġthus Ġhelps Ġus Ġto Ġhave Ġan Ġalternative Ġmechanism Ġto Ġcharacterize Ġthe Ġanswer ability Ġon Ġthe ĠQ & A Ġsite . </s> There Ġare Ġseveral Ġresearch Ġworks Ġdone Ġin ĠQ & A Ġfocusing Ġon Ġcontent Ġof Ġposts . ĠB IB REF 0 Ġexploit Ġcommunity Ġfeedback Ġto Ġidentify Ġhigh Ġquality Ġcontent Ġon ĠYahoo ! ĠAnswers . ĠB IB REF 1 Ġuse Ġtextual Ġfeatures Ġto Ġpredict Ġanswer Ġquality Ġon ĠYahoo ! ĠAnswers . ĠB IB REF 2 Ġ, Ġinvestigate Ġpredict ors Ġof Ġanswer Ġquality Ġthrough Ġa Ġcomparative , Ġcontrolled Ġfield Ġstudy Ġof Ġuser Ġresponses . ĠB IB REF 3 Ġstudy Ġthe Ġproblem Ġof Ġhow Ġlong Ġquestions Ġremain Ġunanswered . ĠB IB REF 4 Ġpropose Ġa Ġprediction Ġmodel Ġon Ġhow Ġmany Ġanswers Ġa Ġquestion Ġshall Ġreceive . ĠB IB REF 5 Ġanalyze Ġand Ġpredict Ġunanswered Ġquestions Ġon ĠYahoo ĠAnswers . ĠB IB REF 6 Ġstudy Ġquestion Ġquality Ġin ĠYahoo ! ĠAnswers . </s> Dat as et Ġdescription </s> We Ġobtained Ġour ĠQu ora Ġdataset ĠB IB REF 7 Ġthrough Ġweb - based Ġcraw ls Ġbetween ĠJune Ġ2014 Ġto ĠAugust Ġ2014 . ĠThis Ġcrawling Ġexercise Ġhas Ġresulted Ġin Ġthe Ġaccumulation Ġof Ġa Ġmassive ĠQ & A Ġdataset Ġspanning Ġover Ġa Ġperiod Ġof Ġover Ġfour Ġyears Ġstarting Ġfrom ĠJanuary Ġ2010 Ġto ĠMay Ġ2014 . ĠWe Ġinitiated Ġcrawling Ġwith Ġ100 Ġquestions Ġrandomly Ġselected Ġfrom Ġdifferent Ġtopics Ġso Ġthat Ġdifferent Ġgenre Ġof Ġquestions Ġcan Ġbe Ġcovered . ĠThe Ġcrawling Ġof Ġthe Ġquestions Ġfollow Ġa ĠB FS Ġpattern Ġthrough Ġthe Ġrelated Ġquestion Ġlinks . ĠWe Ġobtained Ġ8 22 , 040 Ġunique Ġquestions Ġacross Ġ80 , 253 Ġdifferent Ġtopics Ġwith Ġa Ġtotal Ġof Ġ1 , 833 , 125 Ġanswers Ġto Ġthese Ġquestions . ĠFor Ġeach Ġquestion , Ġwe Ġseparately Ġcrawl Ġtheir Ġrevision Ġlogs Ġthat Ġcontain Ġdifferent Ġtypes Ġof Ġedit Ġinformation Ġfor Ġthe Ġquestion Ġand Ġthe Ġactivity Ġlog Ġof Ġthe Ġquestion Ġask er . </s> L ingu istic Ġactivities Ġon ĠQu ora </s> In Ġthis Ġsection , Ġwe Ġidentify Ġvarious Ġlinguistic Ġactivities Ġon ĠQu ora Ġand Ġpropose Ġquant ifications Ġof Ġthe Ġlanguage Ġusage Ġpatterns Ġin Ġthis ĠQ & A Ġsite . ĠIn Ġparticular , Ġwe Ġshow Ġthat Ġthere Ġexists Ġsignificant Ġdifferences Ġin Ġthe Ġlinguistic Ġstructure Ġof Ġthe Ġopen Ġand Ġthe Ġanswered Ġquestions . ĠNote Ġthat Ġmost Ġof Ġthe Ġmeasures Ġthat Ġwe Ġdefine Ġare Ġsimple , Ġintuitive Ġand Ġcan Ġbe Ġeasily Ġobtained Ġautomatically Ġfrom Ġthe Ġdata Ġ( without Ġmanual Ġintervention ). ĠTherefore Ġthe Ġframework Ġis Ġpractical , Ġinexpensive Ġand Ġhighly Ġscalable . </s> Content Ġof Ġa Ġquestion Ġtext Ġis Ġimportant Ġto Ġattract Ġpeople Ġand Ġmake Ġthem Ġengage Ġmore Ġtoward Ġit . ĠThe Ġlinguistic Ġstructure Ġ( i . e ., Ġthe Ġusage Ġof ĠPOS Ġtags , Ġthe Ġuse Ġof ĠOut - of - V oc abulary Ġwords , Ġcharacter Ġusage Ġetc .) Ġone Ġadop ts Ġare Ġkey Ġfactors Ġfor Ġanswer ability Ġof Ġquestions . ĠWe Ġshall Ġdiscuss Ġthe Ġlinguistic Ġstructure Ġthat Ġoften Ġrepresents Ġthe Ġwriting Ġstyle Ġof Ġa Ġquestion Ġask er . </s> In Ġfig Ġ1 Ġ( a ), Ġwe Ġobserve Ġthat Ġask ers Ġof Ġopen Ġquestions Ġgenerally Ġuse Ġmore Ġno . Ġof Ġwords Ġcompared Ġto Ġanswered Ġquestions . ĠTo Ġunderstand Ġthe Ġnature Ġof Ġwords Ġ( standard ĠEnglish Ġwords Ġor Ġchat - like Ġwords Ġfrequently Ġused Ġin Ġsocial Ġmedia ) Ġused Ġin Ġthe Ġtext , Ġwe Ġcompare Ġthe Ġwords Ġwith ĠGNU ĠAs pell Ġdictionary Ġto Ġsee Ġwhether Ġthey Ġare Ġpresent Ġin Ġthe Ġdictionary Ġor Ġnot . ĠWe Ġobserve Ġthat Ġboth Ġopen Ġquestions Ġand Ġanswered Ġquestions Ġfollow Ġsimilar Ġdistribution Ġ( see Ġfig Ġ1 Ġ( b )). ĠPart - of - Spe ech Ġ( POS ) Ġtags Ġare Ġindicators Ġof Ġgram matical Ġaspects Ġof Ġtexts . ĠTo Ġobserve Ġhow Ġthe ĠPart - of - Spe ech Ġtags Ġare Ġdistributed Ġin Ġthe Ġquestion Ġtexts , Ġwe Ġdefine Ġa Ġdiversity Ġmetric . ĠWe Ġuse Ġthe Ġstandard ĠCM U ĠPOS Ġtag ger ĠB IB REF 8 Ġfor Ġidentifying Ġthe ĠPOS Ġtags Ġof Ġthe Ġconstituent Ġwords Ġin Ġthe Ġquestion . ĠWe Ġdefine Ġthe ĠPOS Ġtag Ġdiversity Ġ( POS Div ) Ġof Ġa Ġquestion Ġ$ q _ i $ Ġas Ġfollows : Ġ$ POS Div ( q _ i ) Ġ= Ġ- \ sum Ġ_ { j Ġ\ in Ġpos _{ set }} p _ j \ times Ġ\ log Ġ( p _ j ) $ Ġwhere Ġ$ p _ j $ Ġis Ġthe Ġprobability Ġof Ġthe Ġ$ j ^{ th } $ ĠPOS Ġin Ġthe Ġset Ġof ĠPOS Ġtags . ĠFig Ġ1 Ġ( c ) Ġshows Ġthat Ġthe Ġanswered Ġquestions Ġhave Ġlower ĠPOS Ġtag Ġdiversity Ġcompared Ġto Ġopen Ġquestions . ĠQuestion Ġtexts Ġundergo Ġseveral Ġedits Ġso Ġthat Ġtheir Ġread ability Ġand Ġthe Ġengagement Ġtoward Ġthem Ġare Ġenhanced . ĠIt Ġis Ġinteresting Ġto Ġidentify Ġhow Ġfar Ġsuch Ġedits Ġcan Ġmake Ġthe Ġquestion Ġdifferent Ġfrom Ġthe Ġoriginal Ġversion Ġof Ġit . ĠTo Ġcapture Ġthis Ġphenomena , Ġwe Ġhave Ġadopted ĠR OU GE - LCS Ġrecall ĠB IB REF 9 Ġfrom Ġthe Ġdomain Ġof Ġtext Ġsummar ization . ĠHigher Ġthe Ġrecall Ġvalue , Ġlesser Ġare Ġthe Ġchanges Ġin Ġthe Ġquestion Ġtext . ĠFrom Ġfig Ġ1 Ġ( d ), Ġwe Ġobserve Ġthat Ġopen Ġquestions Ġtend Ġto Ġhave Ġhigher Ġrecall Ġcompared Ġto Ġthe Ġanswered Ġones Ġwhich Ġsuggests Ġthat Ġthey Ġhave Ġnot Ġgone Ġthrough Ġmuch Ġof Ġtext Ġediting Ġthus Ġallowing Ġfor Ġalmost Ġno Ġscope Ġof Ġread ability Ġenhancement . </s> Psych ol ingu istic Ġanalysis : </s> The Ġway Ġan Ġindividual Ġtalks Ġor Ġwrites , Ġgive Ġus Ġclue Ġto Ġhis / her Ġlinguistic , Ġemotional , Ġand Ġcognitive Ġstates . ĠA Ġquestion Ġask er 's Ġlinguistic , Ġemotional , Ġcognitive Ġstates Ġare Ġalso Ġrevealed Ġthrough Ġthe Ġlanguage Ġhe / she Ġuses Ġin Ġthe Ġquestion Ġtext . ĠIn Ġorder Ġto Ġcapture Ġsuch Ġpsych ol ingu istic Ġaspects Ġof Ġthe Ġask er , Ġwe Ġuse ĠL ingu istic ĠInquiry Ġand ĠWord ĠCount Ġ( LI WC ) ĠB IB REF 10 Ġthat Ġanaly zes Ġvarious Ġemotional , Ġcognitive , Ġand Ġstructural Ġcomponents Ġpresent Ġin Ġindividuals ' Ġwritten Ġtexts . ĠLI WC Ġtakes Ġa Ġtext Ġdocument Ġas Ġinput Ġand Ġoutputs Ġa Ġscore Ġfor Ġthe Ġinput Ġfor Ġeach Ġof Ġthe ĠLI WC Ġcategories Ġsuch Ġas Ġlinguistic Ġ( part - of - speech Ġof Ġthe Ġwords , Ġfunction Ġwords Ġetc .) Ġand Ġpsychological Ġcategories Ġ( social , Ġanger , Ġpositive Ġemotion , Ġnegative Ġemotion , Ġsadness Ġetc .) Ġbased Ġon Ġthe Ġwriting Ġstyle Ġand Ġpsych ometric Ġproperties Ġof Ġthe Ġdocument . ĠIn Ġtable Ġ1 Ġ, Ġwe Ġperform Ġa Ġcomparative Ġanalysis Ġof Ġthe Ġask er 's Ġpsych ol ingu istic Ġstate Ġwhile Ġasking Ġan Ġopen Ġquestion Ġand Ġan Ġanswered Ġquestion . </s> As kers Ġof Ġopen Ġquestions Ġuse Ġmore Ġfunction Ġwords , Ġimperson al Ġpronouns , Ġarticles Ġon Ġan Ġaverage Ġwhereas Ġask er Ġof Ġanswered Ġquestions Ġuse Ġmore Ġpersonal Ġpronouns , Ġconj unctions Ġand Ġad verbs Ġto Ġdescribe Ġtheir Ġquestions . ĠEssentially , Ġopen Ġquestions Ġlack Ġcontent Ġwords Ġcompared Ġto Ġanswered Ġquestions Ġwhich , Ġin Ġturn , Ġaffects Ġthe Ġread ability Ġof Ġthe Ġquestion . ĠAs Ġfar Ġas Ġthe Ġpsychological Ġaspects Ġare Ġconcerned , Ġanswered Ġquestion Ġask ers Ġtend Ġto Ġuse Ġmore Ġsocial , Ġfamily , Ġhuman Ġrelated Ġwords Ġon Ġaverage Ġcompared Ġto Ġan Ġopen Ġquestion Ġask er . ĠThe Ġopen Ġquestion Ġask ers Ġexpress Ġmore Ġpositive Ġemotions Ġwhereas Ġthe Ġanswered Ġquestion Ġask er Ġtend Ġto Ġexpress Ġmore Ġnegative Ġemotions Ġin Ġtheir Ġtexts . ĠAlso , Ġanswered Ġquestion Ġask ers Ġare Ġmore Ġemotionally Ġinvolved Ġand Ġtheir Ġquestions Ġreveal Ġhigher Ġusage Ġof Ġanger , Ġsadness , Ġanxiety Ġrelated Ġwords Ġcompared Ġto Ġthat Ġof Ġopen Ġquestions . ĠOpen Ġquestions , Ġon Ġthe Ġother Ġhand , Ġcontains Ġmore Ġsexual , Ġbody , Ġhealth Ġrelated Ġwords Ġwhich Ġmight Ġbe Ġreasons Ġwhy Ġthey Ġdo Ġnot Ġattract Ġanswers . </s> In Ġtable Ġ2 Ġ, Ġwe Ġshow Ġa Ġcollection Ġof Ġexamples Ġof Ġopen Ġquestions Ġto Ġillustrate Ġthat Ġmany Ġof Ġthe Ġabove Ġquantities Ġbased Ġon Ġthe Ġlinguistic Ġactivities Ġdescribed Ġin Ġthis Ġsection Ġnaturally Ġcorrespond Ġto Ġthe Ġfactors Ġthat Ġhuman Ġjudges Ġconsider Ġresponsible Ġfor Ġa Ġquestion Ġremaining Ġunanswered . ĠThis Ġis Ġone Ġof Ġthe Ġprime Ġreasons Ġwhy Ġthese Ġquantities Ġqualify Ġas Ġappropriate Ġindicators Ġof Ġanswer ability . </s> Pred iction Ġmodel </s> In Ġthis Ġsection , Ġwe Ġdescribe Ġthe Ġprediction Ġframework Ġin Ġdetail . ĠOur Ġgoal Ġis Ġto Ġpredict Ġwhether Ġa Ġgiven Ġquestion Ġafter Ġa Ġtime Ġperiod Ġ$ t $ Ġwill Ġbe Ġanswered Ġor Ġnot . </s> L ingu istic Ġstyles Ġof Ġthe Ġquestion Ġask er </s> The Ġcontent Ġand Ġway Ġof Ġposing Ġa Ġquestion Ġis Ġimportant Ġto Ġattract Ġanswers . ĠWe Ġhave Ġobserved Ġin Ġthe Ġprevious Ġsection Ġthat Ġthese Ġlinguistic Ġas Ġwell Ġas Ġpsych ol ingu istic Ġaspects Ġof Ġthe Ġquestion Ġask er Ġare Ġdiscriminatory Ġfactors . ĠFor Ġthe Ġprediction , Ġwe Ġuse Ġthe Ġfollowing Ġfeatures :