SVM
EXTRACTIVE
SVM with linear kernel using bag-of-words features
ABSTRACTIVE
As a first baseline we use Bag-of-Words, a well-known and robust text representations used in various domains BIBREF21 , combined with a standard shallow classifier, namely, a Support Vector Machine with linear kernel. We used LIBSVM implementation of SVM.
As a first baseline we use Bag-of-Words, a well-known and robust text representations used in various domains BIBREF21 , combined with a standard shallow classifier, namely, a Support Vector Machine with linear kernel. We used LIBSVM implementation of SVM.
1707.06806
Introduction </s> The Ġdistribution Ġof Ġtextual Ġcontent Ġis Ġtypically Ġvery Ġfast Ġand Ġcatches Ġuser Ġattention Ġfor Ġonly Ġa Ġshort Ġperiod Ġof Ġtime ĠB IB REF 0 Ġ. ĠFor Ġthis Ġreason , Ġproper Ġwording Ġof Ġthe Ġarticle Ġtitle Ġmay Ġplay Ġa Ġsignificant Ġrole Ġin Ġdetermining Ġthe Ġfuture Ġpopularity Ġof Ġthe Ġarticle . ĠThe Ġreflection Ġof Ġthis Ġphenomenon Ġis Ġthe Ġproliferation Ġof Ġclick - ba its Ġ- Ġshort Ġsnippets Ġof Ġtext Ġwhose Ġmain Ġpurpose Ġis Ġto Ġencourage Ġviewers Ġto Ġclick Ġon Ġthe Ġlink Ġembedded Ġin Ġthe Ġsnippet . ĠAlthough Ġdetection Ġof Ġclick - ba its Ġis Ġa Ġseparate Ġresearch Ġtopic ĠB IB REF 1 Ġ, Ġin Ġthis Ġpaper Ġwe Ġaddress Ġa Ġmore Ġgeneral Ġproblem Ġof Ġpredicting Ġpopularity Ġof Ġonline Ġcontent Ġbased Ġsolely Ġon Ġits Ġtitle . </s> P redict ing Ġpopularity Ġin Ġthe ĠInternet Ġis Ġa Ġchallenging Ġand Ġnon - t riv ial Ġtask Ġdue Ġto Ġa Ġmultitude Ġof Ġfactors Ġimpacting Ġthe Ġdistribution Ġof Ġthe Ġinformation : Ġexternal Ġcontext , Ġsocial Ġnetwork Ġof Ġthe Ġpublishing Ġparty , Ġrelevance Ġof Ġthe Ġvideo Ġto Ġthe Ġfinal Ġuser , Ġetc . ĠThis Ġtopic Ġhas Ġtherefore Ġattracted Ġa Ġlot Ġof Ġattention Ġfrom Ġthe Ġresearch Ġcommunity ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 0 Ġ, ĠB IB REF 4 Ġ. </s> In Ġthis Ġpaper Ġwe Ġpropose Ġa Ġmethod Ġfor Ġonline Ġcontent Ġpopularity Ġprediction Ġbased Ġon Ġa Ġbid irection al Ġrecurrent Ġneural Ġnetwork Ġcalled ĠBi L ST M . ĠThis Ġwork Ġis Ġinspired Ġby Ġrecent Ġsuccessful Ġapplications Ġof Ġdeep Ġneural Ġnetworks Ġin Ġmany Ġnatural Ġlanguage Ġprocessing Ġproblems ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠOur Ġmethod Ġattempts Ġto Ġmodel Ġcomplex Ġrelationships Ġbetween Ġthe Ġtitle Ġof Ġan Ġarticle Ġand Ġits Ġpopularity Ġusing Ġnovel Ġdeep Ġnetwork Ġarchitecture Ġthat , Ġin Ġcontrast Ġto Ġthe Ġprevious Ġapproaches , Ġgives Ġhighly Ġinterpret able Ġresults . ĠLast Ġbut Ġnot Ġleast , Ġthe Ġproposed ĠBi L ST M Ġmethod Ġprovides Ġa Ġsignificant Ġperformance Ġboost Ġin Ġterms Ġof Ġprediction Ġaccuracy Ġover Ġthe Ġstandard Ġshallow Ġapproach , Ġwhile Ġoutper forming Ġthe Ġcurrent Ġstate - of - the - art Ġon Ġtwo Ġdistinct Ġdatasets Ġwith Ġover Ġ40 , 000 Ġsamples . </s> To Ġsummarize , Ġthe Ġcontributions Ġpresented Ġin Ġthis Ġpaper Ġare Ġthe Ġfollowing : </s> The Ġremainder Ġof Ġthis Ġpaper Ġis Ġorganized Ġin Ġthe Ġfollowing Ġmanner : Ġfirst , Ġwe Ġreview Ġthe Ġrelevant Ġliterature Ġand Ġcompare Ġour Ġapproach Ġto Ġexisting Ġwork . ĠNext , Ġwe Ġformulate Ġthe Ġproblem Ġof Ġpopularity Ġprediction Ġand Ġpropose Ġa Ġmodel Ġthat Ġtakes Ġadvantage Ġof ĠBi L ST M Ġarchitecture Ġto Ġaddress Ġit . ĠThen , Ġwe Ġevaluate Ġour Ġmodel Ġon Ġtwo Ġdatasets Ġusing Ġseveral Ġpre - trained Ġword Ġembed d ings Ġand Ġcompare Ġit Ġto Ġbenchmark Ġmodels . ĠWe Ġconclude Ġthis Ġwork Ġwith Ġdiscussion Ġon Ġfuture Ġresearch Ġpaths . </s> Related ĠWork </s> The Ġever Ġincreasing Ġpopularity Ġof Ġthe ĠInternet Ġas Ġa Ġvirtual Ġspace Ġto Ġshare Ġcontent Ġinspired Ġresearch Ġcommunity Ġto Ġanalyze Ġdifferent Ġaspects Ġof Ġonline Ġinformation Ġdistribution . ĠVarious Ġtypes Ġof Ġcontent Ġwere Ġanalyzed , Ġranging Ġfrom Ġtextual Ġdata , Ġsuch Ġas ĠTwitter Ġposts ĠB IB REF 0 Ġor ĠD igg Ġstories ĠB IB REF 2 Ġto Ġimages ĠB IB REF 7 Ġto Ġvideos ĠB IB REF 8 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 9 Ġ. ĠAlthough Ġseveral Ġsimilarities Ġwere Ġobserved Ġacross Ġcontent Ġdomains , Ġe . g . Ġlog - normal Ġdistribution Ġof Ġdata Ġpopularity ĠB IB REF 10 Ġ, Ġin Ġthis Ġwork Ġwe Ġfocus Ġonly Ġon Ġtextual Ġcontent Ġand , Ġmore Ġprecisely , Ġon Ġthe Ġpopularity Ġof Ġnews Ġarticles Ġand Ġits Ġrelation Ġto Ġthe Ġarticle 's Ġtitle . </s> Fore casting Ġpopularity Ġof Ġnews Ġarticles Ġwas Ġespecially Ġwell Ġstudied Ġin Ġthe Ġcontext Ġof ĠTwitter Ġ- Ġa Ġsocial Ġmedia Ġplatform Ġdesigned Ġspecifically Ġfor Ġsharing Ġtextual Ġdata ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ. ĠNot Ġonly Ġdid Ġthe Ġprevious Ġworks Ġfocus Ġon Ġthe Ġprediction Ġpart , Ġbut Ġalso Ġon Ġmodeling Ġmessage Ġpropagation Ġwithin Ġthe Ġnetwork ĠB IB REF 13 Ġ. ĠHowever , Ġmost Ġof Ġthe Ġworks Ġwere Ġfocused Ġon Ġanalyzing Ġthe Ġsocial Ġinteractions Ġbetween Ġthe Ġusers Ġand Ġthe Ġcharacteristics Ġof Ġso - called Ġsocial Ġgraph Ġof Ġusers ' Ġconnections , Ġrather Ġthan Ġon Ġthe Ġtextual Ġfeatures . ĠContrary Ġto Ġthose Ġapproaches , Ġin Ġthis Ġpaper Ġwe Ġbase Ġour Ġpredictions Ġusing Ġonly Ġtextual Ġfeatures Ġof Ġthe Ġarticle Ġtitle . ĠWe Ġalso Ġvalidate Ġour Ġproposed Ġmethod Ġon Ġone Ġdataset Ġcollected Ġusing Ġa Ġdifferent Ġsocial Ġmedia Ġplatform , Ġnamely ĠFacebook , Ġand Ġanother Ġone Ġcreated Ġfrom Ġvarious Ġnews Ġarticles ĠB IB REF 4 Ġ. </s> Recently , Ġseveral Ġworks Ġhave Ġtouched Ġon Ġthe Ġtopic Ġof Ġpopularity Ġprediction Ġof Ġnews Ġarticle Ġfrom Ġa Ġmultim od al Ġperspective ĠB IB REF 4 Ġ, ĠB IB REF 14 Ġ. ĠAlthough Ġin ĠB IB REF 4 Ġthe Ġauthors Ġanalyze Ġnews Ġarticles Ġon Ġa Ġper - mod ality Ġbasis , Ġthey Ġdo Ġnot Ġapproach Ġthe Ġproblem Ġof Ġpopularity Ġprediction Ġin Ġa Ġholistic Ġway . ĠTo Ġaddress Ġthis Ġshort coming , ĠB IB REF 14 Ġhave Ġproposed Ġa Ġmultim od al Ġapproach Ġto Ġpredicting Ġpopularity Ġof Ġshort Ġvideos Ġshares Ġin Ġsocial Ġmedia Ġplatform ĠVine Ġusing Ġa Ġmodel Ġthat Ġf uses Ġfeatures Ġrelated Ġto Ġdifferent Ġmod alities . ĠIn Ġour Ġwork , Ġwe Ġfocus Ġonly Ġon Ġtextual Ġfeatures Ġof Ġthe Ġarticle Ġtitle Ġfor Ġthe Ġpurpose Ġof Ġpopularity Ġprediction , Ġas Ġour Ġgoal Ġis Ġto Ġempower Ġthe Ġjournalists Ġto Ġquant itatively Ġassess Ġthe Ġquality Ġof Ġthe Ġheadlines Ġthey Ġcreate Ġbefore Ġthe Ġpublication . ĠNevertheless , Ġwe Ġbelieve Ġthat Ġin Ġfuture Ġresearch Ġwe Ġwill Ġextend Ġour Ġmethod Ġtowards Ġmultim od al Ġpopularity Ġprediction . </s> Method </s> In Ġthis Ġsection Ġwe Ġpresent Ġthe Ġbid irection al ĠL ST M Ġmodel Ġfor Ġpopularity Ġprediction . ĠWe Ġstart Ġby Ġform ulating Ġthe Ġproblem Ġand Ġfollow Ġup Ġwith Ġthe Ġdescription Ġof Ġword Ġembed d ings Ġused Ġin Ġour Ġapproach . ĠWe Ġthen Ġpresent Ġthe ĠLong ĠShort - Term ĠMemory Ġnetwork Ġthat Ġserves Ġas Ġa Ġbackbone Ġfor Ġour Ġbid irection al ĠL ST M Ġarchitecture . ĠWe Ġconclude Ġthis Ġsection Ġwith Ġour Ġinterpretation Ġof Ġhidden Ġbid irection al Ġstates Ġand Ġdescribe Ġhow Ġthey Ġcan Ġbe Ġemployed Ġfor Ġtitle Ġintro spection . </s> Problem ĠForm ulation </s> We Ġcast Ġthe Ġproblem Ġof Ġpopularity Ġprediction Ġas Ġa Ġbinary Ġclassification Ġtask . ĠWe Ġassume Ġour Ġdata Ġpoints Ġcontain Ġa Ġstring Ġof Ġcharacters Ġrepresenting Ġarticle Ġtitle Ġand Ġa Ġpopularity Ġmetric , Ġsuch Ġas Ġnumber Ġof Ġcomments Ġor Ġviews . ĠThe Ġinput Ġof Ġour Ġclassification Ġis Ġthe Ġcharacter Ġstring , Ġwhile Ġthe Ġoutput Ġis Ġthe Ġbinary Ġlabel Ġcorresponding Ġto Ġpopular Ġor Ġunpopular Ġclass . ĠTo Ġenable Ġthe Ġcomparison Ġof Ġthe Ġmethods Ġon Ġdatasets Ġcontaining Ġcontent Ġpublished Ġon Ġdifferent Ġwebsites Ġand Ġwith Ġdifferent Ġaudience Ġsizes , Ġwe Ġdetermine Ġthat Ġa Ġvideo Ġis Ġpopular Ġif Ġits Ġpopularity Ġmetric Ġexceeds Ġthe Ġmedian Ġvalue Ġof Ġthe Ġcorresponding Ġmetric Ġfor Ġother Ġpoints Ġin Ġthe Ġset , Ġotherwise Ġ- Ġit Ġis Ġlabeled Ġas Ġunpopular . ĠThe Ġdetails Ġof Ġthe Ġlabeling Ġprocedure Ġare Ġdiscussed Ġseparately Ġin Ġthe ĠDat as ets Ġsection . </s> Text ĠRepresent ation </s> Since Ġthe Ġinput Ġof Ġour Ġmethod Ġis Ġtextual Ġdata , Ġwe Ġfollow Ġthe Ġapproach Ġof ĠB IB REF 15 Ġand Ġmap Ġthe Ġtext Ġinto Ġa Ġfixed - size Ġvector Ġrepresentation . ĠTo Ġthis Ġend , Ġwe Ġuse Ġword Ġembed d ings Ġthat Ġwere Ġsuccessfully Ġapplied Ġin Ġother Ġdomains . ĠWe Ġfollow ĠB IB REF 5 Ġand Ġuse Ġpre - trained ĠGl o Ve Ġword Ġvectors ĠB IB REF 16 Ġto Ġinitialize Ġthe Ġembed ding Ġlayer Ġ( also Ġknown Ġas Ġlook - up Ġtable ). ĠSection ĠSEC REF 18 Ġdiscusses Ġthe Ġembed ding Ġlayer Ġin Ġmore Ġdetails . </s> B id irection al ĠLong ĠShort - Term ĠMemory ĠNetwork </s> Our Ġmethod Ġfor Ġpopularity Ġprediction Ġusing Ġarticle 's Ġtitle Ġis Ġinspired Ġby Ġa Ġbid irection al ĠL ST M Ġarchitecture . ĠThe Ġoverview Ġof Ġthe Ġmodel Ġcan Ġbe Ġseen Ġin ĠFig . ĠFIG REF 8 Ġ. </s> Let ĠIN LINE FORM 0 Ġbe ĠIN LINE FORM 1 Ġ- dimensional Ġword Ġvector Ġcorresponding Ġto Ġthe ĠIN LINE FORM 2 Ġ- the Ġword Ġin Ġthe Ġheadline , Ġthen Ġa Ġvariable Ġlength Ġsequence : ĠIN LINE FORM 3 Ġrepresents Ġa Ġheadline . ĠA Ġrecurrent Ġneural Ġnetwork Ġ( R NN ) Ġprocesses Ġthis Ġsequence Ġby Ġrec urs ively Ġapplying Ġa Ġtransformation Ġfunction Ġto Ġthe Ġcurrent Ġelement Ġof Ġsequence ĠIN LINE FORM 4 Ġand Ġits Ġprevious Ġhidden Ġinternal Ġstate ĠIN LINE FORM 5 Ġ( option ally Ġoutput ting ĠIN LINE FORM 6 Ġ). ĠAt Ġeach Ġtime Ġstep ĠIN LINE FORM 7 Ġ, Ġthe Ġhidden Ġstate Ġis Ġupdated Ġby : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġnon - linear Ġactivation Ġfunction . ĠL ST M Ġnetwork ĠB IB REF 17 Ġupdates Ġits Ġinternal Ġstate Ġdifferently , Ġat Ġeach Ġstep ĠIN LINE FORM 1 Ġit Ġcalculates : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġs igm oid Ġactivation Ġfunction , Ġtan h Ġis Ġthe Ġhyper b olic Ġtang ent Ġfunction Ġand ĠIN LINE FORM 1 Ġdenotes Ġcomponent - wise Ġmultiplication . ĠIn Ġour Ġexperiments Ġwe Ġused Ġ128 , Ġ256 Ġfor Ġthe Ġdimension ality Ġof Ġhidden Ġlayer Ġin Ġboth ĠL ST M Ġand ĠBi L ST M . ĠThe Ġterm Ġin Ġequation ĠEQ REF 10 ĠIN LINE FORM 2 Ġ, Ġis Ġcalled Ġthe Ġinput Ġgate Ġand Ġit Ġuses Ġthe Ġinput Ġword Ġand Ġthe Ġpast Ġhidden Ġstate Ġto Ġdetermine Ġwhether Ġthe Ġinput Ġis Ġworth Ġremembering Ġor Ġnot . ĠThe Ġamount Ġof Ġinformation Ġthat Ġis Ġbeing Ġdiscarded Ġis Ġcontrolled Ġby Ġforget Ġgate ĠIN LINE FORM 3 Ġ, Ġwhile ĠIN LINE FORM 4 Ġis Ġthe Ġoutput Ġgate Ġthat Ġcontrols Ġthe Ġamount Ġof Ġinformation Ġthat Ġleaks Ġfrom Ġmemory Ġcell ĠIN LINE FORM 5 Ġto Ġthe Ġhidden Ġstate ĠIN LINE FORM 6 Ġ. ĠIn Ġthe Ġcontext Ġof Ġclassification , Ġwe Ġtypically Ġtreat Ġthe Ġoutput Ġof Ġthe Ġhidden Ġstate Ġat Ġthe Ġlast Ġtime Ġstep Ġof ĠL ST M Ġas Ġthe Ġdocument Ġrepresentation Ġand Ġfeed Ġit Ġto Ġs igm oid Ġlayer Ġto Ġperform Ġclassification ĠB IB REF 18 Ġ. </s> Due Ġto Ġits Ġsequential Ġnature , Ġa Ġrecurrent Ġneural Ġnetwork Ġputs Ġmore Ġemphasis Ġon Ġthe Ġrecent Ġelements . ĠTo Ġcircumvent Ġthis Ġproblem ĠB IB REF 19 Ġintroduced Ġa Ġbid irection al ĠR NN Ġin Ġwhich Ġeach Ġtraining Ġsequence Ġis Ġpresented Ġforwards Ġand Ġbackwards Ġto Ġtwo Ġseparate Ġrecurrent Ġnets , Ġboth Ġof Ġwhich Ġare Ġconnected Ġto Ġthe Ġsame Ġoutput Ġlayer . ĠTherefore , Ġat Ġany Ġtime - step Ġwe Ġhave Ġthe Ġwhole Ġinformation Ġabout Ġthe Ġsequence . ĠThis Ġis Ġshown Ġby Ġthe Ġfollowing Ġequation : ĠDIS PLAY FORM 0 </s> In Ġour Ġmethod , Ġwe Ġuse Ġthe Ġbid irection al ĠL ST M Ġarchitecture Ġfor Ġcontent Ġpopularity Ġprediction Ġusing Ġonly Ġtextual Ġcues . ĠWe Ġhave Ġto Ġtherefore Ġmap Ġthe Ġneural Ġnetwork Ġoutputs Ġfrom Ġa Ġset Ġof Ġhidden Ġstates ĠIN LINE FORM 0 Ġto Ġclassification Ġlabels . ĠWe Ġevaluated Ġseveral Ġapproaches Ġto Ġthis Ġproblem , Ġsuch Ġas Ġmax Ġor Ġmean Ġpool ing . ĠThe Ġinitial Ġexperiments Ġshowed Ġthat Ġthe Ġhighest Ġperformance Ġwas Ġachieved Ġusing Ġlate Ġfusion Ġapproach , Ġthat Ġis Ġby Ġconc aten ating Ġthe Ġlast Ġhidden Ġstate Ġin Ġforward Ġand Ġbackward Ġsequence . ĠThe Ġintuition Ġbehind Ġthis Ġdesign Ġchoice Ġis Ġthat Ġthe Ġimportance Ġof Ġthe Ġfirst Ġfew Ġwords Ġof Ġthe Ġheadline Ġis Ġrelatively Ġhigh , Ġas Ġthe Ġinformation Ġcontained Ġin ĠIN LINE FORM 1 Ġ, Ġi . e . Ġthe Ġlast Ġitem Ġin Ġthe Ġbackward Ġsequence , Ġis Ġmostly Ġtaken Ġfrom Ġthe Ġfirst Ġword . </s> Hidden ĠState ĠInterpret ation </s> One Ġinteresting Ġproperty Ġof Ġbid irection al ĠR NN s Ġis Ġthe Ġfact , Ġthat Ġthe Ġconc aten ation Ġof Ġhidden Ġstates ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġcan Ġbe Ġinterpreted Ġas Ġa Ġcontext - dependent Ġvector Ġrepresentation Ġof Ġword ĠIN LINE FORM 2 Ġ. ĠThis Ġallows Ġus Ġto Ġintro spect Ġa Ġgiven Ġtitle Ġand Ġapproximate Ġthe Ġcontribution Ġof Ġeach Ġword Ġto Ġthe Ġestimated Ġpopularity . ĠTo Ġthat Ġend Ġone Ġcan Ġprocess Ġthe Ġheadline Ġrepresentation ĠIN LINE FORM 3 Ġthrough Ġthe Ġbid irection al Ġrecurrent Ġnetwork Ġand Ġthen Ġretrieve Ġpairs Ġof Ġforward Ġand Ġbackwards Ġhidden Ġstate ĠIN LINE FORM 4 Ġfor Ġeach Ġword ĠIN LINE FORM 5 Ġ. ĠThen , Ġthe Ġoutput Ġof Ġthe Ġlast Ġfully - connected Ġlayer ĠIN LINE FORM 6 Ġcould Ġbe Ġinterpreted Ġas Ġcontext - dep ended Ġpopularity Ġof Ġa Ġword ĠIN LINE FORM 7 Ġ. </s> Training </s> In Ġour Ġexperiments Ġwe Ġminimize Ġthe Ġbinary Ġcross - ent ropy Ġloss Ġusing ĠSt och astic ĠGrad ient ĠDes cent Ġon Ġrandomly Ġshuff led Ġmini - bat ches Ġwith Ġthe ĠAdam Ġoptimization Ġalgorithm ĠB IB REF 20 Ġ. ĠWe Ġreduce Ġthe Ġlearning Ġrate Ġby Ġa Ġfactor Ġof Ġ0 . 2 Ġonce Ġlearning Ġplate aus . ĠWe Ġalso Ġemploy Ġearly Ġstopping Ġstrategy , Ġi . e . Ġstopping Ġthe Ġtraining Ġalgorithm Ġbefore Ġconvergence Ġbased Ġon Ġthe Ġvalues Ġof Ġloss Ġfunction Ġon Ġthe Ġvalidation Ġset . </s> E val uation </s> In Ġthis Ġsection , Ġwe Ġevaluate Ġour Ġmethod Ġand Ġcompare Ġits Ġperformance Ġagainst Ġthe Ġcompetitive Ġapproaches . ĠWe Ġuse ĠIN LINE FORM 0 Ġ- fold Ġevaluation Ġprotocol Ġwith ĠIN LINE FORM 1 Ġwith Ġrandom Ġdataset Ġsplit . ĠWe Ġmeasure Ġthe Ġperformance Ġusing Ġstandard Ġaccuracy Ġmetric Ġwhich Ġwe Ġdefine Ġas Ġa Ġratio Ġbetween Ġcorrectly Ġclassified Ġdata Ġsamples Ġfrom Ġtest Ġdataset Ġand Ġall Ġtest Ġsamples . </s> Dat as ets </s> In Ġthis Ġsection Ġwe Ġpresent Ġtwo Ġdatasets Ġused Ġin Ġour Ġexperiments : ĠThe ĠNow This News Ġdataset , Ġcollected Ġfor Ġthe Ġpurpose Ġof Ġthis Ġpaper , Ġand ĠThe ĠBreaking News Ġdataset ĠB IB REF 4 Ġ, Ġpublicly Ġavailable Ġdataset Ġof Ġnews Ġarticles . </s> cont ains Ġ4090 Ġposts Ġwith Ġassociated Ġvideos Ġfrom ĠNow This News ĠFacebook Ġpage Ġcollected Ġbetween Ġ07 / 2015 Ġand Ġ07 / 2016 . ĠFor Ġeach Ġpost Ġwe Ġcollected Ġits Ġtitle Ġand Ġthe Ġnumber Ġof Ġviews Ġof Ġthe Ġcorresponding Ġvideo , Ġwhich Ġwe Ġconsider Ġour Ġpopularity Ġmetric . ĠDue Ġto Ġa Ġfairly Ġlengthy Ġdata Ġcollection Ġprocess , Ġwe Ġdecided Ġto Ġnormal ize Ġour Ġdata Ġby Ġfirst Ġgrouping Ġposts Ġaccording Ġto Ġtheir Ġpublication Ġmonth Ġand Ġthen Ġlabeling Ġthe Ġposts Ġfor Ġwhich Ġthe Ġpopularity Ġmetric Ġexceeds Ġthe Ġmedian Ġmonthly Ġvalue Ġas Ġpopular , Ġthe Ġremaining Ġpart Ġas Ġunpopular . </s> BI B REF 4 Ġcontains Ġa Ġvariety Ġof Ġnews - related Ġinformation Ġsuch Ġas Ġimages , Ġcapt ions , Ġgeo - location Ġinformation Ġand Ġcomments Ġwhich Ġcould Ġbe Ġused Ġas Ġa Ġproxy Ġfor Ġarticle Ġpopularity . ĠThe Ġarticles Ġin Ġthis Ġdataset Ġwere Ġcollected Ġbetween ĠJanuary Ġand ĠDecember Ġ2014 . ĠAlthough Ġwe Ġtried Ġto Ġretrieve Ġthe Ġentire Ġdataset , Ġwe Ġwere Ġable Ġto Ġdownload Ġonly Ġ38 , 182 Ġarticles Ġdue Ġto Ġthe Ġdead Ġlinks Ġpublished Ġin Ġthe Ġdataset . ĠThe Ġretrieved Ġarticles Ġwere Ġpublished Ġin Ġmain Ġnews Ġchannels , Ġsuch Ġas ĠYahoo ĠNews , ĠThe ĠGuardian Ġor ĠThe ĠWashington ĠPost . ĠSimilarly , Ġto ĠThe ĠNow This News Ġdataset Ġwe Ġnormal ize Ġthe Ġdata Ġby Ġgrouping Ġarticles Ġper Ġpublisher , Ġand Ġclass ifying Ġthem Ġas Ġpopular , Ġwhen Ġthe Ġnumber Ġof Ġcomments Ġexceeds Ġthe Ġmedian Ġvalue Ġfor Ġgiven Ġpublisher . </s> Bas elines </s> As Ġa Ġfirst Ġbaseline Ġwe Ġuse ĠBag - of - Words , Ġa Ġwell - known Ġand Ġrobust Ġtext Ġrepresentations Ġused Ġin Ġvarious Ġdomains ĠB IB REF 21 Ġ, Ġcombined Ġwith Ġa Ġstandard Ġshallow Ġclass ifier , Ġnamely , Ġa ĠSupport ĠVector ĠMachine Ġwith Ġlinear Ġkernel . ĠWe Ġused ĠLI BS VM Ġimplementation Ġof ĠS VM . </s> Our Ġsecond Ġbaseline Ġis Ġa Ġdeep ĠCon ve ction al ĠNeural ĠNetwork Ġapplied Ġon Ġword Ġembed d ings . ĠThis Ġbaseline Ġrepresents Ġstate - of - the - art Ġmethod Ġpresented Ġin ĠB IB REF 4 Ġwith Ġminor Ġadjustments Ġto Ġthe Ġbinary Ġclassification Ġtask . ĠThe Ġarchitecture Ġof Ġthe ĠCNN Ġbenchmark Ġwe Ġuse Ġis Ġthe Ġfollowing : Ġthe Ġembed ding Ġlayer Ġtransforms Ġone - hot Ġencoded Ġwords Ġto Ġtheir Ġdense Ġvector Ġrepresentations , Ġfollowed Ġby Ġthe Ġconv olution Ġlayer Ġof Ġ256 Ġfilters Ġwith Ġwidth Ġequal Ġto Ġ5 Ġfollowed Ġby Ġmax Ġpool ing Ġlayer Ġ( repe ated Ġthree Ġtimes ), Ġfully - connected Ġlayer Ġwith Ġdrop out Ġand ĠIN LINE FORM 0 Ġregular ization Ġand Ġfinally , Ġs igm oid Ġactivation Ġlayer . ĠFor Ġfair Ġcomparison , Ġboth Ġbas elines Ġwere Ġtrained Ġusing Ġthe Ġsame Ġtraining Ġprocedure Ġas Ġour Ġmethod . </s> Emb edd ings </s> As Ġa Ġtext Ġembed ding Ġin Ġour Ġexperiments , Ġwe Ġuse Ġpublicly Ġavailable ĠGl o Ve Ġword Ġvectors ĠB IB REF 16 Ġpre - trained Ġon Ġtwo Ġdatasets : ĠWikipedia Ġ2014 Ġwith ĠGig aw ord 5 Ġ( W + G 5 ) Ġand ĠCommon ĠC rawl Ġ( CC ). ĠSince Ġtheir Ġoutput Ġdimension ality Ġcan Ġbe Ġmodified , Ġwe Ġshow Ġthe Ġresults Ġfor Ġvarying Ġdimension ality Ġsizes . ĠOn Ġtop Ġof Ġthat , Ġwe Ġevaluate Ġtwo Ġtraining Ġapproaches : Ġusing Ġstatic Ġword Ġvectors Ġand Ġfine - tun ing Ġthem Ġduring Ġtraining Ġphase . </s> Results </s> The Ġresults Ġof Ġour Ġexperiments Ġcan Ġbe Ġseen Ġin ĠTab . ĠT AB REF 21 Ġand ĠT AB REF 22 Ġ. ĠOur Ġproposed ĠBi L ST M Ġapproach Ġoutper forms Ġthe Ġcompeting Ġmethods Ġconsistently Ġacross Ġboth Ġdatasets . ĠThe Ġperformance Ġimprovement Ġis Ġespecially Ġvisible Ġfor ĠThe ĠNow This News Ġdataset Ġand Ġreaches Ġover Ġ15 % Ġwith Ġrespect Ġto Ġthe Ġshallow Ġarchitecture Ġin Ġterms Ġof Ġof Ġaccuracy . ĠAlthough Ġthe Ġimprovement Ġwith Ġrespect Ġto Ġthe Ġother Ġmethods Ġbased Ġon Ġdeep Ġneural Ġnetwork Ġis Ġless Ġevident , Ġthe Ġrecurrent Ġnature Ġof Ġour Ġmethod Ġprovides Ġmuch Ġmore Ġintuitive Ġinterpretation Ġof Ġthe Ġresults Ġand Ġallow Ġfor Ġparsing Ġthe Ġcontribution Ġof Ġeach Ġsingle Ġword Ġto Ġthe Ġoverall Ġscore . </s> To Ġpresent Ġhow Ġour Ġmodel Ġworks Ġin Ġpractice , Ġwe Ġshow Ġin ĠTab . ĠT AB REF 23 Ġa Ġlist Ġof Ġ3 Ġheadlines Ġfrom ĠNow This News Ġdataset Ġthat Ġare Ġscored Ġwith Ġthe Ġhighest Ġprobability Ġof Ġbelonging Ġto Ġa Ġpopular Ġclass , Ġas Ġwell Ġas Ġ3 Ġheadlines Ġwith Ġthe Ġlowest Ġscore . ĠAs Ġcan Ġbe Ġseen , Ġour Ġmodel Ġcorrectly Ġdetected Ġvideos Ġthat Ġbecome Ġviral Ġat Ġthe Ġsame Ġtime Ġassigning Ġlow Ġscore Ġto Ġcontent Ġthat Ġunder per formed . ĠWe Ġbelieve Ġthat ĠBi L ST M Ġcould Ġbe Ġsuccessfully Ġapplied Ġin Ġreal - life Ġscenarios . </s> Con clusions </s> In Ġthis Ġpaper Ġwe Ġpresent Ġa Ġnovel Ġapproach Ġto Ġthe Ġproblem Ġof Ġonline Ġarticle Ġpopularity Ġprediction . ĠTo Ġour Ġknowledge , Ġthis Ġis Ġthe Ġfirst Ġattempt Ġof Ġpredicting Ġthe Ġperformance Ġof Ġcontent Ġon Ġsocial Ġmedia Ġusing Ġonly Ġtextual Ġinformation Ġfrom Ġits Ġtitle . ĠWe Ġshow Ġthat Ġour Ġmethod Ġconsistently Ġoutper forms Ġbenchmark Ġmodels . ĠAdditionally , Ġthe Ġproposed Ġmethod Ġcould Ġnot Ġonly Ġbe Ġused Ġto Ġcompare Ġcompeting Ġtitles Ġwith Ġregard Ġto Ġtheir Ġestimated Ġprobability , Ġbut Ġalso Ġto Ġgain Ġinsights Ġabout Ġwhat Ġconstitutes Ġa Ġgood Ġtitle . ĠFuture Ġwork Ġincludes Ġmodeling Ġpopularity Ġprediction Ġproblem Ġwith Ġmultiple Ġdata Ġmod alities , Ġsuch Ġas Ġimages Ġor Ġvideos . ĠFurthermore , Ġall Ġof Ġthe Ġevaluated Ġmodels Ġfunction Ġat Ġthe Ġword Ġlevel , Ġwhich Ġcould Ġbe Ġproblematic Ġdue Ġto Ġidiosyncr atic Ġnature Ġof Ġsocial Ġmedia Ġand ĠInternet Ġcontent . ĠIt Ġis , Ġtherefore , Ġworth Ġinvestigating , Ġwhether Ġcombining Ġmodels Ġthat Ġoperate Ġat Ġthe Ġcharacter Ġlevel Ġto Ġlearn Ġand Ġgenerate Ġvector Ġrepresentation Ġof Ġtitles Ġwith Ġvisual Ġfeatures Ġcould Ġimprove Ġthe Ġoverall Ġperformance .
Introduction The distribution of textual content is typically very fast and catches user attention for only a short period of time BIBREF0 . For this reason, proper wording of the article title may play a significant role in determining the future popularity of the article. The reflection of this phenomenon is the proliferation of click-baits - short snippets of text whose main purpose is to encourage viewers to click on the link embedded in the snippet. Although detection of click-baits is a separate research topic BIBREF1 , in this paper we address a more general problem of predicting popularity of online content based solely on its title. Predicting popularity in the Internet is a challenging and non-trivial task due to a multitude of factors impacting the distribution of the information: external context, social network of the publishing party, relevance of the video to the final user, etc. This topic has therefore attracted a lot of attention from the research community BIBREF2 , BIBREF3 , BIBREF0 , BIBREF4 . In this paper we propose a method for online content popularity prediction based on a bidirectional recurrent neural network called BiLSTM. This work is inspired by recent successful applications of deep neural networks in many natural language processing problems BIBREF5 , BIBREF6 . Our method attempts to model complex relationships between the title of an article and its popularity using novel deep network architecture that, in contrast to the previous approaches, gives highly interpretable results. Last but not least, the proposed BiLSTM method provides a significant performance boost in terms of prediction accuracy over the standard shallow approach, while outperforming the current state-of-the-art on two distinct datasets with over 40,000 samples. To summarize, the contributions presented in this paper are the following: The remainder of this paper is organized in the following manner: first, we review the relevant literature and compare our approach to existing work. Next, we formulate the problem of popularity prediction and propose a model that takes advantage of BiLSTM architecture to address it. Then, we evaluate our model on two datasets using several pre-trained word embeddings and compare it to benchmark models. We conclude this work with discussion on future research paths. Related Work The ever increasing popularity of the Internet as a virtual space to share content inspired research community to analyze different aspects of online information distribution. Various types of content were analyzed, ranging from textual data, such as Twitter posts BIBREF0 or Digg stories BIBREF2 to images BIBREF7 to videos BIBREF8 , BIBREF3 , BIBREF9 . Although several similarities were observed across content domains, e.g. log-normal distribution of data popularity BIBREF10 , in this work we focus only on textual content and, more precisely, on the popularity of news articles and its relation to the article's title. Forecasting popularity of news articles was especially well studied in the context of Twitter - a social media platform designed specifically for sharing textual data BIBREF11 , BIBREF12 . Not only did the previous works focus on the prediction part, but also on modeling message propagation within the network BIBREF13 . However, most of the works were focused on analyzing the social interactions between the users and the characteristics of so-called social graph of users' connections, rather than on the textual features. Contrary to those approaches, in this paper we base our predictions using only textual features of the article title. We also validate our proposed method on one dataset collected using a different social media platform, namely Facebook, and another one created from various news articles BIBREF4 . Recently, several works have touched on the topic of popularity prediction of news article from a multimodal perspective BIBREF4 , BIBREF14 . Although in BIBREF4 the authors analyze news articles on a per-modality basis, they do not approach the problem of popularity prediction in a holistic way. To address this shortcoming, BIBREF14 have proposed a multimodal approach to predicting popularity of short videos shares in social media platform Vine using a model that fuses features related to different modalities. In our work, we focus only on textual features of the article title for the purpose of popularity prediction, as our goal is to empower the journalists to quantitatively assess the quality of the headlines they create before the publication. Nevertheless, we believe that in future research we will extend our method towards multimodal popularity prediction. Method In this section we present the bidirectional LSTM model for popularity prediction. We start by formulating the problem and follow up with the description of word embeddings used in our approach. We then present the Long Short-Term Memory network that serves as a backbone for our bidirectional LSTM architecture. We conclude this section with our interpretation of hidden bidirectional states and describe how they can be employed for title introspection. Problem Formulation We cast the problem of popularity prediction as a binary classification task. We assume our data points contain a string of characters representing article title and a popularity metric, such as number of comments or views. The input of our classification is the character string, while the output is the binary label corresponding to popular or unpopular class. To enable the comparison of the methods on datasets containing content published on different websites and with different audience sizes, we determine that a video is popular if its popularity metric exceeds the median value of the corresponding metric for other points in the set, otherwise - it is labeled as unpopular. The details of the labeling procedure are discussed separately in the Datasets section. Text Representation Since the input of our method is textual data, we follow the approach of BIBREF15 and map the text into a fixed-size vector representation. To this end, we use word embeddings that were successfully applied in other domains. We follow BIBREF5 and use pre-trained GloVe word vectors BIBREF16 to initialize the embedding layer (also known as look-up table). Section SECREF18 discusses the embedding layer in more details. Bidirectional Long Short-Term Memory Network Our method for popularity prediction using article's title is inspired by a bidirectional LSTM architecture. The overview of the model can be seen in Fig. FIGREF8 . Let INLINEFORM0 be INLINEFORM1 -dimensional word vector corresponding to the INLINEFORM2 -the word in the headline, then a variable length sequence: INLINEFORM3 represents a headline. A recurrent neural network (RNN) processes this sequence by recursively applying a transformation function to the current element of sequence INLINEFORM4 and its previous hidden internal state INLINEFORM5 (optionally outputting INLINEFORM6 ). At each time step INLINEFORM7 , the hidden state is updated by: DISPLAYFORM0 where INLINEFORM0 is a non-linear activation function. LSTM network BIBREF17 updates its internal state differently, at each step INLINEFORM1 it calculates: DISPLAYFORM0 where INLINEFORM0 is the sigmoid activation function, tanh is the hyperbolic tangent function and INLINEFORM1 denotes component-wise multiplication. In our experiments we used 128, 256 for the dimensionality of hidden layer in both LSTM and BiLSTM. The term in equation EQREF10 INLINEFORM2 , is called the input gate and it uses the input word and the past hidden state to determine whether the input is worth remembering or not. The amount of information that is being discarded is controlled by forget gate INLINEFORM3 , while INLINEFORM4 is the output gate that controls the amount of information that leaks from memory cell INLINEFORM5 to the hidden state INLINEFORM6 . In the context of classification, we typically treat the output of the hidden state at the last time step of LSTM as the document representation and feed it to sigmoid layer to perform classification BIBREF18 . Due to its sequential nature, a recurrent neural network puts more emphasis on the recent elements. To circumvent this problem BIBREF19 introduced a bidirectional RNN in which each training sequence is presented forwards and backwards to two separate recurrent nets, both of which are connected to the same output layer. Therefore, at any time-step we have the whole information about the sequence. This is shown by the following equation: DISPLAYFORM0 In our method, we use the bidirectional LSTM architecture for content popularity prediction using only textual cues. We have to therefore map the neural network outputs from a set of hidden states INLINEFORM0 to classification labels. We evaluated several approaches to this problem, such as max or mean pooling. The initial experiments showed that the highest performance was achieved using late fusion approach, that is by concatenating the last hidden state in forward and backward sequence. The intuition behind this design choice is that the importance of the first few words of the headline is relatively high, as the information contained in INLINEFORM1 , i.e. the last item in the backward sequence, is mostly taken from the first word. Hidden State Interpretation One interesting property of bidirectional RNNs is the fact, that the concatenation of hidden states INLINEFORM0 and INLINEFORM1 can be interpreted as a context-dependent vector representation of word INLINEFORM2 . This allows us to introspect a given title and approximate the contribution of each word to the estimated popularity. To that end one can process the headline representation INLINEFORM3 through the bidirectional recurrent network and then retrieve pairs of forward and backwards hidden state INLINEFORM4 for each word INLINEFORM5 . Then, the output of the last fully-connected layer INLINEFORM6 could be interpreted as context-depended popularity of a word INLINEFORM7 . Training In our experiments we minimize the binary cross-entropy loss using Stochastic Gradient Descent on randomly shuffled mini-batches with the Adam optimization algorithm BIBREF20 . We reduce the learning rate by a factor of 0.2 once learning plateaus. We also employ early stopping strategy, i.e. stopping the training algorithm before convergence based on the values of loss function on the validation set. Evaluation In this section, we evaluate our method and compare its performance against the competitive approaches. We use INLINEFORM0 -fold evaluation protocol with INLINEFORM1 with random dataset split. We measure the performance using standard accuracy metric which we define as a ratio between correctly classified data samples from test dataset and all test samples. Datasets In this section we present two datasets used in our experiments: The NowThisNews dataset, collected for the purpose of this paper, and The BreakingNews dataset BIBREF4 , publicly available dataset of news articles. contains 4090 posts with associated videos from NowThisNews Facebook page collected between 07/2015 and 07/2016. For each post we collected its title and the number of views of the corresponding video, which we consider our popularity metric. Due to a fairly lengthy data collection process, we decided to normalize our data by first grouping posts according to their publication month and then labeling the posts for which the popularity metric exceeds the median monthly value as popular, the remaining part as unpopular. BIBREF4 contains a variety of news-related information such as images, captions, geo-location information and comments which could be used as a proxy for article popularity. The articles in this dataset were collected between January and December 2014. Although we tried to retrieve the entire dataset, we were able to download only 38,182 articles due to the dead links published in the dataset. The retrieved articles were published in main news channels, such as Yahoo News, The Guardian or The Washington Post. Similarly, to The NowThisNews dataset we normalize the data by grouping articles per publisher, and classifying them as popular, when the number of comments exceeds the median value for given publisher. Baselines As a first baseline we use Bag-of-Words, a well-known and robust text representations used in various domains BIBREF21 , combined with a standard shallow classifier, namely, a Support Vector Machine with linear kernel. We used LIBSVM implementation of SVM. Our second baseline is a deep Convectional Neural Network applied on word embeddings. This baseline represents state-of-the-art method presented in BIBREF4 with minor adjustments to the binary classification task. The architecture of the CNN benchmark we use is the following: the embedding layer transforms one-hot encoded words to their dense vector representations, followed by the convolution layer of 256 filters with width equal to 5 followed by max pooling layer (repeated three times), fully-connected layer with dropout and INLINEFORM0 regularization and finally, sigmoid activation layer. For fair comparison, both baselines were trained using the same training procedure as our method. Embeddings As a text embedding in our experiments, we use publicly available GloVe word vectors BIBREF16 pre-trained on two datasets: Wikipedia 2014 with Gigaword5 (W+G5) and Common Crawl (CC). Since their output dimensionality can be modified, we show the results for varying dimensionality sizes. On top of that, we evaluate two training approaches: using static word vectors and fine-tuning them during training phase. Results The results of our experiments can be seen in Tab. TABREF21 and TABREF22 . Our proposed BiLSTM approach outperforms the competing methods consistently across both datasets. The performance improvement is especially visible for The NowThisNews dataset and reaches over 15% with respect to the shallow architecture in terms of of accuracy. Although the improvement with respect to the other methods based on deep neural network is less evident, the recurrent nature of our method provides much more intuitive interpretation of the results and allow for parsing the contribution of each single word to the overall score. To present how our model works in practice, we show in Tab. TABREF23 a list of 3 headlines from NowThisNews dataset that are scored with the highest probability of belonging to a popular class, as well as 3 headlines with the lowest score. As can be seen, our model correctly detected videos that become viral at the same time assigning low score to content that underperformed. We believe that BiLSTM could be successfully applied in real-life scenarios. Conclusions In this paper we present a novel approach to the problem of online article popularity prediction. To our knowledge, this is the first attempt of predicting the performance of content on social media using only textual information from its title. We show that our method consistently outperforms benchmark models. Additionally, the proposed method could not only be used to compare competing titles with regard to their estimated probability, but also to gain insights about what constitutes a good title. Future work includes modeling popularity prediction problem with multiple data modalities, such as images or videos. Furthermore, all of the evaluated models function at the word level, which could be problematic due to idiosyncratic nature of social media and Internet content. It is, therefore, worth investigating, whether combining models that operate at the character level to learn and generate vector representation of titles with visual features could improve the overall performance.
Which shallow approaches did they experiment with?
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Which Ġshallow Ġapproaches Ġdid Ġthey Ġexperiment Ġwith ?
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<s> Which Ġshallow Ġapproaches Ġdid Ġthey Ġexperiment Ġwith ? </s> Introduction </s> The Ġdistribution Ġof Ġtextual Ġcontent Ġis Ġtypically Ġvery Ġfast Ġand Ġcatches Ġuser Ġattention Ġfor Ġonly Ġa Ġshort Ġperiod Ġof Ġtime ĠB IB REF 0 Ġ. ĠFor Ġthis Ġreason , Ġproper Ġwording Ġof Ġthe Ġarticle Ġtitle Ġmay Ġplay Ġa Ġsignificant Ġrole Ġin Ġdetermining Ġthe Ġfuture Ġpopularity Ġof Ġthe Ġarticle . ĠThe Ġreflection Ġof Ġthis Ġphenomenon Ġis Ġthe Ġproliferation Ġof Ġclick - ba its Ġ- Ġshort Ġsnippets Ġof Ġtext Ġwhose Ġmain Ġpurpose Ġis Ġto Ġencourage Ġviewers Ġto Ġclick Ġon Ġthe Ġlink Ġembedded Ġin Ġthe Ġsnippet . ĠAlthough Ġdetection Ġof Ġclick - ba its Ġis Ġa Ġseparate Ġresearch Ġtopic ĠB IB REF 1 Ġ, Ġin Ġthis Ġpaper Ġwe Ġaddress Ġa Ġmore Ġgeneral Ġproblem Ġof Ġpredicting Ġpopularity Ġof Ġonline Ġcontent Ġbased Ġsolely Ġon Ġits Ġtitle . </s> P redict ing Ġpopularity Ġin Ġthe ĠInternet Ġis Ġa Ġchallenging Ġand Ġnon - t riv ial Ġtask Ġdue Ġto Ġa Ġmultitude Ġof Ġfactors Ġimpacting Ġthe Ġdistribution Ġof Ġthe Ġinformation : Ġexternal Ġcontext , Ġsocial Ġnetwork Ġof Ġthe Ġpublishing Ġparty , Ġrelevance Ġof Ġthe Ġvideo Ġto Ġthe Ġfinal Ġuser , Ġetc . ĠThis Ġtopic Ġhas Ġtherefore Ġattracted Ġa Ġlot Ġof Ġattention Ġfrom Ġthe Ġresearch Ġcommunity ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 0 Ġ, ĠB IB REF 4 Ġ. </s> In Ġthis Ġpaper Ġwe Ġpropose Ġa Ġmethod Ġfor Ġonline Ġcontent Ġpopularity Ġprediction Ġbased Ġon Ġa Ġbid irection al Ġrecurrent Ġneural Ġnetwork Ġcalled ĠBi L ST M . ĠThis Ġwork Ġis Ġinspired Ġby Ġrecent Ġsuccessful Ġapplications Ġof Ġdeep Ġneural Ġnetworks Ġin Ġmany Ġnatural Ġlanguage Ġprocessing Ġproblems ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠOur Ġmethod Ġattempts Ġto Ġmodel Ġcomplex Ġrelationships Ġbetween Ġthe Ġtitle Ġof Ġan Ġarticle Ġand Ġits Ġpopularity Ġusing Ġnovel Ġdeep Ġnetwork Ġarchitecture Ġthat , Ġin Ġcontrast Ġto Ġthe Ġprevious Ġapproaches , Ġgives Ġhighly Ġinterpret able Ġresults . ĠLast Ġbut Ġnot Ġleast , Ġthe Ġproposed ĠBi L ST M Ġmethod Ġprovides Ġa Ġsignificant Ġperformance Ġboost Ġin Ġterms Ġof Ġprediction Ġaccuracy Ġover Ġthe Ġstandard Ġshallow Ġapproach , Ġwhile Ġoutper forming Ġthe Ġcurrent Ġstate - of - the - art Ġon Ġtwo Ġdistinct Ġdatasets Ġwith Ġover Ġ40 , 000 Ġsamples . </s> To Ġsummarize , Ġthe Ġcontributions Ġpresented Ġin Ġthis Ġpaper Ġare Ġthe Ġfollowing : </s> The Ġremainder Ġof Ġthis Ġpaper Ġis Ġorganized Ġin Ġthe Ġfollowing Ġmanner : Ġfirst , Ġwe Ġreview Ġthe Ġrelevant Ġliterature Ġand Ġcompare Ġour Ġapproach Ġto Ġexisting Ġwork . ĠNext , Ġwe Ġformulate Ġthe Ġproblem Ġof Ġpopularity Ġprediction Ġand Ġpropose Ġa Ġmodel Ġthat Ġtakes Ġadvantage Ġof ĠBi L ST M Ġarchitecture Ġto Ġaddress Ġit . ĠThen , Ġwe Ġevaluate Ġour Ġmodel Ġon Ġtwo Ġdatasets Ġusing Ġseveral Ġpre - trained Ġword Ġembed d ings Ġand Ġcompare Ġit Ġto Ġbenchmark Ġmodels . ĠWe Ġconclude Ġthis Ġwork Ġwith Ġdiscussion Ġon Ġfuture Ġresearch Ġpaths . </s> Related ĠWork </s> The Ġever Ġincreasing Ġpopularity Ġof Ġthe ĠInternet Ġas Ġa Ġvirtual Ġspace Ġto Ġshare Ġcontent Ġinspired Ġresearch Ġcommunity Ġto Ġanalyze Ġdifferent Ġaspects Ġof Ġonline Ġinformation Ġdistribution . ĠVarious Ġtypes Ġof Ġcontent Ġwere Ġanalyzed , Ġranging Ġfrom Ġtextual Ġdata , Ġsuch Ġas ĠTwitter Ġposts ĠB IB REF 0 Ġor ĠD igg Ġstories ĠB IB REF 2 Ġto Ġimages ĠB IB REF 7 Ġto Ġvideos ĠB IB REF 8 Ġ, ĠB IB REF 3 Ġ, ĠB IB REF 9 Ġ. ĠAlthough Ġseveral Ġsimilarities Ġwere Ġobserved Ġacross Ġcontent Ġdomains , Ġe . g . Ġlog - normal Ġdistribution Ġof Ġdata Ġpopularity ĠB IB REF 10 Ġ, Ġin Ġthis Ġwork Ġwe Ġfocus Ġonly Ġon Ġtextual Ġcontent Ġand , Ġmore Ġprecisely , Ġon Ġthe Ġpopularity Ġof Ġnews Ġarticles Ġand Ġits Ġrelation Ġto Ġthe Ġarticle 's Ġtitle . </s> Fore casting Ġpopularity Ġof Ġnews Ġarticles Ġwas Ġespecially Ġwell Ġstudied Ġin Ġthe Ġcontext Ġof ĠTwitter Ġ- Ġa Ġsocial Ġmedia Ġplatform Ġdesigned Ġspecifically Ġfor Ġsharing Ġtextual Ġdata ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ. ĠNot Ġonly Ġdid Ġthe Ġprevious Ġworks Ġfocus Ġon Ġthe Ġprediction Ġpart , Ġbut Ġalso Ġon Ġmodeling Ġmessage Ġpropagation Ġwithin Ġthe Ġnetwork ĠB IB REF 13 Ġ. ĠHowever , Ġmost Ġof Ġthe Ġworks Ġwere Ġfocused Ġon Ġanalyzing Ġthe Ġsocial Ġinteractions Ġbetween Ġthe Ġusers Ġand Ġthe Ġcharacteristics Ġof Ġso - called Ġsocial Ġgraph Ġof Ġusers ' Ġconnections , Ġrather Ġthan Ġon Ġthe Ġtextual Ġfeatures . ĠContrary Ġto Ġthose Ġapproaches , Ġin Ġthis Ġpaper Ġwe Ġbase Ġour Ġpredictions Ġusing Ġonly Ġtextual Ġfeatures Ġof Ġthe Ġarticle Ġtitle . ĠWe Ġalso Ġvalidate Ġour Ġproposed Ġmethod Ġon Ġone Ġdataset Ġcollected Ġusing Ġa Ġdifferent Ġsocial Ġmedia Ġplatform , Ġnamely ĠFacebook , Ġand Ġanother Ġone Ġcreated Ġfrom Ġvarious Ġnews Ġarticles ĠB IB REF 4 Ġ. </s> Recently , Ġseveral Ġworks Ġhave Ġtouched Ġon Ġthe Ġtopic Ġof Ġpopularity Ġprediction Ġof Ġnews Ġarticle Ġfrom Ġa Ġmultim od al Ġperspective ĠB IB REF 4 Ġ, ĠB IB REF 14 Ġ. ĠAlthough Ġin ĠB IB REF 4 Ġthe Ġauthors Ġanalyze Ġnews Ġarticles Ġon Ġa Ġper - mod ality Ġbasis , Ġthey Ġdo Ġnot Ġapproach Ġthe Ġproblem Ġof Ġpopularity Ġprediction Ġin Ġa Ġholistic Ġway . ĠTo Ġaddress Ġthis Ġshort coming , ĠB IB REF 14 Ġhave Ġproposed Ġa Ġmultim od al Ġapproach Ġto Ġpredicting Ġpopularity Ġof Ġshort Ġvideos Ġshares Ġin Ġsocial Ġmedia Ġplatform ĠVine Ġusing Ġa Ġmodel Ġthat Ġf uses Ġfeatures Ġrelated Ġto Ġdifferent Ġmod alities . ĠIn Ġour Ġwork , Ġwe Ġfocus Ġonly Ġon Ġtextual Ġfeatures Ġof Ġthe Ġarticle Ġtitle Ġfor Ġthe Ġpurpose Ġof Ġpopularity Ġprediction , Ġas Ġour Ġgoal Ġis Ġto Ġempower Ġthe Ġjournalists Ġto Ġquant itatively Ġassess Ġthe Ġquality Ġof Ġthe Ġheadlines Ġthey Ġcreate Ġbefore Ġthe Ġpublication . ĠNevertheless , Ġwe Ġbelieve Ġthat Ġin Ġfuture Ġresearch Ġwe Ġwill Ġextend Ġour Ġmethod Ġtowards Ġmultim od al Ġpopularity Ġprediction . </s> Method </s> In Ġthis Ġsection Ġwe Ġpresent Ġthe Ġbid irection al ĠL ST M Ġmodel Ġfor Ġpopularity Ġprediction . ĠWe Ġstart Ġby Ġform ulating Ġthe Ġproblem Ġand Ġfollow Ġup Ġwith Ġthe Ġdescription Ġof Ġword Ġembed d ings Ġused Ġin Ġour Ġapproach . ĠWe Ġthen Ġpresent Ġthe ĠLong ĠShort - Term ĠMemory Ġnetwork Ġthat Ġserves Ġas Ġa Ġbackbone Ġfor Ġour Ġbid irection al ĠL ST M Ġarchitecture . ĠWe Ġconclude Ġthis Ġsection Ġwith Ġour Ġinterpretation Ġof Ġhidden Ġbid irection al Ġstates Ġand Ġdescribe Ġhow Ġthey Ġcan Ġbe Ġemployed Ġfor Ġtitle Ġintro spection . </s> Problem ĠForm ulation </s> We Ġcast Ġthe Ġproblem Ġof Ġpopularity Ġprediction Ġas Ġa Ġbinary Ġclassification Ġtask . ĠWe Ġassume Ġour Ġdata Ġpoints Ġcontain Ġa Ġstring Ġof Ġcharacters Ġrepresenting Ġarticle Ġtitle Ġand Ġa Ġpopularity Ġmetric , Ġsuch Ġas Ġnumber Ġof Ġcomments Ġor Ġviews . ĠThe Ġinput Ġof Ġour Ġclassification Ġis Ġthe Ġcharacter Ġstring , Ġwhile Ġthe Ġoutput Ġis Ġthe Ġbinary Ġlabel Ġcorresponding Ġto Ġpopular Ġor Ġunpopular Ġclass . ĠTo Ġenable Ġthe Ġcomparison Ġof Ġthe Ġmethods Ġon Ġdatasets Ġcontaining Ġcontent Ġpublished Ġon Ġdifferent Ġwebsites Ġand Ġwith Ġdifferent Ġaudience Ġsizes , Ġwe Ġdetermine Ġthat Ġa Ġvideo Ġis Ġpopular Ġif Ġits Ġpopularity Ġmetric Ġexceeds Ġthe Ġmedian Ġvalue Ġof Ġthe Ġcorresponding Ġmetric Ġfor Ġother Ġpoints Ġin Ġthe Ġset , Ġotherwise Ġ- Ġit Ġis Ġlabeled Ġas Ġunpopular . ĠThe Ġdetails Ġof Ġthe Ġlabeling Ġprocedure Ġare Ġdiscussed Ġseparately Ġin Ġthe ĠDat as ets Ġsection . </s> Text ĠRepresent ation </s> Since Ġthe Ġinput Ġof Ġour Ġmethod Ġis Ġtextual Ġdata , Ġwe Ġfollow Ġthe Ġapproach Ġof ĠB IB REF 15 Ġand Ġmap Ġthe Ġtext Ġinto Ġa Ġfixed - size Ġvector Ġrepresentation . ĠTo Ġthis Ġend , Ġwe Ġuse Ġword Ġembed d ings Ġthat Ġwere Ġsuccessfully Ġapplied Ġin Ġother Ġdomains . ĠWe Ġfollow ĠB IB REF 5 Ġand Ġuse Ġpre - trained ĠGl o Ve Ġword Ġvectors ĠB IB REF 16 Ġto Ġinitialize Ġthe Ġembed ding Ġlayer Ġ( also Ġknown Ġas Ġlook - up Ġtable ). ĠSection ĠSEC REF 18 Ġdiscusses Ġthe Ġembed ding Ġlayer Ġin Ġmore Ġdetails . </s> B id irection al ĠLong ĠShort - Term ĠMemory ĠNetwork </s> Our Ġmethod Ġfor Ġpopularity Ġprediction Ġusing Ġarticle 's Ġtitle Ġis Ġinspired Ġby Ġa Ġbid irection al ĠL ST M Ġarchitecture . ĠThe Ġoverview Ġof Ġthe Ġmodel Ġcan Ġbe Ġseen Ġin ĠFig . ĠFIG REF 8 Ġ. </s> Let ĠIN LINE FORM 0 Ġbe ĠIN LINE FORM 1 Ġ- dimensional Ġword Ġvector Ġcorresponding Ġto Ġthe ĠIN LINE FORM 2 Ġ- the Ġword Ġin Ġthe Ġheadline , Ġthen Ġa Ġvariable Ġlength Ġsequence : ĠIN LINE FORM 3 Ġrepresents Ġa Ġheadline . ĠA Ġrecurrent Ġneural Ġnetwork Ġ( R NN ) Ġprocesses Ġthis Ġsequence Ġby Ġrec urs ively Ġapplying Ġa Ġtransformation Ġfunction Ġto Ġthe Ġcurrent Ġelement Ġof Ġsequence ĠIN LINE FORM 4 Ġand Ġits Ġprevious Ġhidden Ġinternal Ġstate ĠIN LINE FORM 5 Ġ( option ally Ġoutput ting ĠIN LINE FORM 6 Ġ). ĠAt Ġeach Ġtime Ġstep ĠIN LINE FORM 7 Ġ, Ġthe Ġhidden Ġstate Ġis Ġupdated Ġby : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġnon - linear Ġactivation Ġfunction . ĠL ST M Ġnetwork ĠB IB REF 17 Ġupdates Ġits Ġinternal Ġstate Ġdifferently , Ġat Ġeach Ġstep ĠIN LINE FORM 1 Ġit Ġcalculates : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġs igm oid Ġactivation Ġfunction , Ġtan h Ġis Ġthe Ġhyper b olic Ġtang ent Ġfunction Ġand ĠIN LINE FORM 1 Ġdenotes Ġcomponent - wise Ġmultiplication . ĠIn Ġour Ġexperiments Ġwe Ġused Ġ128 , Ġ256 Ġfor Ġthe Ġdimension ality Ġof Ġhidden Ġlayer Ġin Ġboth ĠL ST M Ġand ĠBi L ST M . ĠThe Ġterm Ġin Ġequation ĠEQ REF 10 ĠIN LINE FORM 2 Ġ, Ġis Ġcalled Ġthe Ġinput Ġgate Ġand Ġit Ġuses Ġthe Ġinput Ġword Ġand Ġthe Ġpast Ġhidden Ġstate Ġto Ġdetermine Ġwhether Ġthe Ġinput Ġis Ġworth Ġremembering Ġor Ġnot . ĠThe Ġamount Ġof Ġinformation Ġthat Ġis Ġbeing Ġdiscarded Ġis Ġcontrolled Ġby Ġforget Ġgate ĠIN LINE FORM 3 Ġ, Ġwhile ĠIN LINE FORM 4 Ġis Ġthe Ġoutput Ġgate Ġthat Ġcontrols Ġthe Ġamount Ġof Ġinformation Ġthat Ġleaks Ġfrom Ġmemory Ġcell ĠIN LINE FORM 5 Ġto Ġthe Ġhidden Ġstate ĠIN LINE FORM 6 Ġ. ĠIn Ġthe Ġcontext Ġof Ġclassification , Ġwe Ġtypically Ġtreat Ġthe Ġoutput Ġof Ġthe Ġhidden Ġstate Ġat Ġthe Ġlast Ġtime Ġstep Ġof ĠL ST M Ġas Ġthe Ġdocument Ġrepresentation Ġand Ġfeed Ġit Ġto Ġs igm oid Ġlayer Ġto Ġperform Ġclassification ĠB IB REF 18 Ġ. </s> Due Ġto Ġits Ġsequential Ġnature , Ġa Ġrecurrent Ġneural Ġnetwork Ġputs Ġmore Ġemphasis Ġon Ġthe Ġrecent Ġelements . ĠTo Ġcircumvent Ġthis Ġproblem ĠB IB REF 19 Ġintroduced Ġa Ġbid irection al ĠR NN Ġin Ġwhich Ġeach Ġtraining Ġsequence Ġis Ġpresented Ġforwards Ġand Ġbackwards Ġto Ġtwo Ġseparate Ġrecurrent Ġnets , Ġboth Ġof Ġwhich Ġare Ġconnected Ġto Ġthe Ġsame Ġoutput Ġlayer . ĠTherefore , Ġat Ġany Ġtime - step Ġwe Ġhave Ġthe Ġwhole Ġinformation Ġabout Ġthe Ġsequence . ĠThis Ġis Ġshown Ġby Ġthe Ġfollowing Ġequation : ĠDIS PLAY FORM 0 </s> In Ġour Ġmethod , Ġwe Ġuse Ġthe Ġbid irection al ĠL ST M Ġarchitecture Ġfor Ġcontent Ġpopularity Ġprediction Ġusing Ġonly Ġtextual Ġcues . ĠWe Ġhave Ġto Ġtherefore Ġmap Ġthe Ġneural Ġnetwork Ġoutputs Ġfrom Ġa Ġset Ġof Ġhidden Ġstates ĠIN LINE FORM 0 Ġto Ġclassification Ġlabels . ĠWe Ġevaluated Ġseveral Ġapproaches Ġto Ġthis Ġproblem , Ġsuch Ġas Ġmax Ġor Ġmean Ġpool ing . ĠThe Ġinitial Ġexperiments Ġshowed Ġthat Ġthe Ġhighest Ġperformance Ġwas Ġachieved Ġusing Ġlate Ġfusion Ġapproach , Ġthat Ġis Ġby Ġconc aten ating Ġthe Ġlast Ġhidden Ġstate Ġin Ġforward Ġand Ġbackward Ġsequence . ĠThe Ġintuition Ġbehind Ġthis Ġdesign Ġchoice Ġis Ġthat Ġthe Ġimportance Ġof Ġthe Ġfirst Ġfew Ġwords Ġof Ġthe Ġheadline Ġis Ġrelatively Ġhigh , Ġas Ġthe Ġinformation Ġcontained Ġin ĠIN LINE FORM 1 Ġ, Ġi . e . Ġthe Ġlast Ġitem Ġin Ġthe Ġbackward Ġsequence , Ġis Ġmostly Ġtaken Ġfrom Ġthe Ġfirst Ġword . </s> Hidden ĠState ĠInterpret ation </s> One Ġinteresting Ġproperty Ġof Ġbid irection al ĠR NN s Ġis Ġthe Ġfact , Ġthat Ġthe Ġconc aten ation Ġof Ġhidden Ġstates ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġcan Ġbe Ġinterpreted Ġas Ġa Ġcontext - dependent Ġvector Ġrepresentation Ġof Ġword ĠIN LINE FORM 2 Ġ. ĠThis Ġallows Ġus Ġto Ġintro spect Ġa Ġgiven Ġtitle Ġand Ġapproximate Ġthe Ġcontribution Ġof Ġeach Ġword Ġto Ġthe Ġestimated Ġpopularity . ĠTo Ġthat Ġend Ġone Ġcan Ġprocess Ġthe Ġheadline Ġrepresentation ĠIN LINE FORM 3 Ġthrough Ġthe Ġbid irection al Ġrecurrent Ġnetwork Ġand Ġthen Ġretrieve Ġpairs Ġof Ġforward Ġand Ġbackwards Ġhidden Ġstate ĠIN LINE FORM 4 Ġfor Ġeach Ġword ĠIN LINE FORM 5 Ġ. ĠThen , Ġthe Ġoutput Ġof Ġthe Ġlast Ġfully - connected Ġlayer ĠIN LINE FORM 6 Ġcould Ġbe Ġinterpreted Ġas Ġcontext - dep ended Ġpopularity Ġof Ġa Ġword ĠIN LINE FORM 7 Ġ. </s> Training </s> In Ġour Ġexperiments Ġwe Ġminimize Ġthe Ġbinary Ġcross - ent ropy Ġloss Ġusing ĠSt och astic ĠGrad ient ĠDes cent Ġon Ġrandomly Ġshuff led Ġmini - bat ches Ġwith Ġthe ĠAdam Ġoptimization Ġalgorithm ĠB IB REF 20 Ġ. ĠWe Ġreduce Ġthe Ġlearning Ġrate Ġby Ġa Ġfactor Ġof Ġ0 . 2 Ġonce Ġlearning Ġplate aus . ĠWe Ġalso Ġemploy Ġearly Ġstopping Ġstrategy , Ġi . e . Ġstopping Ġthe Ġtraining Ġalgorithm Ġbefore Ġconvergence Ġbased Ġon Ġthe Ġvalues Ġof Ġloss Ġfunction Ġon Ġthe Ġvalidation Ġset . </s> E val uation </s> In Ġthis Ġsection , Ġwe Ġevaluate Ġour Ġmethod Ġand Ġcompare Ġits Ġperformance Ġagainst Ġthe Ġcompetitive Ġapproaches . ĠWe Ġuse ĠIN LINE FORM 0 Ġ- fold Ġevaluation Ġprotocol Ġwith ĠIN LINE FORM 1 Ġwith Ġrandom Ġdataset Ġsplit . ĠWe Ġmeasure Ġthe Ġperformance Ġusing Ġstandard Ġaccuracy Ġmetric Ġwhich Ġwe Ġdefine Ġas Ġa Ġratio Ġbetween Ġcorrectly Ġclassified Ġdata Ġsamples Ġfrom Ġtest Ġdataset Ġand Ġall Ġtest Ġsamples . </s> Dat as ets </s> In Ġthis Ġsection Ġwe Ġpresent Ġtwo Ġdatasets Ġused Ġin Ġour Ġexperiments : ĠThe ĠNow This News Ġdataset , Ġcollected Ġfor Ġthe Ġpurpose Ġof Ġthis Ġpaper , Ġand ĠThe ĠBreaking News Ġdataset ĠB IB REF 4 Ġ, Ġpublicly Ġavailable Ġdataset Ġof Ġnews Ġarticles . </s> cont ains Ġ4090 Ġposts Ġwith Ġassociated Ġvideos Ġfrom ĠNow This News ĠFacebook Ġpage Ġcollected Ġbetween Ġ07 / 2015 Ġand Ġ07 / 2016 . ĠFor Ġeach Ġpost Ġwe Ġcollected Ġits Ġtitle Ġand Ġthe Ġnumber Ġof Ġviews Ġof Ġthe Ġcorresponding Ġvideo , Ġwhich Ġwe Ġconsider Ġour Ġpopularity Ġmetric . ĠDue Ġto Ġa Ġfairly Ġlengthy Ġdata Ġcollection Ġprocess , Ġwe Ġdecided Ġto Ġnormal ize Ġour Ġdata Ġby Ġfirst Ġgrouping Ġposts Ġaccording Ġto Ġtheir Ġpublication Ġmonth Ġand Ġthen Ġlabeling Ġthe Ġposts Ġfor Ġwhich Ġthe Ġpopularity Ġmetric Ġexceeds Ġthe Ġmedian Ġmonthly Ġvalue Ġas Ġpopular , Ġthe Ġremaining Ġpart Ġas Ġunpopular . </s> BI B REF 4 Ġcontains Ġa Ġvariety Ġof Ġnews - related Ġinformation Ġsuch Ġas Ġimages , Ġcapt ions , Ġgeo - location Ġinformation Ġand Ġcomments Ġwhich Ġcould Ġbe Ġused Ġas Ġa Ġproxy Ġfor Ġarticle Ġpopularity . ĠThe Ġarticles Ġin Ġthis Ġdataset Ġwere Ġcollected Ġbetween ĠJanuary Ġand ĠDecember Ġ2014 . ĠAlthough Ġwe Ġtried Ġto Ġretrieve Ġthe Ġentire Ġdataset , Ġwe Ġwere Ġable Ġto Ġdownload Ġonly Ġ38 , 182 Ġarticles Ġdue Ġto Ġthe Ġdead Ġlinks Ġpublished Ġin Ġthe Ġdataset . ĠThe Ġretrieved Ġarticles Ġwere Ġpublished Ġin Ġmain Ġnews Ġchannels , Ġsuch Ġas ĠYahoo ĠNews , ĠThe ĠGuardian Ġor ĠThe ĠWashington ĠPost . ĠSimilarly , Ġto ĠThe ĠNow This News Ġdataset Ġwe Ġnormal ize Ġthe Ġdata Ġby Ġgrouping Ġarticles Ġper Ġpublisher , Ġand Ġclass ifying Ġthem Ġas Ġpopular , Ġwhen Ġthe Ġnumber Ġof Ġcomments Ġexceeds Ġthe Ġmedian Ġvalue Ġfor Ġgiven Ġpublisher . </s> Bas elines </s> As Ġa Ġfirst Ġbaseline Ġwe Ġuse ĠBag - of - Words , Ġa Ġwell - known Ġand Ġrobust Ġtext Ġrepresentations Ġused Ġin Ġvarious Ġdomains ĠB IB REF 21 Ġ, Ġcombined Ġwith Ġa Ġstandard Ġshallow Ġclass ifier , Ġnamely , Ġa ĠSupport ĠVector ĠMachine Ġwith Ġlinear Ġkernel . ĠWe Ġused ĠLI BS VM Ġimplementation Ġof ĠS VM . </s> Our Ġsecond Ġbaseline Ġis Ġa Ġdeep ĠCon ve ction al ĠNeural ĠNetwork Ġapplied Ġon Ġword Ġembed d ings . ĠThis Ġbaseline Ġrepresents Ġstate - of - the - art Ġmethod Ġpresented Ġin ĠB IB REF 4 Ġwith Ġminor Ġadjustments Ġto Ġthe Ġbinary Ġclassification Ġtask . ĠThe Ġarchitecture Ġof Ġthe ĠCNN Ġbenchmark Ġwe Ġuse Ġis Ġthe Ġfollowing : Ġthe Ġembed ding Ġlayer Ġtransforms Ġone - hot Ġencoded Ġwords Ġto Ġtheir Ġdense Ġvector Ġrepresentations , Ġfollowed Ġby Ġthe Ġconv olution Ġlayer Ġof Ġ256 Ġfilters Ġwith Ġwidth Ġequal Ġto Ġ5 Ġfollowed Ġby Ġmax Ġpool ing Ġlayer Ġ( repe ated Ġthree Ġtimes ), Ġfully - connected Ġlayer Ġwith Ġdrop out Ġand ĠIN LINE FORM 0 Ġregular ization Ġand Ġfinally , Ġs igm oid Ġactivation Ġlayer . ĠFor Ġfair Ġcomparison , Ġboth Ġbas elines Ġwere Ġtrained Ġusing Ġthe Ġsame Ġtraining Ġprocedure Ġas Ġour Ġmethod . </s> Emb edd ings </s> As Ġa Ġtext Ġembed ding Ġin Ġour Ġexperiments , Ġwe Ġuse Ġpublicly Ġavailable ĠGl o Ve Ġword Ġvectors ĠB IB REF 16 Ġpre - trained Ġon Ġtwo Ġdatasets : ĠWikipedia Ġ2014 Ġwith ĠGig aw ord 5 Ġ( W + G 5 ) Ġand ĠCommon ĠC rawl Ġ( CC ). ĠSince Ġtheir Ġoutput Ġdimension ality Ġcan Ġbe Ġmodified , Ġwe Ġshow Ġthe Ġresults Ġfor Ġvarying Ġdimension ality Ġsizes . ĠOn Ġtop Ġof Ġthat , Ġwe Ġevaluate Ġtwo Ġtraining Ġapproaches : Ġusing Ġstatic Ġword Ġvectors Ġand Ġfine - tun ing Ġthem Ġduring Ġtraining Ġphase . </s> Results </s> The Ġresults Ġof Ġour Ġexperiments Ġcan Ġbe Ġseen Ġin ĠTab . ĠT AB REF 21 Ġand ĠT AB REF 22 Ġ. ĠOur Ġproposed ĠBi L ST M Ġapproach Ġoutper forms Ġthe Ġcompeting Ġmethods Ġconsistently Ġacross Ġboth Ġdatasets . ĠThe Ġperformance Ġimprovement Ġis Ġespecially Ġvisible Ġfor ĠThe ĠNow This News Ġdataset Ġand Ġreaches Ġover Ġ15 % Ġwith Ġrespect Ġto Ġthe Ġshallow Ġarchitecture Ġin Ġterms Ġof Ġof Ġaccuracy . ĠAlthough Ġthe Ġimprovement Ġwith Ġrespect Ġto Ġthe Ġother Ġmethods Ġbased Ġon Ġdeep Ġneural Ġnetwork Ġis Ġless Ġevident , Ġthe Ġrecurrent Ġnature Ġof Ġour Ġmethod Ġprovides Ġmuch Ġmore Ġintuitive Ġinterpretation Ġof Ġthe Ġresults Ġand Ġallow Ġfor Ġparsing Ġthe Ġcontribution Ġof Ġeach Ġsingle Ġword Ġto Ġthe Ġoverall Ġscore . </s> To Ġpresent Ġhow Ġour Ġmodel Ġworks Ġin Ġpractice , Ġwe Ġshow Ġin ĠTab . ĠT AB REF 23 Ġa Ġlist Ġof Ġ3 Ġheadlines Ġfrom ĠNow This News Ġdataset Ġthat Ġare Ġscored Ġwith Ġthe Ġhighest Ġprobability Ġof Ġbelonging Ġto Ġa Ġpopular Ġclass , Ġas Ġwell Ġas Ġ3 Ġheadlines Ġwith Ġthe Ġlowest Ġscore . ĠAs Ġcan Ġbe Ġseen , Ġour Ġmodel Ġcorrectly Ġdetected Ġvideos Ġthat Ġbecome Ġviral Ġat Ġthe Ġsame Ġtime Ġassigning Ġlow Ġscore Ġto Ġcontent Ġthat Ġunder per formed . ĠWe Ġbelieve Ġthat ĠBi L ST M Ġcould Ġbe Ġsuccessfully Ġapplied Ġin Ġreal - life Ġscenarios . </s> Con clusions </s> In Ġthis Ġpaper Ġwe Ġpresent Ġa Ġnovel Ġapproach Ġto Ġthe Ġproblem Ġof Ġonline Ġarticle Ġpopularity Ġprediction . ĠTo Ġour Ġknowledge , Ġthis Ġis Ġthe Ġfirst Ġattempt Ġof Ġpredicting Ġthe Ġperformance Ġof Ġcontent Ġon Ġsocial Ġmedia Ġusing Ġonly Ġtextual Ġinformation Ġfrom Ġits Ġtitle . ĠWe Ġshow Ġthat Ġour Ġmethod Ġconsistently Ġoutper forms Ġbenchmark Ġmodels . ĠAdditionally , Ġthe Ġproposed Ġmethod Ġcould Ġnot Ġonly Ġbe Ġused Ġto Ġcompare Ġcompeting Ġtitles Ġwith Ġregard Ġto Ġtheir Ġestimated Ġprobability , Ġbut Ġalso Ġto Ġgain Ġinsights Ġabout Ġwhat Ġconstitutes Ġa Ġgood Ġtitle . ĠFuture Ġwork Ġincludes Ġmodeling Ġpopularity Ġprediction Ġproblem Ġwith Ġmultiple Ġdata Ġmod alities , Ġsuch Ġas Ġimages Ġor Ġvideos . ĠFurthermore , Ġall Ġof Ġthe Ġevaluated Ġmodels Ġfunction Ġat Ġthe Ġword Ġlevel , Ġwhich Ġcould Ġbe Ġproblematic Ġdue Ġto Ġidiosyncr atic Ġnature Ġof Ġsocial Ġmedia Ġand ĠInternet Ġcontent . ĠIt Ġis , Ġtherefore , Ġworth Ġinvestigating , Ġwhether Ġcombining Ġmodels Ġthat Ġoperate Ġat Ġthe Ġcharacter Ġlevel Ġto Ġlearn Ġand Ġgenerate Ġvector Ġrepresentation Ġof Ġtitles Ġwith Ġvisual Ġfeatures Ġcould Ġimprove Ġthe Ġoverall Ġperformance .