Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

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by  Dominik Kowald

Temporal Effects on Hashtag Reuse in Twitter:
A Cognitive-Inspired Hashtag Recommendation Approach
Dominik Kowald Subhash Pujari Elisabeth Lex
Know-Center GrazUniversityofTechnology GrazUniversityofTechnology
Graz,Austria Graz,Austria Graz,Austria
[email protected] [email protected] [email protected]
ABSTRACT ternetwork. Forexample,userscanretrievetweetscreatedduring
theEuropeanfootballchampionshipbysearchingforthehashtag
Hashtagshavebecomeapowerfultoolinsocialplatformssuchas
7 #euro2016,eveniftheydonothaveasociallinktothetweetpro-
Twitter to categorize and search for content, and to spread short
1
ducers. Meanwhile,manysocialplatforms,suchasInstagramand
0 messagesacrossmembersofthesocialnetwork. Inthispaper,we
Facebook,haveadoptedhashtagsaswell.
2 studytemporalhashtagusagepracticesinTwitterwiththeaimof
designingacognitive-inspiredhashtagrecommendationalgorithm Problem. Unsurprisingly, thewidespreadacceptanceofhashtags
n
wecallBLLI,S. Ourmainideaistoincorporatetheeffectoftime hassparkedalotofresearchinthefieldofhashtagrecommenda-
a
on(i)individualhashtagreuse(i.e.,reusingownhashtags),and(ii) tions(seeSection6foraselectionofapproaches)tosupportusers
J
socialhashtagreuse(i.e.,reusinghashtags,whichhasbeenprevi- inassigningthemostdescriptivehashtagstotheirposts. Existing
5
ously used by a followee) into a predictive model. For this, we methodstypicallyutilizecollaborative, contentandtopicfeatures

turntotheBase-LevelLearning(BLL)equationfromthecognitive oftweetstorecommendhashtagstousers.Undoubtedly,thesefea-
]
R architectureACT-R,whichaccountsforthetime-dependentdecay turesplayanimportantroleinrecommendinghashtagsthatbestde-
I of item exposure in human memory. We validate BLLI,S using scribeatweet. Inthispaper,however,weareespeciallyinterested
. two crawled Twitter datasets in two evaluation scenarios: firstly, in predicting which hashtags a user will likely apply in a newly
s
c onlytemporalusagepatternsofpasthashtagassignmentsareuti- createdtweetgivenprevioushashtagassignments.
[ lized and secondly, these patterns are combined with a content- Themainproblemwewanttoaddressiswhetherwecanidentify
basedanalysisofthecurrenttweet. Inbothscenarios,wefindnot temporalusagepatternsthatinfluenceifaTwitteruserwilllikely
1 onlythattemporaleffectsplayanimportantroleforbothindividual utilizeacertainhashtaginatweet, giventhehashtagssheand/or
v andsocialhashtagreusebutalsothatBLLI,Sprovidessignificantly herfolloweeshavebeenusinginthepast. Ourgoalistodescribe
6 betterpredictionaccuracyandrankingresultsthancurrentstate-of- suchtemporalusagepatternsusingamodelfromhumanmemory
7
the-arthashtagrecommendationmethods. theoryandtodesignahashtagrecommendationalgorithmbasedon
2
that. Tothebestofourknowledge,sofar,fewstudies(e.g.,[11])
1 Keywords. Twitter; Hashtags; BLL equation; ACT-R; TF-IDF;
haveinvestigatedthewaytemporaleffectscanbeexploitedinthe
0 Recency;HashtagRecommendation;HashtagReusePrediction
hashtagrecommendationprocess.
.
1
0 1. INTRODUCTION Approachandmethods.Weproposeacognitive-inspiredhashtag
7 Overthepastyears,themicrobloggingplatformTwitterhasbe- recommendationalgorithmwecallBLLI,Sthatisbasedontempo-
1 comeoneofthemostpopularsocialnetworksontheWeb. Users ralusagepatternsofhashtagsderivedfromempiricalevidence. In
: essence,thesepatternsreflecthowaperson’sownhashtagsaswell
v canbuildanetworkoffollowerconnectionstootherTwitterusers,
ashashtagsfromthesocialnetworkareutilizedandreused. Inour
i which means that they can subscribe to content posted by their
X followees [31, 24]. Twitter was also the first social platform that approach,weutilizetheBase-LevelLearning(BLL)equationfrom
r adoptedtheconceptofhashtags,assuggestedbyChrisMessina1. the cognitive architecture ACT-R [2, 3] to model temporal usage
a Hashtagsarefreely-chosenkeywordsstartingwiththehashchar- of hashtags. The BLL equation accounts for the time-dependent
decay of item exposure in human memory. It quantifies the use-
acter “#” to annotate, categorize and contextualize Twitter posts
fulness of a piece of information (e.g., a hashtag) based on how
(i.e., tweets) [34, 13]. The advantage of hashtags is that anyone
frequentlyandhowrecentlyitwasusedbyauserinthepastand
withaninterestinahashtagcantrackitandsearchforit[38],thus
modelsthistime-dependentdecaybymeansofapower-lawdistri-
receivingcontentpostedbysomebodyoutsideoftheirownTwit-
bution. Thus, BLL takesintoconsiderationthefrequencyand
I,S
1https://twitter.com/chrismessina/status/223115412 recencyofhashtagsusedbyauserandherfolloweesinthepast.
WepresentedtheBLLequationinourpreviousworkasamodel
to recommend tags in social bookmarking systems such as Bib-
Sonomy and CiteULike [21, 20]. In the present work, we build
upontheseresultsbyadoptingtheBLLequationtomodeltheef-
fectoftimeonthereuseofindividualandsocialhashtagstobuild
our hashtag recommendation algorithm. We demonstrate the ef-
ficacy of our approach in two empirical social networks crawled
Copyrightisheldbytheauthors. fromTwitter. Thefirstsocialnetwork,termedCompScidataset,is
ACMISBN978-1-4503-2138-9. builtuponthetweetsofasampleofTwitterusers,whohavebeen
DOI:10.1145/1235
identifiedascomputerscientistsinpreviousrelatedwork[10],and Dataset US F U T HT HTAS
| | | | | | | | | | | |
their followees. The second network, termed Random dataset, is CompSci 2,551 241,225 91,776 5,649,359 1,081,403 9,161,842
builtuponthetweetsofasetofrandomlychosenTwitterusersand Random 3,466 252,219 127,112 8,157,702 1,507,773 13,628,750
theirfollowees. Weexperimentwiththesedatasetstoinvestigate
theperformanceofourhashtagrecommendationapproachintwo Table1: StatisticsofourCompSciandRandomTwitterdata-
settings: (i)tweetsofadomain-specificTwitternetwork, and(ii) sets. Here,|US|isthenumberofseedusers,|F|isthenumber
tweetsofarandomnetworkofTwitterusers. offolloweesoftheseseedusers,|U|isthenumberoftotalusers,
|T| is the number of Tweets, |HT| is the number of distinct
Contributionsandfindings. Themaincontributionsofourwork
hashtagsand|HTAS|isthenumberofhashtagassignments.
aretwo-fold.Firstly,ourpapershowsthattimehasalargeeffecton
individualaswellassocialhashtagreuseinTwitter. Specifically,
weobserveatime-dependentdecayofindividualandsocialhash- 60
52
tagreusethatfollowsapower-lawdistribution. Thisfindingpaves CompSci
%]50
the way for our idea to utilize the BLL equation as a predictive [ Random
s
modeltorecommendhashtagsfornewtweets. Thus, oursecond nt40
e
m
contributionisthatwedesign,developandevaluateapersonalized n 31
hashtagrecommendationalgorithmbasedontheBLLequationthat sig30 26
s
outWpeerfimorpmlesmcuenrrtetnhtesBtaLteL-oefq-tuhaet-iaorntainpptwrooacvhaerisa.nts,wherethefirst htaga20 15 14 20 15 13
s
one(i.e.,BLLI,S)predictsthehashtagsofausersolelybasedon Ha10 8 6
pasthashtagusage,andthesecondone(i.e.,BLL )combines
I,S,C 0
BLLI,Swithacontent-basedtweetanalysistoalsoincorporatethe individual social individual/social network external
textofthecurrentlyproposedtweetofauser. Weevaluateourap- Hashtagusagetype
proachusingstandardevaluationprotocolsandmetrics,andwefind
thatourapproachprovidessignificantlyhigherpredictionaccuracy Figure 1: Analysis of hashtag usage types in our two data-
andrankingestimatesthancurrentstate-of-the-arthashtagrecom- sets.Foreachhashtagassignment,westudywhetherthecorre-
mendation algorithms in both scenarios. We attribute this to the spondinghashtaghasbeenusedbythesameuserbeforeintime
factthatourapproach,incontrasttootherrelatedmethods,mimics (“individual”), by some of the users she follows (“social”), by
thewayhumansuseandadapthashtagsbybuildinguponinsights both(“individual/social”),byanyoneelseinthedataset(“net-
fromhumanmemorytheory(i.e.,theBLLequation). work”)orneitherofthem(“external”). Wefindthatbetween
66%and81%ofhashtagassignmentsinbothdatasetscanbe
Structureofthispaper. InSection2,wecontinuebydescribing
explainedbyindividualorsocialhashtagusage(i.e.,thesumof
thecrawlingprocedureofourtwoTwitterdatasetsandanalyzing
“individual”,“social”and“individual/social”).
hashtagusagetypesinthesedatasets.Then,inSection3,westudy
temporalusagepatternsofindividualandsocialhashtagreuse. In
Section4,wedescribetwovariantsofourapproach(i.e.,without usedtheStreamingAPIofTwitter3inOctober2015togetastream
andwiththecurrenttweet). ThisisfollowedinSection5byour oftweetsandextractedtheuser-idstogetourlistofrandomseed
evaluationmethodologyandexperimentalresults. Finally,wedis- users.Frombothuserlists,weremovealluserswithmorethan180
cussrelatedworkinthefieldinSection6andwegiveasummary followees,whichresultsin|US|=2,551seedusersfortheCompSci
ofourfindingsaswellasourfutureplansinSection7. datasetand|US|=3,466seedusersfortheRandomdataset. The
thresholdofusingamaximumof180followeesischosenbecause
the Twitter Search API only allows 180 requests per 15 minutes,
2. DATASETS whichgivesusthepossibilitytocrawlthetweetsofallfollowees
In this section, we describe the data collection procedure and ofaseeduserwithinthisreasonabletimewindow.
thetwodatasetsweuseforourstudy. Additionally,weinvestigate (b)Crawlfollowees. Next, weusethesefollowerrelationships
individualaswellassocialhashtagreusepatternsinourdatasetsas tocrawlthefolloweesF oftheseedusersinordertocreateadi-
aprerequisiteforourhashtagrecommendationapproach. rectedusernetworkforanalyzingthesocialinfluenceonhashtag
reuse. Basedonthenumberofseedusers,theaveragenumberof
Crawlingstrategyanddatasetstatistics. Inordertoaddressour followeesperseeduser|F|/|U |=94inthecaseoftheCompSci
S
researchgoals,wecrawltwodatasetsusingtheSearchAPIofTwit- datasetand72inthecaseoftheRandomdataset. Followingthese
ter2.ThefinalstatisticsofthesedatasetsareillustratedinTable1. notations, the set of followees of user u is denoted as F in the
u
Thefirstone(i.e.,CompScidataset)consistsofresearchersfrom remainderofthispaper. Overall,ourcrawlingproceduregivesus
thefieldofcomputerscienceandtheirfollowees,whilethesecond |U|=91,776totalusersfortheCompScidatasetand|U|=127,112
one(i.e.,Randomdataset)consistsofrandompeopleandtheirfol- totalusersfortheRandomdataset.
lowees. Ourideaistotestourhashtagrecommendationapproach (c)Crawltweets.Inthethirdstep,wecrawlthe200mostrecent
intwodifferentnetworksettings: (i)adomain-specificone,inour tweetsofalltheusersandremovethetweetsinwhichnohashtags
casethedomainofcomputerscientists,and(ii)amoregeneralone areused. Thethresholdofamaximumof200mostrecenttweets
consistingofrandomTwitterusers. Ourcrawlingstrategyforboth issetbecauseofanotherrestrictionoftheTwitterSearchAPIthat
datasetscomprisesofthefollowingfoursteps: only allows 200 tweets to be received per a single request. This
(a)Crawlseedusers. Westartwithidentifyingandcrawlinga crawlingprocedureresultsin|T|=5,649,359tweetsfortheComp-
listofseedusersUS foreachdataset. InthecaseoftheCompSci Scidatasetwithanaveragenumberoftweetsperuser|T|/|U|=61,
dataset,wetaketheuserswhowereidentifiedascomputerscien- and|T|=8,157,702tweetsfortheRandomdatasetwith|T|/|U|=
tists in the work of [10]. In the case of the Random dataset, we 64.Ourcrawledtweetscoveratimerangefrom2007to2015.
2https://dev.twitter.com/rest/public/search 3https://dev.twitter.com/streaming/overview
htndividualreusecountof111111100000001234567 htndividualreusecountof111111110000000012345678 htSocialreusecountof 111111000000012345 htSocialreusecountof111110000012345
I100100 101 102 103 104 I100100 101 102 103 104 10−1100 101 102 103 104 100100 101 102 103 104
Reuserecencyofhtbyu[hours] Reuserecencyofhtbyu[hours] ReuserecencyofhtbyFu[hours] ReuserecencyofhtbyFu[hours]
(a)Individualhashtagreuse (b)Individualhashtagreuse (c)Socialhashtagreuse (d)Socialhashtagreuse
CompScidataset(R2=.883) Randomdataset(R2=.894) CompScidataset(R2=.689) Randomdataset(R2=.771)
Figure2:TheeffectoftimeonindividualandsocialhashtagreusefortheCompSciandRandomdatasets(plotsareinlog-logscale).
Plots(a)and(b)showthatthemorerecentlyahashtaghtwasusedbyauseru,thehigheritsindividualreusecount(i.e.,people
tendtoreusehashtagsthathavebeenusedveryrecentlybytheirown). Plots(c)and(d)showthatthemorerecentlyauseruwas
exposedtoahashtaght,whichwasusedbyherfolloweesF ,thehigheritssocialreusecount(i.e.,peopletendtoreusehashtagsthat
u
havebeenusedrecentlyinthesocialnetwork). Additionally,wereporttheR2estimatesforthelinearfitsofthedata. Wefindthat
temporaleffectsplayanimportantroleinindividualandsocialhashtagreuseinbothdatasets.
(d) Extract hashtags. Finally, we extract the hashtags of the Temporaleffectsonindividualhashtagreuse.Theeffectoftime
tweets by searching for all words that start with a “#” character. onindividualhashtagreuseisvisualizedintheplots(a)and(b)of
Thisresultsin|HTAS|=9,161,842hashtagassignmentsfor|HT| Figure2.Toputthex-scaleoftheseplotsontoameaningfulrange,
=1,081,403distincthashtagsintheCompScinetworkand|HTAS| we set the threshold for the maximum hashtag reuse recency to
=13,628,750for|HT|=1,507,773intheRandomnetwork.Thus, oneyear(i.e.,8,760hours). Theplotsshowtheindividualhashtag
inbothdatasets,eachdistincthashtagisusedapproximately9times reusecountplottedoverthereuserecencyofahashtaghtbyauser
onaverageandeachuserusesapproximately100hashtagassign- uinhours.Hence,foreachhashtagassignmentofahashtaghtby
mentsinhertweetsonaverage.Examplesforpopularhashtagsare useru, wetakethetimesincethelastusageofhtbyu(i.e., the
#bigdata,#iotand#uxincaseoftheCompScidataset,and#shah- reuserecency)andpooltogetherallhashtagassignmentswiththe
bag,#ff and#artincaseoftheRandomdataset. samerecencyvalue(i.e.,thesametimedifferenceinhours). The
individualreusecountforthisrecencyvalueisthengivenbythe
Analysisofhashtagusagetypes.Inourdatasets,weanalyzehash-
sizeofthesetofthesehashtagassignments.
tagassignmentsaswellashashtagreusepracticeswiththeaimof
Thetwoplotsshowsimilarresultsforbothdatasetsandindicate
identifyingthedifferenttypesofhashtagusagesasaprerequisite
that the more recently a hashtag ht was used by a user u in the
forourrecommendationapproach. Specifically, foreachhashtag
past,thehigheritsindividualreusecountis. Interestingly,thereis
assignment, we study whether the corresponding hashtag has ei-
aclearpeakafter24hoursinbothdatasets,whichfurtherindicates
therbeenusedbythesameuserbefore(“individual”),bysomeof
thatuserstypicallyusethesamesetofhashtagsinthistimespan
herfollowees(“social”), byboth(“individual/social”), byanyone
andthus,tendtotweetaboutsimilartopicsonadailybasis. Fur-
elseinthedataset(“network”)orbyneitherofthem(“external”).
thermore,wealsoobservehighR2valuesofnearly.9forthelinear
TheresultsofthisstudyareshowninFigure1.Wefindthat66%
fitsinthelog-logscaledplots,whichindicatesthatalargeamount
ofhashtagassignmentsintheCompScidatasetand81%intheRan-
ofourdatacanbeexplainedbyapowerfunction.Thisisalsosug-
domdatasetcanbeexplainedbyindividualorsocialhashtagreuse.
gestedbythepower-law-basedmodeloftheBLLequation[3,2].
This finding further corroborates our choice to utilize these two
Incontrast,thelinearfitsinlog-linearscaledplotsonlyprovideR2
typesofinfluences(i.e.,individualandsocial)tocreateourmodel.
valuesofapproximately.7,wherehighvalueswouldspeakinfavor
Incontrasttotheselargenumbers,the6%to8%ofhashtagsinthe
ofanexponentialfunction.
“network”categoryisrelativelysmall.Interestingly,theamountof
“external” hashtags is twice as high in the CompSci dataset (i.e., Temporal effects on social hashtag reuse. Plots (c) and (d) of
26%)asintheRandomone(i.e.,13%).Thus,inourdatasets,com- Figure2showtheeffectoftimeonthesocialhashtagreuseforthe
puter scientists tend to use more hashtags, which have not been CompSciandRandomdatasets. Theseplotsarecreatedsimilarly
previously introduced in the network, than random Twitter users. asplots(a)and(b)butthistime,weplotthesocialhashtagreuse
Becauseofthis,webelievethattherecommendationaccuracyre- countoverthereuserecencyofahashtaghtbythefolloweesF of
u
sultswouldgenerallybelowerintheCompScidatasetthaninthe useru.Hence,foreachhashtagassignmentofhtbyu,wetakethe
Randomone,whichwillbeevaluatedinSection5. Summingup, mostrecentusagetimestampofhtbyF . Thedifferencebetween
u
bothindividualandsocialhashtagshaveanimpactonusers’choice thistimestampandthetimestampofthecurrentlyanalyzedhashtag
ofhashtagsforanewtweet. assignmentindicatesthetimesincethelastsocialexposureofht
tou. Again,wesetthethresholdforthemaximumhashtagreuse
3. TEMPORALEFFECTSONHASHTAG recencytooneyear(i.e.,8,760hours).
REUSEINTWITTER In these plots, we observe similar results for the two datasets
since, in both cases, the more recently a user was exposed to a
Inthissection,westudytowhatextenttemporaleffectsplaya hashtag,thehigheritssocialreusecountis. Furthermore,thereis
roleinthereuseofindividualandsocialhashtagsinourtwodata- again(i)aclearpeakafter24hours,and(ii)theR2 valuesforthe
sets(i.e.,CompSciandRandom). Specifically,weanalyzetherec- linearfitsinthelog-logscaledplots(i.e.,=.7)arelargerthaninthe
encyofhashtagsassignments(i.e.,thetimesincethelasthashtag log-linearscaledplots(i.e.,=.4),whichspeaksinfavorofapower
usage/exposure), aswellaswhetherthiseffectoftime-dependent function.Wenowstudyifthisisreallythecase.
decayfollowsapower-laworexponentialdistribution.
Dataset Parameter Individualhtreuse Socialhtreuse
Scenario 1: Hashtag rec. w/o current tweet Hybrid combination
xmin 141 1 User Hashtags of u Individual reuse
CompSci α 1.699 1.242 u HTu BLLI Individual reuse
R 188 164 BLL equation + social reuse
Random xαmin 11.74213 1.2169 FollFowuees HashHtaTgFs uof Fu SocBiaLl LreSuse BLLI,S
R 235 294
Scenario 2: Hashtag rec. w/ current tweet
Individual reuse
Table2: Power-lawvs. exponentialtime-dependentdecay. We Current tweet All tweets Content analysis + social reuse
t T C + content analysis
seethatapowerfunctionprovidesabetterfitthananexponen- TF-IDF BLLI,S,C
tial function (R > 0) for explaining temporal effects on indi- Terms in t Similar tweets Hashtags of St
vidualandsocialhashtagreuseinourtwodatasets(p<.001). Ct St HTSt Hybrid combination
Power-lawvs. exponentialtime-dependentdecay. Thequestion Figure 3: Schematic illustration of our cognitive-inspired ap-
whetherapoweroranexponentialfunctionisbettersuitedtomodel proachforhashtagrecommendations. Weimplementourap-
thetime-dependentdecayofhashtagreuseisofinterestespecially proach in two scenarios (i.e., without and with incorporating
forthedesignofourhashtagrecommendationapproachsinceboth thecontentofthecurrenttweet).InScenario1,weusetheBLL
types of functions have been used in the area of time-aware rec- equation to realize (i) the individual BLLI algorithm, (ii) the
ommendersystems. WhiletheBLLequationsuggeststheuseof socialBLLS algorithm,and(iii)thehybridBLLI,S algorithm,
a power function to model the decay of item exposure in human whichcombinesboth.InScenario2,weuseTF-IDFtoidentify
memory[3],relatedhashtagrecommenderapproaches,suchasthe similartweetsforacurrentlyproposedtweettandidentifythe
oneproposedin[11],useanexponentialfunctionforthispurpose. hashtags of the most similar ones. We combine this content-
Asalreadymentioned,thevisualinspectionofFigure2andtheR2 basedtweetanalysiswithourBLLI,S methodtoprovideper-
valuesofthelinearfitsfavorapowerfunction. However, [5]has sonalizedandcontent-awarehashtagrecommendationsinthe
shownthatthisleastsquares-basedmethodcanleadtomisinterpre- formofourhybridBLLI,S,C approach.
tationsandthus,alikelihoodratio-basedtestissuggested.
WeusethePythonimplementation[1]ofthemethoddescribed toforeseethetopicsaspecificuserwilltweetaboutbasedonthe
in[5]tovalidateifapowerfunctionproducesabetterfitthanan predictedhashtags,whereasthesecondoneaimstosupportauser
exponentialone. TheresultsofthistestareshowninTable2. The infindingthemostdescriptivehashtagsforanewtweettext[9].
mainvalueofinteresthereisthelog-likelihoodratioRbetweenthe For reasons of reproducibility, we implement and evaluate our
twofunctions. Aswesee,R > 0inallfourcaseswithp < .001. approach by extending our open-source tag recommender bench-
Thismeansthatthepowerfunctionindeedprovidesabetterfitthan marking framework TagRec. The source code and framework is
the exponential function for explaining temporal effects on indi- freelyaccessibleforscientificpurposesontheWeb4.
vidualandsocialhashtagreuse. Wealsoprovidethex andα
min
valuesofthefits.Inthisrespect,theαslopescanbeusedtosetthe 4.1 Scenario1: Hashtagrec. w/ocurrenttweet
dparameteroftheBLLequation(i.e.,1.7intheindividualcaseand
For the first variant of our approach, we ignore the content of
1.25inthesocialcase, seeSection4). Interestingly, thesevalues
the current tweet t and solely utilize past hashtag usages. As al-
are much higher than the suggested value of BLL’s d parameter,
readystated,weusetheBLLequationcomingfromthecognitive
whichis.5[2]. Webelievethatthisisthecasebecausetweeting
architecture ACT-R [2, 3] for this task. We go for a cognitive-
is more strongly influenced by temporal interest drifts than other
inspiredapproach,sinceweknowfromresearchontheunderlying
applicationsstudiedintheACT-Rcommunity(e.g.,[3]).
mechanisms of social tagging that the way users choose tags for
annotatingresources(e.g.,Weblinks)stronglycorrespondstopro-
Finding1:Temporaleffectshaveanimportantinfluenceonboth
cessesinhumanmemoryanditscognitivestructures[6,36]. The
individualaswellassocialhashtagreuse: peopletendtoreuse
BLLequationquantifiesthegeneralusefulnessofapieceofinfor-
hashtags that were used very recently by their own and/or by
mation(e.g.,awordorhashtag)byconsideringhowfrequentlyand
theirTwitterfollowees. Furthermore,apowerfunctionisbetter
recentlyitwasusedbyauserinthepast.Formally,itisgivenby:
suited to model this time-dependent decay than an exponential
one. ThissuggeststhattheBLLequationfromthecognitivear- n
chitectureACT-Rshouldbeasuitablemodelfordesigningour B =ln((cid:88)t−d) (1)
i j
time-dependenthashtagrecommendationalgorithm. j=1
where B is the base-level activation of a memory unit i and n
i
4. ACOGNITIVE-INSPIREDHASHTAG
is the frequency of i’s occurrences in the past (i.e., how often i
RECOMMENDATIONAPPROACH wasusedbyu). Furthermore,t statestherecency(i.e.,thetime
j
sincethejthoccurrenceofi)andtheexponentdaccountsforthe
Intheprevioussection,wehaveshownthattemporaleffectsare
power-lawoftime-dependentdecay. AsvisualizedinScenario1
importantfactorswhenusersreuseindividualandsocialhashtags.
ofFigure3,weadopttheBLLequationfor(i)modelingthereuse
Inthissection,weusetheseinsightsasabasistodesignourhashtag
of individual hashtags (BLL ), (ii) modeling the reuse of social
recommendationapproachillustratedinFigure3. Thus,wedistin- I
hashtags(BLL ),and(iii)combiningtheformertwointoahybrid
guishbetweenhashtagrecommendationswithout(Scenario1)and S
recommendationapproach(BLL ).
with(Scenario2)incorporatingthecurrenttweett. I,S
Whereas the first variant of our approach solely uses the past Modelingindividualhashtagreuse.Inordertomodelthereuseof
hashtags of a user u and/or her followees Fu, the second variant individualhashtags,wedefinetheindividualbase-levelactivation
alsoutilizesthetextofthecurrenttweett. Hence,thesetwosce-
nariosalsodifferintheirpossibleusecasessincethefirstoneaims 4https://github.com/learning-layers/TagRec
B (ht,u)ofahashtaghtforauseruasfollows: Content-basedtweetanalysis. Weanalyzethecontentoftweets
I
inordertofindsimilartweetsforatargettweettandtoextractthe
n
B (ht,u)=ln((cid:88)(TS −TS )−dI) (2) hashtagsofthesesimilarones. Therefore,weincorporatetheterm
I ref ht,u,j
frequency-inverse document frequency (TF-IDF) statistic, which
j=1
identifiestheimportanceofatermforadocumentinacollectionof
wherendenotesthenumberoftimeshtwasusedbyuinthepast
documents. TF-IDFcanbefurtherusedtocalculatethesimilarity
(i.e.,|HTAS |)andthetermTS −TS statestherec-
ht,u ref ht,u,j betweentwodocumentsdanddbysumminguptheTF-IDFstatis-
ency of the jth usage of ht by u. In this respect, TS is the
ref ticsofd’stermsind. WhenapplyingthisstatistictoTwitter, we
referencetimestamp(i.e.,whenrecommendationsshouldbecalcu-
treattweetsasdocumentsandcalculatethesimilaritybetweenthe
lated)andTS isthetimestampwhenhtwasusedbyuforthe
ht,u,j targettweettandacandidatetweettasfollows:
jthtime.BasedontheresultsofouranalysispresentedinTable2,
wesettheindividualtime-dependentdecayfactord to1.7. (cid:88) |T|
I sim(t,t)= n ×log( ) (6)
c,t |{t(cid:48) :c∈t(cid:48)}|
Modeling social hashtag reuse. We model the reuse of social c∈Ct
hashtagsinasimilarwaybutinsteadofanalyzinghowfrequently
where C are the terms in the text of target tweet t, n is the
andrecentlyahashtaghtwasusedbyuseru,weanalyzehowfre- t c,t
numberoftimesc∈C occursinthecandidatetweett,|T|isthe
quentlyandrecentlyhtwasusedbythesetoffolloweesF ofu. t
u numberoftweetsinthedatasetand|{t(cid:48) :c∈t(cid:48)}|isthenumberof
Thus,weformulatethesocialbase-levelactivationB (ht,u)ofht
S timescoccursinanytweett(cid:48) ∈T.Thefirstfactorofthisequation
foruasfollows:
reflectsthetermfrequencyTF,whereasthesecondfactorreflects
m
B (ht,u)=ln((cid:88)(TS −TS )−dS) (3) theinversedocumentfrequencyIDF [45].
S ref ht,Fu,j Based on these similarity values, we identify the most similar
j=1 tweetsS fortandextractthehashtagsusedinthesetweets(i.e.,
t
wheremisthenumberoftimeshtwasusedbyFubeforetheref- HTSt). Foreachhashtaght ∈ HTSt,weassignacontent-based
erencetimestampTSref (i.e.,|HTASht,Fu|).ThetermTSref − score CB(ht,t), which is the highest similarity value within the
TSht,Fu,j statestherecencyofthejthexposureofhttoucaused most similar tweets St in which ht occurs. We implement this
byFu,whereTSht,Fu,jisthetimestampwhenhtwasusedbyFu methodusingtheLucene-basedfull-textsearchengineApacheSolr
forthejthtime. Aswhenmodelingtheindividualhashtagreuse, 4.7.105. BasedonSolr’ssoftwaredocumentationandourownex-
wesetthesocialtime-dependentdecayfactordS basedonthere- perimentation,wesettheminimumtermfrequencytf to2andthe
sultsofouranalysisinTable2(i.e.,to1.25). minimumdocumentfrequencydf to5.
Combiningindividualandsocialhashtagreuse.Aswehavefor- Combining personalized and content-aware hashtag rec. We
malizedtheindividualaswellassocialhashtagreuse,wewantto combineourpersonalizedBLL approachwiththiscontent-based
I,S
mixbothcomponentsinformofahybridapproachusingalinear analysis(C)inordertogeneratepersonalizedhashtagrecommen-
combination [16]. Hence, in order to be able to add the individ- dations(seeFigure3). Again,weachievethisviaalinearcombi-
ualandsocialbase-levelactivationsBI(ht,u)andBS(ht,u),we nationofbothapproaches.Takentogether,thetop-krecommended
havetomapthesevaluesontoacommonrangeof0to1thatadd hashtagsH(cid:103)Tu,tforuseruandtweettaregivenby:
upto1. Therefore,wedefinethesoftmaxfunctionsσ(B (ht,u))
I
andσ(BS(ht,u))asproposedby[30,21].Thisisgivenby: H(cid:103)Tu,t = argkmax(λBI,S(ht,u)+(1−λ)σ(CB(ht,t))) (7)
exp(B (t,u)) ht∈HTu,t (cid:124) (cid:123)(cid:122) (cid:125) (cid:124) (cid:123)(cid:122) (cid:125)
σ(BI(ht,u))= (cid:80) expI(B (ht(cid:48),u)) (4) BLLI,S C
I
ht(cid:48)∈HTu where HTu,t is the set of candidate hashtags for u and t (i.e.,
HT ∪HT ∪HT ). Theλparameterisusedtogiveweights
whereHT isthesetofdistincthashtagsusedbyu.ForB (ht,u), u Fu St
u S to the personalized and content-aware components. To that end,
thesoftmaxfunctionσ(B (ht,u))canbecalculatedinthesame
S we set λ to .3 based on experimentation. Please note that the
waybutonthebasisofHT (i.e.,thesetofhashtagsusedbyu’s
Fu content-basedscoreCB(ht,t)hastobenormalizedusingthesoft-
followeesF ). Takentogether,thecombinedbase-levelactivation
u maxfunction(seeEquation4),whereasB (ht,u)isalreadynor-
B forourBLL approachisgivenby: I,S
I,S I,S malized(seeEquation5). Thisfinallyconstitutesourpersonalized
BI,S(ht,u)=βσ(BI(ht,u))+(1−β)σ(BS(ht,u)) (5) hashtagrecommendationalgorithmtermedBLLI,S,C.
(cid:124) (cid:123)(cid:122) (cid:125) (cid:124) (cid:123)(cid:122) (cid:125)
BLLI BLLS 5. EVALUATION
wheretheβparametercanbeusedtogiveweightstothetwocom-
ponents.Basedonexperimentation,wesetβto.5toequallyweigh Inthissection,wepresenttheevaluationofourapproach. This
theindividualandsocialinfluence.AsindicatedinEquation5and includes the methodology used as well as the results in terms of
Figure3, wecanalsocalculatepredictionseithersolelybasedon recommendationaccuracyandrankingforourtwoscenarios.
theindividualhashtagreuse,referredasBLL ,orthesocialhash-
I 5.1 Methodology
tagreuse,referredasBLL .
S
The methodology of our evaluation is given by the evaluation
4.2 Scenario2: Hashtagrec. w/currenttweet
protocol,evaluationmetricsandbaselinealgorithmsused.
AsshowninScenario2ofFigure3, thesecondvariantofour
Evaluationprotocol.Inordertosplitourdatasetsintotrainingand
approachaimstoprovidehashtagsuggestionswhilealsoincorpo-
test sets, we use an established leave-one-out evaluation protocol
ratingthecontentofthecurrentlyproposedtweett.Thus,webuild
fromresearchoninformationretrievalandrecommendersystems
ontheunpersonalizedmethodproposedby[45]tofindhashtagsof
[16].Foreachseeduserinourdatasets(seeSection2)withatleast
similartweetsandcombinethismethodwithourBLL approach
I,S
togeneratepersonalizedandcontent-awarerecommendations. 5http://lucene.apache.org/solr/
twotweets(i.e.,2,020usersintheCompScidatasetand2,679users standardFRimplementationprovidedbytheUniversityofKassel7
intheRandomdataset),wedeterminehermostrecenttweetandput withitssuggesteddefaultparameters.Morespecifically,theweight
it(anditshashtags)intothetestset.Theremainingtweetsarethen ofthepreferencevectordissetto.7andthemaximumnumberof
put into the training set. This protocol ensures not only that the iterationslissetto10[16].
hashtagsofatleastonetweetperuserareavailablefortrainingbut CF. User-based Collaborative Filtering is a well-known algo-
alsothatthechronologicalorderofthedataispreserved(i.e.,future rithmusedinmanyvariantsofmodernrecommendersystemsand
hashtagsarepredictedbasedonusagepatternsofpastones). We was adapted by [29] for use in tag-based settings. We apply the
usethesesetsintwoevaluationscenarios: same idea for the task of recommending hashtags and thus, first
Scenario 1. In the first scenario, we ignore the content of the identify the k most similar users (i.e., the nearest neighbors) for
currently proposed tweet (i.e., the one in the test set) and solely currentuserubymeansofthecosinesimilaritymeasureandthen
providehashtagpredictionsbasedonthecurrentuser-id. Thus,in suggestthehashtagsusedbytheseneighbors.Forourexperiments,
Scenario1,weareabletoevaluatealltestsettweets. weuseaneighborhoodsizekof20users(seealso[8]).
Scenario2.Inthesecondscenario,wealsoincorporatethecon- SR.SimilarityRank isanunpersonalizedhashtagrecommenda-
tentofthecurrenttweet. Inthissetting,weonlyevaluatethetest tionalgorithm,whichutilizesthecontentofthecurrentlyproposed
setentries,whichdonotincluderetweets(i.e.,954testsettweets tweett[45]. SimilarlytoourBLL approach,thisisachieved
I,S,C
in the CompSci dataset and 1,504 test set tweets in the Random usingTF-IDFtodeterminecontent-basedsimilarityscoresbetween
dataset). The reason for excluding the retweets from the test set tweets(seeSection4.2). Thesescoresareusedtorecommendthe
in Scenario 2 is that searching for similar tweets in the training khashtagsthatoccurint’smostsimilartweets.
setwouldresultinidenticaltweetswithidenticalhashtags,which TCI.TemporalCombIntisoneofthemostrecentapproachesfor
wouldheavilybiasourevaluation(seealso[45]). personalized hashtag recommendations and also one of the very
fewapproachesthataccountsfortheeffectoftimeonhashtagus-
Evaluation metrics. To finally quantify the quality of the algo-
age[11](seealsoSection6). TCIbuildsonalinearcombination
rithms,foreachtestsetentry,wecomparethetop-10hashtagsan
ofSRandCFandincorporatestemporaleffectsbyconsideringthe
algorithm predicts for the given user u and tweet t (i.e., H(cid:103)Tu,t) time-dependentrelevanceofahashtagwithrespecttotherecom-
withthesetofrelevanthashtagsactuallyusedbyuint.
mendationdate.Thisisdonebycategorizingthehashtagsinto“or-
Thiscomparisonisdoneusingvariousevaluationmetricsknown
ganizational”and“conversational”hashtags,andmodelingthede-
from the field of recommender systems. Specifically, we report
cayoftemporalrelevanceusinganexponentialfunction.Byfitting
Precision (P) and Recall (R) for k = 1 to 10 predicted hashtags
thismodeltoourcrawleddata,wesetthetwomainparametersof
bymeansofPrecision/Recallplots,andF1-score(F1@5)fork =
thealgorithm,η andη ,to.1and.2,respectively.
l h
5 predicted hashtags. We set k = 5 for the F1-score since F1@5
was also used as the main evaluation metric in the well-known 5.2 ResultsandDiscussion
ECMLPKDD2009discoverychallenge6. Additionally,wereport
InSection3,wefoundthattimeisanimportantfactorforhash-
theranking-dependentmetricsMeanReciprocalRank(MRR@10),
tag reuse. Because of this, we assume that our time-dependent
MeanAveragePrecision(MAP@10)andNormalizedDiscounted
andcognitive-inspiredapproachshouldprovidereasonableresults
CumulativeGain(nDCG@10)fork=10predictedhashtags[14].
comparedtootheralgorithms. Theaccuracyestimatesforourtwo
Baselinealgorithms. Wecompareourapproachtoarichsetof9 evaluationscenariosareshowninTable3andFigure4.
state-of-the-arthashtagrecommendationalgorithms:
Scenario1: Hashtagrec. w/ocurrenttweet. Inourfirstevalu-
MP . The Most Popular Individual Hashtags algorithm ranks
I ationscenario,wevalidateapproachesthatpredictfuturehashtags
thehashtagsbasedonthefrequencyinthehashtagassignmentsof
withoutincorporatingthecontentofthecurrentlyproposedtweet.
current user u. MP is also referred to as Most Popular Tags by
I Here,weidentifythreemainresults:
User(MP )intagrecommendationliterature[16].
u (a) BLL > MP , MR . When predicting individual hashtag
MR . Most Recent Individual Hashtags is a time-dependent I I I
I reuse,wecompareourBLL approachtothefrequency-basedMP
variantofMP .MR suggeststhekmostrecentlyusedhashtagsof I I
I I andtherecency-basedMR algorithms. Theresultsclearlyreflect
currentuseru[4].OurBLL approachcanbeseenasanintegrated I
I theimportanceofthetimecomponentsinceMR andBLL pro-
combinationofMP andMR basedonhumanmemorytheory. I I
I I vide higher prediction accuracy and ranking estimates than MP
MP .TheMostPopularSocialHashtagsalgorithmisthesocial I
S forallevaluationmetricsacrossbothdatasets.Apartfromthat,we
correspondent to the individual MP approach [16]. Thus, MP
I S observethatBLL outperformsMR ,whichspeaksinfavorofthe
doesnotrankthehashtagsbasedonthefrequencyinthehashtag I I
cognitive-inspired combination of hashtag frequency and recency
assignments of user u but based on the frequency in the hashtag
bymeansoftheBLLequation.
assignmentsofuseru’ssetoffolloweesF .
u (b) BLL > MP , MR . Concerning the prediction of social
MR .MostRecentSocialHashtagsisthetime-dependentequiv- S S S
S hashtagreuse,wecompareourBLL approachtothefrequency-
alenttoMP .MR sortsthehashtagassignmentsofu’sfollowees S
S S based MP and the recency-based MR methods. Similar to the
F bytimeandrecommendsthek mostrecentones. OurBLL S S
u S caseofindividualhashtagreuse,MR andourBLL-basedmethod
algorithmisacognitive-inspiredintegrationofMP andMR . S
S S providehigheraccuracyestimatesthanthesolelyfrequency-based
MP. The unpersonalized Most Popular Hashtags approach re-
one,butinterestingly,thistimethedifferencesbetweenthesemeth-
turns the same set of hashtags for any user. These hashtags are
odsismuchlarger.Thisindicatesthatthetimeinformationisespe-
rankedbytheiroverallfrequencyinthedataset[16].
ciallyimportantinasocialsetting. Wesomehowexpectedthisbe-
FR. FolkRank is an adaption of Google’s PageRank approach
haviorsincetypicallyonlythemostrecenttweetsofthefollowees
usedtoranktheentitiesinfolksonomygraphsandhasbecomeone
are shown on a user’s Twitter timeline. Again, the combination
ofthemostsuccessfultagrecommendermethods[12]. Weusethe
ofhashtagfrequencyandrecencybymeansoftheBLLequation
providesthebestresults.
6http://www.kde.cs.uni-kassel.de/ws/dc09/evaluation. 7http://www.kde.cs.uni-kassel.de/code
Scenario1: Scenario2:
Hashtagrec.w/ocurrenttweet Hashtagrec.w/currenttweet
Dataset Metric MPI MRI BLLI MPS MRS BLLS MP FR CF BLLI,S SR TCI BLLI,S,C
F1@5 .086 .098 .101 .022 .076 .118 .006 .083 .099 .153∗∗∗ .139 .182 .200∗
MRR@10 .136 .188 .193 .032 .122 .187 .007 .130 .163 .268∗∗∗ .264 .334 .395∗∗∗
CompSci
MAP@10 .143 .195 .202 .033 .128 .205 .007 .136 .169 .285∗∗∗ .283 .354 .417∗∗∗
nDCG@10 .175 .218 .225 .046 .154 .235 .012 .169 .196 .324∗∗∗ .299 .385 .446∗∗
F1@5 .160 .169 .175 .072 .103 .138 .012 .159 .165 .208∗∗∗ .181 .243 .261∗
MRR@10 .261 .300 .314 .109 .159 .220 .023 .260 .278 .361∗∗∗ .341 .436 .489∗∗
Random
MAP@10 .279 .315 .335 .116 .171 .240 .024 .279 .296 .389∗∗∗ .374 .472 .530∗∗
nDCG@10 .323 .352 .370 .144 .205 .280 .035 .324 .333 .434∗∗∗ .388 .507 .562∗∗
Table 3: Recommender accuracy results of our two evaluation scenarios. In Scenario 1, we compare approaches that ignore the
currenttweetcontent,whileinScenario2,wecomparealgorithmsthatalsoincorporatethecurrenttweet.Weobservethat(i)BLL
I
outperformsMP andMR ,(ii)BLL outperformsMP andMR ,(iii)BLL outperformsMP,FRandCF,and(iv)BLL
I I S S S I,S I,S,C
outperformsSRandTCI.Basedonat-test,thesymbols∗ (α=.1),∗∗ (α=.01)and∗∗∗ (α=.001)indicatestatisticallysignificant
differencesbetweenBLL andCFinScenario1,andbetweenBLL andTCIinScenario2.
I,S I,S,C
0.25 0.40 0.40 0.6
BLLI BLLI SR SR
BLLS 0.35 BLLS 0.35 TCI 0.5 TCI
0.20 CF CF 0.30 BLLI,S,C BLLI,S,C
BLLI,S 0.30 BLLI,S 0.4
Precision00..1105 Precision00..2205 Precision000...122505 Precision0.3
0.2
0.15
0.10
0.05 0.10 0.05 0.1
0.000.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.005.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.000.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.00.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65
Recall Recall Recall Recall
(a)Scenario1:Hashtagrec.w/o (b)Scenario1:Hashtagrec.w/o (c)Scenario2:Hashtagrec.w/ (d)Scenario2:Hashtagrec.w/
currenttweet currenttweet currenttweet currenttweet
CompScidataset Randomdataset CompScidataset Randomdataset
Figure4: Precision/RecallplotsofourtwoevaluationscenariosshowingtheaccuracyofBLL ,BLL ,CF,BLL ,SR,TCIand
I S I,S
BLL fork=1-10recommendedhashtags.Again,BLL providesthebestresultsinScenario1andBLL inScenario2.
I,S,C I,S I,S,C
(c) BLL > MP, FR, CF. Finally, we compare our hybrid CFforaddingpersonalization,weincorporatenotonlyindividual
I,S
BLL approach to the unpersonalized MP algorithm, the well- hashtags of the current user but also social hashtags of the cur-
I,S
knownFRmethodfromtagrecommenderresearchandclassicuser- rentuser’sfollowees,(ii)insteadofapplyingtheeffectoftimeon
basedCF.Thefirstobservationthatbecomesapparentisthepoor aglobalhashtaglevel,wemodelthetime-dependentdecayonan
performanceoftheunpersonalizedMPbaseline,whichunderpins individualandsociallevel,and(iii)insteadofmodelingthistime-
theimportanceofpersonalizedmethodsforhashtagrecommenda- dependent decay using an exponential function, we use a power
tion. Additionally, andmoreimportantly, ourhybridBLL ap- functionbymeansoftheBLLequation.
I,S
proachdoesnotonlyimproveitsBLL andBLL componentsbut
I S CompScidatasetvs. Randomdataset. Anotherinterestingfind-
also provides significantly higher accuracy and ranking estimates
ingweobserveisthatallalgorithmsprovidebetterresultsforthe
thanFRandCF.ThisshowsthatBLL iscapableofproviding
I,S RandomdatasetthanfortheCompScidataset. Inourcase,thisin-
reasonablehashtagrecommendationssolelybasedontemporalus-
dicatesthatthetaskofpredictinghashtagsinthedomain-specific
agepatternsofpasthashtagassignments.
networkofcomputerscientistsisharderthaninthenetworkofran-
Scenario2: Hashtagrec. w/currenttweet. Inthesecondsce- dom users. If we look back at Figure 1, this makes sense since
nario,weevaluatehashtagrecommendationmethodsthatalsoin- theamountof“external”hashtagsistwiceashighintheCompSci
corporatethecontentofthecurrenttweet.Thisincludestheunper- dataset(i.e.,26%)thanintheRandomone(i.e.,13%).
sonalizedSRapproach,thetime-dependentTCIalgorithmandour
BLL approach.Ourtwomainresultsare: Finding2: TheBLLequation,whichaccountsfortemporalef-
I,S,C
(a) TCI, BLL > SR. The first main result of our second fects of item exposure in human memory, provides a suitable
I,S,C
evaluationscenarioisthatbothtime-dependentmethodsTCIand model for personalized hashtag recommendations. This is val-
BLL outperformtheunpersonalizedSRapproach. Wesome- idatedintwoevaluationscenarios(i.e.,withoutandwithincor-
I,S,C
howexpectedthisresultsincebothTCIandBLL extendthe poratingthecontentofthecurrenttweet),inwhichourcognitive-
I,S,C
TF-IDF-based tweet content analysis of SR with personalization inspired approach outperforms several state-of-the-art hashtag
techniquesviaCF(TCI)ortheBLLequation(BLL ). recommendationalgorithmsintermsofpredictionaccuracy.
I,S,C
(b) BLL > TCI. The second main result of Scenario 2
I,S,C
isthatBLL providessignificantlyhigheraccuracyestimates
I,S,C 6. RELATEDWORK
thanTCI.Thisisduetothreemaindifferencesbetweenthesemeth-
ods: (i) instead of using hashtags of similar users by means of Overthepastyears,tagginghasemergedasanimportantfeature
ofthesocialWeb,whichsupportsuserstocollaborativelyorganize
and find content [18]. Two types of tags have been established: thehashtagsintotwocategories: “organizational”ones,whichare
(i)socialtagsasusedinsystemslikeBibSonomyandCiteUlike, usedoveralongperiodoftimeand“conversational”ones,which
and (ii) hashtags as used in systems like Twitter and Instagram. areusedonlyduringashorttimespan(e.g.,foraspecificevent).
Whereas social tags are mainly used to index resources for later Incontrasttoourproposedalgorithm,whichreliesontheBLL
retrieval,hashtagshaveamoreconversationalnatureandareused equation, their approach considers the effect of time on a global
tofilteranddirectcontenttocertainstreamsofinformation[13]. hashtag level of the whole Twitter network and not on an indi-
Oneofthemostprominentapproachesinthefieldoftagrecom- vidual and social level of a specific user. Furthermore, we use a
mendationsistheFolkRankalgorithm[12,15,16].FolkRankisan powerfunctionratherthananexponentialonetomodelthetime-
extension of the well-known Google PageRank approach to rank dependentdecaybasedonourempiricalfindings.
theentitiesinfolksonomies(i.e.,users,resourcesandtags). Other
importanttagrecommendationmethodsarebasedonCollaborative
7. CONCLUSIONANDFUTUREWORK
Filtering[29,8],LatentDirichletAllocation[23,22]orTensorFac-
torization[33,32]. Recentobservationsinthefieldofsocialtag- In this paper, we presented a cognitive-inspired approach for
gingstatetheimportanceofthetimecomponentfortheindividual hashtag recommendations in Twitter. Our approach utilizes the
taggingbehaviorofusers.Inthisrespect,[47,43,44]proposetime- BLL equation from the cognitive architecture ACT-R to account
dependenttagrecommenderapproaches,whichmodelthetagging fortemporaleffectsonindividualhashtagreuse(i.e.,reusingown
variation over time using exponential functions. In our previous hashtags) and social hashtag reuse (i.e., reusing hashtags, which
work[21,20],wepresentedamoretheory-drivenapproach,where hasbeenpreviouslyusedbyafollowee). Ouranalysisofhashtag
we use the BLL equation coming from the cognitive architecture usagetypesintwoempiricalnetworks(i.e.,CompSciandRandom
ACT-R[3,2]tomodelthepower-lawoftime-dependentdecay.We datasets)crawledfromTwitterrevealsthatbetween66%and81%
evaluatedourapproachindetailandcomparedittootherstate-of- ofhashtagassignmentscanbeexplainedbypastindividualandso-
the-art methods in [19]. In the present work, we build upon our cialhashtagusage. Byanalyzingthetimestampsofthesehashtag
resultsandincorporatetheBLLequationtostudytheeffectoftime assignments, wefindthattemporaleffectsplayanimportantrole
onhashtagreusetodesignourhashtagrecommendationapproach. forbothindividualandsocialreuseofhashtagsandthatapower
Thereisalreadyalargebodyofresearchavailablethatfocuses function provides a better fit to model this time-dependent decay
ontherecommendationofhashtagsinTwitter. Oneillustrativeex- thananexponentialfunction.
ampleistheworkpresentedin[9],inwhichhashtagrecommenda- Thus,themorerecentlyahashtagwasusedbyauserorherfol-
tionsareprovidedbycategorizingtweetsintogeneraltopicsusing lowees,thehighertheprobabilitythatthisuserwillusethesame
LDA.Theapproachthenrecommendsthehashtagsthatbestfitthe hashtagagainlaterintime.Basedonthesefindings,weutilizedthe
topicsofanewtweet. Theauthorsevaluatetheirapproachusing Base-LevelLearning(BLL)equationofthecognitivearchitecture
aqualitativestudy,inwhichtheyaskpersonsiftherecommended ACT-R,whichaccountsforthetime-dependentdecayofitemex-
hashtagsdescribethetopicsofatweetandcouldbeusedtoseman- posureinhumanmemory,todevelopBLLI,S andBLLI,S,C,two
tically enrich it. In 80% of the cases, they are able to provide a algorithmsforrecommendinghashtags. WhereasBLLI,S aimsto
suitablehashtagfromaselectionoffivepossibilities.Othersimilar recommendhashtagswithoutincorporatingthecurrenttweet(Sce-
approachesthatusetopicmodelsforhashtagrecommendationsare nario 1), BLLI,S,C also utilizes the content of the current tweet
presentedin[37,40,41,7].In[17],arelatedalgorithmbasedona usingtheTF-IDFstatistic(Scenario2). Wecomparedbothalgo-
hashtagclassificationschemeisproposed. Themostnotablework rithmstostate-of-the-arthashtagrecommendationalgorithmsand
inthecontextofhashtagrecommendationsisprobablythecontent- foundthatourcognitive-inspiredapproachesoutperformtheseal-
based SR approach presented in [45] and [46]. The authors use gorithmsintermsofpredictionaccuracyandranking.
the TF-IDF statistic to calculate similarities between tweets and Onelimitationofthisworkisthatwemodelthereuseofsocial
identify suitable hashtags based on these similarity scores. They hashtagssolelybyanalyzinghowfrequentlyandrecentlyahashtag
showthatSRimprovesRecallandPrecisionbyaround35%com- was used by a user’s followees, neglecting by whom the hashtag
paredtoapopularity-basedapproach.OurBLL approachuses wasused. Thus,forfuturework,weplantoextendourapproach
I,S,C
thesamestatistictointegratethecontentofauser’scurrentlypro- withthesocialstatusofthefollowee(e.g.,viathereputationofthe
posedtweet.In[25],apersonalizedextensionofSRispresented,in userbymeansofthenumberoffollowers).Inthisrespect,wewill
whichtheauthorscombineitwithuser-basedCF.Apartfromthat,a alsoutilizethesocialconnectionstrengthbetweenauserandher
content-basedhashtagrecommendationalgorithmforhyper-linked followee(e.g.,bythenumberofmentionsorretweets).
tweetsisproposedin[35]. Withrespecttothehashtagassignmentsthatcannotbeexplained
Relatedresearchhasstudiedtemporaleffectsonhashtagusage, byhashtagreuse(i.e.,26%intheCompScidatasetand13%inthe
forinstanceinthecontextofpopularhashtagsinTwitter[27,26, Randomdataset),wewanttoutilizeanexternalknowledgebaseto
39,28]. Forexample,in[28],theauthorsaimtopredictifaspe- alsoaccountforthesehashtagassignments.Wewillachievethisby
cifichashtagwillbepopularonthenextday. Byformulatingthis suggestinghashtagsofcurrentlytrendingtopicsorevents. Finally,
taskasaclassificationproblem,theyfindthatbothcontentfeatures wealsoplantoverifyourfindingsinlargerTwitterdatasamples
(e.g.,thetopicofthehashtag)andcontextfeatures(e.g.,theusers than the ones used in this paper as well as in other online social
whousedthehashtags)areeffectivefeaturesforpopularitypredic- networksthatfeaturehashtags,suchasInstagramandFacebook.
tion. Asimilarapproachispresentedin[42],inwhichtheauthors Insummary,ourworkcontributestotherichlineofresearchon
uncoverthetemporaldynamicsofonlinecontent(e.g.,tweets)by improving the use of hashtags in social networks. We hope that
formulatingatimeseriesclusteringproblem. Oneoftheveryfew futureworkwillbeattractedbyourinsightsintohowtemporalef-
examplesofatime-awarehashtagrecommendationapproachisthe fectsonhashtagusagecanbemodeledusingmodelsfromhuman
recentlyproposedalgorithmdescribedin[11]. Theauthorsextend memorytheory,suchastheBLLequation.
the content-based SR approach [45] with a personalization tech-
Acknowledgments.TheauthorswouldliketothankMatthiasTraub
niquebymeansofCFandfurtherconsiderthetemporalrelevance
andDieterTheilerforvaluableinputs. Thisworkisfundedbythe
of hashtags. To account for this temporal relevance, they divide
Know-CenterandtheEUprojectAFEL(GA:687916).
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