The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams

1.4 MB·

by  David Graus

The Birth of Collective Memories: Analyzing
Emerging Entities in Text Streams
David Graus1,2∗ Daan Odijk3 Maarten de Rijke1
1 Informatics Institute, University of Amsterdam
Amsterdam, The Netherlands
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1 2 FD Mediagroep, Amsterdam, The Netherlands
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3 Blendle, Utrecht, The Netherlands
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a [email protected], [email protected], [email protected]
J

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] Abstract
R
I Westudyhowcollectivememoriesareformedonline. Wedosobytrackingentitiesthat
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s emerge in public discourse, that is, in online text streams such as social media and news
c
[ streams,beforetheyareincorporatedintoWikipedia,which,weargue,canbeviewedasan

online place for collective memory. By tracking how entities emerge in public discourse,
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v i.e., the temporal patterns between their first mention in online text streams and subsequent
9 incorporationintocollectivememory,wegaininsightsintohowthecollectiveremembrance
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process happens online. Specifically, we analyze nearly 80,000 entities as they emerge in
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4 onlinetextstreamsbeforetheyareincorporatedintoWikipedia. Theonlinetextstreamswe
0 use for our analysis comprise of social media and news streams, and span over 579 million
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1 documentsinatimespanof18months.
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We discover two main emergence patterns: entities that emerge in a “bursty” fashion,
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1 i.e.,thatappearinpublicdiscoursewithoutaprecedent,blastintoactivityandtransitioninto
: collective memory. Other entities display a “delayed” pattern, where they appear in public
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i discourse, experienceaperiodofinactivity, andthenresurfacebeforetransitioningintoour
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culturalcollectivememory.
r
a
1 Introduction
Rememberingisasocialprocess[19]. Collectiveremembranceistheprocessinwhichinforma-
tion moves from public discourse into a shared collective memory. In the literature this process
has been described as being similar to the remembrance process of an individual, whose mem-
ories or experiences transfer from short-term into long-term memory [1]. This comparison has
∗Correspondingauthor.
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been formalized by mapping the collective’s equivalent of the individual’s long-term and short-
termmemorytothecollective’scultural andcommunicativememory,respectively.
Cultural collective memory (CM), the collective’s equivalent of an individual’s long-term
memory,ischaracterizedbybeingorganized,specialized,formal,structured,anddistancedfrom
the immediate [1]. Wikipedia is known to “democratize information,” through its collabora-
tive nature: its content is being produced by volunteer editors and authors from all over the
world [49]. Wikipedia has been called an online place for cultural CM [31, 38]. We support this
view, and argue that the aforementioned characteristics fit Wikipedia’s nature. First, Wikipedia
isorganized,throughitshierarchyofcontributors,whereauthorsaredistinguishedfromadmins.
Wikipedia is specialized, since appropriately citing relevant and expert sources to support and
back up newly added information is a requirement. These conventions, requirements, and poli-
cies around contributing new information to Wikipedia impose a level of formality and enable
its coherent and consistent structure. Finally, the requirement for new articles to be collectively
deemed“importantenough,”ensuresWikipedia’sdistancefromtheimmediate.
Communicative CM is in many aspects the opposite of cultural CM. Analogously to an
individual’s short-term memory, communicative CM is mainly orally negotiated, close to the
everyday, disorganized, informal, and non-specialized [1]. The large volume of content that is
produced continuously in online text streams fits this notion of orally negotiated memory: the
highly interactive and reactive nature of the content, through the rapid pace and high volume
at which it is created, means that—as opposed to the carefully curated and edited nature of
Wikipedia—online text streams are close to the everyday: they not only record and reflect the
actions of everyday life but also have a role in producing everyday life for a media-enabled
public [44, p. 33]. With the advent of Web 2.0, and the ability for anyone to publish content and
participate in public discourse on the web, online text streams became disorganized, informal,
andnon-specialized.
Inthispaper,westudytheevolutionofcollectivememory,bytrackingadditionstoouronline
cultural collective memory, Wikipedia. Specifically, we study the link between what happens in
the news and social media on the one hand and the addition of new Wikipedia articles on the
otherhand,bytrackingrealworldentitiesastheyemergeintheformer(ourcommunicativeCM),
beforetheyareaddedtothelatter(ourculturalCM).1 EntitiesthatemergeincommunicativeCM
and that are deemed important enough to be incorporated into cultural CM are thereby deemed
importantenoughtobecollectivelyremembered. WithWikipediaasanonlineplaceforcultural
CM, we are able to detail and study the collective remembrance process through the process of
Wikipedia’sexpansion.
Every day, new content is being added to Wikipedia, with the knowledge base receiving
over 6 million monthly edits at its peak [43]. Domain experts may find information missing on
Wikipedia, and take up the task of contributing this new information. Alternatively, as events
unfold in the real world, new, previously unknown, and unheard-of entities (people, organiza-
tions, products, etc.) emerge into public discourse. These new entities emerge online in news
1Byan“entity”wefollowstandardpracticeandmeanathingwithdistinctandindependentexistence,e.g.,aper-
son or device (Oxford Dictionary, http://www.oxforddictionaries.com/definition/english/
entity).
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articles and social media postings that may describe or comment on events, e.g., the Olympics
mayintroducenewathletesontotheworldstage,anewlyannouncedsmartphoneorvideogame
consolemaygenerateawaveofactivityonsocialmedia,ortheopeningofanewrestaurantmay
bereportedinlocalnewsmediaandpopupinsocialmedia.
Studyingourcollectivememorythroughstudyingtheentitiesthatemergeinpublicdiscourse
givesusinsightsintohowcollectivememoryevolves—forthefirsttime,theonlineworldallows
ustomakethesesortofobservationsatalargescale. Furthermore,intoday’sdigitalworld,with
virtually unconstrained ways of digitally storing information, we can keep accumulating events
in our communicative collective memory in great detail. Studying which entities transition to
our cultural CM, their properties and emergence patterns, is a cornerstone for understanding
whatwe,humans,findimportanttorememberasacollective.
In this paper, we study “emerging entities,” which we consider to be the core unit for under-
standing the collective remembrance process. We analyze a sample of online social media and
news text streams, spanning over 18 months and comprising over 579 million documents. We
focusontheemergencepatternsoftheseentities,i.e.,howanewentity’sexposuredevelopsand
evolves in the timespan between the entity’s first mention in online text streams, and when an
article devoted to the entity is subsequently added to Wikipedia. More precisely, we define an
entity’semergencepatterntobeits“documentmentiontimeseries,”i.e.,thetimeseriesthatrep-
resentsthenumberofdocumentsthatmentionaspecificentityovertime,startingatthemoment
itisfirstmentioned,uptothemomentitisincorporatedintoWikipedia.
An example of one such emergence pattern is shown in Figure 1. The time series shows
the number of documents that mention a given entity per day on the y-axis (i.e., the emergence
volume), with the x-axis representing the time between the first mention of the entity in the
online text stream and the day it is added to Wikipedia (i.e., the emergence duration).2 Since
emergence durations naturally differ between entities, our time series are of variable length. As
weareinterestedinbroadattentionpatterns,westudydocumentmentiontimeseries,asopposed
to total mention volume. And because we want to study global, broad, and long-term patterns,
ourtimeseriesareatagranularityofdays,nothours.
Themainfindingsofthispaperareasfollows. Byclusteringtimeseriesofmentionsofentitiesas
theseentitiesemergeinnewsandsocialmediastreams,wefindbroadlytwodifferentemergence
patterns: entitiesthatshowastronginitialburst aroundthetimeoftheirintroductionintopublic
discourse,andlateburstingentitiesthatexhibitamoregradualemergencepattern. Furthermore,
we find meaningful differences between how entities emerge in social media and news streams:
entitiesthatemergeinsocialmediastreamsexhibitaslowertransitionfromcommunicativeCM
to cultural CM than entities emerging in news streams. Finally, we show how different types
of real world entities exhibit different emergence patterns; we find that the entities that emerge
fastest are entity types that know shorter life-cycles such as devices (e.g., smartphones), and
“culturalartifacts”(e.g.,moviesandmusicalbums).
In Section 2 we describe related work for this study. In Section 3 we formulate our research
questions. The data that we use in our study is described in Section 4. In Section 5 we detail
2ThisistheEnglishversionofWikipedia;seeSection4fordetailsonthedatausedinthispaper.
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Figure 1: Emergence pattern of the entity Curiosity (Rover), first mentioned in our text
stream in October 2011. The Wikipedia page for Curiosity was created nine months later, on
August 6, 2012. There are two distinct bursts, one late November 2011, the second shortly
beforetheentityisaddedtoWikipedia. ThetwodocumentburstscorrespondtotheMarsrover’s
launch date (November 26, 2011) and its subsequent Mars landing (August 6, 2012). The time
series shows us that while the launch did generate publicity, yielding a burst of documents (and
signaling increased attention for the entity), at that point it was not deemed important enough to
beaddedtoWikipedia.
the methods used to analyze the data. Results of our analysis are presented in Section 6. We
concludeinSection7.
2 Related Work
Twotypesofworkarerelatedtothispaper: workoncollectivememoryandworkonthegrowth
anddevelopmentofWikipedia.
2.1 Collective memory
The concept “collective memory” was analyzed and advanced in the 1920s by Halbwachs [18];
see also [19]. The notion of a “shared pool of knowledge and information in the memories of
a social group,” depends upon the framework of a group in a society. Since its introduction the
concept has been studied in a wide variety of interdisciplinary fields, most notably in literature,
history, and media [8, 37] but also in (experimental) psychology [20, 41, 51], e.g., by emperi-
cally studying the performance of remembering events of different members of a single social
group[5].
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Wikipedia was first dubbed a global memory place where collective memories are built by
Pentzold [38], with follow-up studies by Keegan [25] and Ferron and Massa [11] shortly after
that. As Ferron [10, p. 23] puts it, “Wikipedia’s processes of discussion and article construction
can be seen as the discursive formation of memory, or in other terms, as the transition from
communicative memory, which is interactive, informal, nonspecialized, reciprocal, disorganized
andunstable,toculturalmemory,whichisformal,wellorganizedandobjective”(ouritalics).
Inthecontextofonlinecollectivememory,studieshaverevolvedaroundautomatedmethods
foranalyzingtexts,e.g.,byextractingandstudyingtemporalexpressionsfromwebdocumentsit
has been shown that we tend to remember the “near past” online [2]. Other sources used for on-
linecollectivememorystudiesincludesearchenginequerylogs[6],andmicroblogservices[23].
Our work differs from previous work on collective memory in two important ways. We are
the first to empirically study the transition from communicative CM to collective CM in terms
ofentitiesbeingmentionedinnewsandsocialtextstreamspriortobeingincludedinWikipedia.
Andwearethefirsttoempiricallystudythistransitionatscaleandacrosstextstreamsandentity
types, signifying an important difference from the case studies that often involve dramatic or
traumatizingeventsthatarecharacteristicofthestudyof“collectivememory”[30,36].
2.2 Growth and development of Wikipedia
PreviousworkonstudyingtheexpansionofWikipediathroughtheadditionofnewpagesstudies
the phenomenon from the perspective of Wikipedia itself, e.g., by analyzing how newly created
articlesfitinWikipedia’ssemanticnetwork,studyingtherelationbetweenactivityontalkpages
andtheadditionofnewcontenttoarticles,orbystudyingcontroversyanddisagreementonnew
contentthrough“editwars”[24,26,52,53].
Studying emerging entities has received considerable attention from the natural language
processing and information retrieval communities. Most notably, different methods and systems
for identifying and linking unknown or emerging entities have been proposed [21, 29, 35, 48].
Graus et al. [16] study the problem of predicting new concepts in social streams. More recently,
Fa¨rberetal.[9]formalizeandanalyzethespecificchallengesandaspectsthatcomewithlinking
emergingentities,whileReinandaetal.[42]studytheproblemofidentifyingrelevantdocuments
for known and emerging as as new information comes in and Graus et al. [17] present a method
forupdatingrepresentationsbasedonnewlyidentifiedinformation.
Our work differs from the aforementioned studies in being largely observational in nature,
focusing on temporal patterns of emerging entities, and studying it from the perspective of the
“outsideworld,”notWikipedia.
3 Research Questions
In studying emergence patterns of entities, we apply different methods for grouping entities.
Specifically, we apply three grouping strategies, representing alternative views on emerging en-
tities. In Section 6.1 below, we apply a burst-based unsupervised hierarchical clustering method
to cluster similar entity emergence patterns, so as to discover groups of entities with broadly
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common emergence patterns. That is, we group entity time series by similarities in the peaks of
comparatively higher activity (i.e., peaks of exposure in public discourse). This type of analysis
ismeanttohelpusanswerthefollowingresearchquestion:
RQ1 Aretherecommonpatternsinhowentitiesemergeinonlinetextstreams?
We show that entities emerge in two distinct patterns: so-called early bursting (EB) entities, that
show a strong initial burst around the time of their introduction into public discourse, or late
bursting (LB) entities, that exhibit a more gradual emergence pattern. EB entities are shown to
transitionfromcommunicativetoculturalCMmorequicklythanLBentities.
In Section 6.2, we adopt an alternative view of emerging entities, and examine their emer-
gence patterns in different types of text stream, comparing patterns between entities that emerge
innewstosocialmediastreams. Thisviewismotivatedbyperceiveddifferencesinthenatureof
the professionally curated, authoritative, and high impact, “mainstream media” versus the user-
generated, unedited, social media streams. We apply two grouping methods. First, we group
the time series by type of text stream, and provide their descriptive statistics, hypothesizing that
news streams will exhibit shorter emergence durations than social media streams, due to their
reach and impact. We also analyze the cross-pollination between the two types of stream, i.e.,
we study whether entities appear first in either of the streams, or whether they appear in both at
thesametime,etc. Theseanalyseshelpusanswerthefollowingresearchquestion:
RQ2 Donewsandsocialmediatextstreamsexhibitdifferentemergencepatterns?
We find that news and social media streams show broadly similar emergence patterns for enti-
ties. However, entities that first emerge in social media streams exhibit slower transitions from
communicativetoculturalCMthanentitiesthatfirstemergeinnewsstreams.
Finally, in Section 6.3, we study the similarities and differences between different types of
entities as they emerge in public discourse. Specifically, we leverage DBpedia, the structured
counterpart of Wikipedia, to group emerging entities by their underlying entity types, such as
companies, athletes, and video games. This allows us to gain insights into whether different
typesofentitiesexhibitdifferentemergencepatterns. Inaddition,byconsideringentitytypeswe
study whether the news stream and the social stream exhibit different focal points, i.e., where
professionallycuratednewsstreamsexhibitafocusonthemainstreamanduser-generatedsocial
media streams surface more niche entities. This analysis helps us answer the following research
question:
RQ3 Dodifferenttypesofentitiesexhibitdifferentemergencepatterns?
We find that different entity types exhibit substantially different emergence patterns. Further-
more, we find that specific entity types are more or less likely to emerge in either the news or
socialmediastreams,whichcanbelargelyattributedtothedifferentnatureofbothstreams,and
their authors. Finally, we find that entity types that have shorter emergence durations remain
popular over time. The findings suggest that the entity types that transition relatively fast from
communicativetoculturalCM,takeamorecentralplaceinourculturalCM.
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4 Data
As described in the introduction, we study the emergence patterns of entities by looking at the
lead time between the first mention of an entity, i.e., its first appearance in communicative CM,
up to its subsequent incorporation into Wikipedia, i.e., its transition into cultural CM. To study
these emergence patterns, we generate a custom dataset of timestamped documents that are an-
notatedretrospectivelywithlinkstoWikipedia,includingforeachlink(i)thecreationdateofthe
associatedWikipediapage,and(ii)whethertheWikipediapageexistedatthetimethedocument
was created. Our dataset spans 7.3 million documents with 36.2 million references to 79,482
unique emerging entities. We create this dataset by extending an existing document-stream
dataset (TREC KBA’s StreamCorpus 20143) with an additional set of annotations to Freebase
(FAKBA14). We enrich the dataset by including Wikipedia creation dates of the Freebase enti-
ties, and the relative age of the entity to the document. We subsequently reduce this dataset by
filtering out documents that have “future leaks”, i.e., that contain references to entities whose
Wikipedia pages were created after the dataset’s timespan. Below, we describe our data acquisi-
tionandpreparationprocessesindetail.
4.1 Emerging entities in news and social media
Our dataset is derived from the TREC KBA StreamCorpus 2014, a dataset of roughly 1.2 billion
timestampeddocumentsfromvarioussources,e.g.,blogandforumpostsandnewswires. Itspans
572days(orroughly18months)fromOctober7th,2011toMay1st,2013(line1inTable1). The
StreamCorpusiscomposedofmultiplesubsets,withslightlydifferentperiodsandvolumes(i.e.,
numbersofdocuments). Alldocumentsinthecorpusthatwereautomaticallyclassifiedasbeing
written in English have been automatically tagged for named entities with the Serif tagger [4],
yielding roughly 580M tagged documents. This annotated subset of documents was the official
collectionfortheTRECKnowledgeBaseAcceleration(KBA)taskin2014[14].
Dalton et al. [7] further annotated these 580M documents with Freebase entities, resulting
in the Freebase Annotations of TREC KBA 2014 Stream Corpus, v1 (FAKBA1) dataset, which
spans over 394M documents (line 2 in Table 1). It is essential to our study that the Freebase
dump used for linking the entities is dated after the span of the StreamCorpus. Because of this,
we can isolate entities that are mentioned (i.e., linked) in documents before their corresponding
Wikipediapagewascreated.
Wetakeanentity’sWikipediapagecreationdatetobethetimeatwhichtheentitytransitions
from communicative to cultural CM. To extract Wikipedia page creation dates for the entities
present in the TREC KBA StreamCorpus 2014, we first map the Freebase entities linked in the
FAKBA1 collection to their corresponding Wikipedia pages, using the available mappings in
Freebase. We extract Wikipedia page creation dates from a dump of Wikipedia with the full
revision history of all pages (enwiki-latest-page-meta-history.xml). We append
theWikipediapagecreationdateorentitytimestamp(denotede )toeachentityinFAKBA1. In
T
3http://trec-kba.org/kba-stream-corpus-2014.shtml
4http://trec-kba.org/data/fakba1/
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Table 1: Descriptive statistics of our dataset acquisition. Coverage over preceding dataset in
brackets. Looking at the second and third row in the table, we note that roughly two-thirds of
the FAKBA1 entities can be mapped to Wikipedia. However, this portion represents 98% of the
mentions. Closer inspection showed that the missing one-third, i.e., the entities we could not
map to Wikipedia creation dates, were Freebase entities that were not linked to their Wikipedia
counterparts, most notably, WordNet concepts and entities from the “MusicBrainz” knowledge
base (i.e., artists, albums, and artists). The last two rows show that one in ten of the entities
emergeduringthespanofthedataset,however,theyconstituteamere1%ofthementions.
Dataset #entities #mentions #documents
1.TRECKBA[14] N/A N/A 579,838,246
2.FAKBA1[7] 3,272,980 9,423,901,114 394,051,027(68.0%)
3.FAKBAT 2,254,177(68.9%)9,221,204,641(97.8%)394,051,027 (100%)
4.OOKBAT 225,291(10.0%) 94,929,292 (1.0%) 23,896,922 (6.1%)
5.Emergingentities 79,482(35.3%) 36,242,096(38.2%) 7,291,700(30.5%)
addition,weincludetheentity’s“age”relativetothedocumenttimestamp(doc ): thedifference
T
between the Wikipedia page creation date and the document timestamp, i.e., e = e −doc .
age T T
The resulting dataset, FAKBA1, extended with the entity age and entity timestamp, is denoted
FAKBAT (Freebase Annotations of TREC KBA 2014 Stream Corpus with Timestamps), see
line3inTable1.
As a next step, we filter to retain only documents that contain emerging entities. Emerging
entities are entities with e < 0, i.e., entities mentioned in documents dated before the entity’s
age
Wikipedia creation date. We denote the resulting subset of FAKBAT documents with emerging
entitiesOOKBAT (OutofKnowledgeBaseAnnotations(with)Timestamps). Thisyieldsasetof
nearly24Mdocuments(line4inTable1).
Tobeabletostudyanemergingentity’scompleteemergencepattern,wetaketwoadditional
filtering steps. First, we prune entities with creation dates later than the last document in our
stream, to ensure the entities emerged in the timespan of our document stream, i.e., we remove
all entities whose Wikipedia page has a creation date later than May 1st, 2013. Next, we prune
all entities that are mentioned in fewer than 5 documents, to be able to visualize and study their
time series. This yields our final dataset, which comprises 79,482 emerging entities (line 5 in
Table1).
4.1.1 Entitytypes
IntheanalysisinSection6.3,weleverageanentity’s“class”fromtheDBpediaontology.5 Free-
base provides mappings to Wikipedia and its structured counterpart DBpedia. In the DBpedia
ontology, an entity is assigned to one or more classes in a tree-like class structure. We map each
of our emerging entities to the classes assigned in DBpedia, e.g., the entity Barack Obama is
5http://mappings.dbpedia.org/server/ontology/classes/
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Figure 2: Total document volume (i.e., count) for the news (green line) and social media (grey
line) text streams over time (n = 23,896,922). The streams are composed of multiple separate
datasets,explainingthegapinMaythroughmidJulyofthenewsstream. (Bestviewedincolor.)
mapped to the Person, Politician, Author, Award Winner classes. We extract these mappings
by extracting all triples that have rdf:type property (e.g., Barack Obama <rdf:type>
Person). Out of the 79,482 emerging entities in our dataset, we have 39,713 class-mappings
(i.e.,acoverageof50.0%).
4.1.2 Entitypopularity
Finally, as a proxy for an entity’s popularity, which we use to study the composition of clusters
and the different substreams in Section 6.3, we extract Wikipedia pageview statistics. More
specifically, we extract the total number of pageviews each entity received during 2015. We
choosetousethepageviewcountsofayearthatfallsoutsideofthetimespanofourdatasetsoas
to minimize the effects of timeliness (i.e., we want to separate the true “head” entities from the
onesthathaveashorterlifespan).
4.2 Timeseries of emerging entity mentions
We use the dataset that we have created, the annotated subset of documents in the TREC KBA
StreamCorpus2014containingemergingentities,togeneratetimeseriesthatdescribetheiremer-
gence. Figure 2 shows the document volume over time of the different streams in our dataset.
It shows a larger number of news documents compared to social media documents in the first
halfofthedata,andalargernumberofsocialmediacomparedtonewsdocumentsinthesecond
half. In total, the news stream comprises 1,836,022 documents, making it substantially smaller
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Figure 3: Distribution of emerging entities in our dataset (n = 79,482). The x-axis shows the
timespan of our dataset in days, the y-axis shows the number of entities that emerge in our
datasetonthatday(i.e.,havetheirWikipediapagescreated).
thanthesocialmediastream,at5,357,014documents. Thenewsandsocialmediastreamsthem-
selves are composed of multiple datasets from different sources [13], which explains the gap
seenaroundMay2012uptosomewhereinJuly2012.
Wenotethat79,482entitiesemergeduringthe18+month(572days)timespanoftheTREC
KBAStreamCorpus2014dataset,i.e.,onaverageover138newentitiesemergeperday. Looking
at the distribution at which these entities emerge over time, we observe in Figure 3 that they do
not emerge uniformly. In particular, Figure 3 shows a gradual increase of emerging entities
between the start of our dataset and May 2012, at which it peaks. The subsequent gap can be
explainedbytheabsenceofthenewsstreamduringthattime(seealsoFigure2).
The core unit in our analysis are so-called entity document mention time series, i.e., time
series that represent the number of documents that mention an entity over time (see, e.g., Mars
Curiosity’s document mention time series in Figure 1). These time series are characterized
by several properties. First, the time series are of variable length: each entity’s time series starts
at the first mention of the entity in our dataset, and ends at the day the Wikipedia page for that
entitywascreated. Forsomeentities,thetimeseriesmayspanseveraldays,whereasothersmay
span months. Second, the time series in our dataset are not temporally aligned. They exhibit
different absolute timings, where the date of the first mention (i.e., the start of the time series)
andlastmention(i.e.,theendofthetimeseries)variesbetweenemergingentities.
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