Effective Event Identification in Social Media

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Effective Event Identification in Social Media

2014/4/27(Mon.)Chang Wei-Yuan @ MakeLab Lab Meeting

Fotis Psallidas, Hila BeckerDEB’13

+Outline

n Introduction

nMethodn Known-Event Identificationn Unknown-Event Identificationn Improving Identification Effectiveness

nExperimental Evaluation

nConclusion

nThought

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+ Introduction

nOnline social media are extensively distribute content related to real-world events. n event: something that occurs at a certain time

in a certain place

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+ Introduction

nOnline social media are extensively distribute content related to real-world events. n event: something that occurs at a certain time

in a certain place

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Goal：Identifying Events and Associated Social Media Documents

+ Introduction

nGeneral approach: group similar documents via clusteringn Each cluster corresponds to one event and

its associated social media documents

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+ Introduction

nChallengesn Uneven data qualityn Highly heterogeneousn Dynamic data stream of event informationn Number of events unknown

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+Event Identification

nKnown-Event Identification

nUnknown-Event Identification

nImproving Identification Effectiveness

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+Known-Event Identification8

+Known-Event Identification

nSocial media content related to known eventsn reside in multiple social media sites, each

contributing different information

nTo retrieve cross-site social media documents for same eventn miss many relevant event documents

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+Known-Event Identification

nIn the first step, using the known event properties to achieve high-precision results.

nIn the second step, using term extraction and frequency analysis to improve recall.

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+Unknown-Event Identification11

na Twitter stream may contain many tweets related to an event n with messages related to other eventsn with messages unrelated to events

+Unknown-Event Identification

nThe proposed online clustering frameworkn leverages the multiple features to decide

when two social media documents correspond to the same event

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Social Media Document Clustering Framework

Document  featurerepresentation

Social  mediadocuments

Event  clusters

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Ensemble Algorithm

nThe proposed online clustering frameworkn deployed ensemble learning methods to learn and

associate each feature with a weight and a threshold that capture the importance of the features

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Consensus Function:combine ensemble similarities

Wtitle

Wtags

Wtime

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f(C,W)

Ctitle

Ctags

Ctime

Ensemble clustering solution

Learned in a training step

Ensemble Algorithm

Event Classification

nThe proposed online clustering frameworkn deployed event classification to distinguish

between event-related clusters and non-event ones

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Event Classification 17

Ensemble clustering solution

Event

unrelated to events

related to an event

event classification

+ Improving Identification Effectiveness

nHow events behave over time have a significant impact on the effectiveness of the document clustering procedure?

nHow to refine the clustering procedure to benefit from these factors is a challenging task?

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+URLs

nURLs in event-related social streams are ubiquitous. Individuals use them to share meaningful event-related external content.

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+Bursty Vocabulary

nThe social media content related to an event tends to revolve around a central topic.n this central topic is expressed by a set of

terms that is significantly more frequent n span a wide time range exhibit a different set

of these bursty terms at different points of their lifetime.

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+Bursty Vocabulary

nThe social media content related to an event tends to revolve around a central topic.

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+Time Decay

na time decay function to the clustering frameworkn penalizes clusters that have been inactive for

a long time.n re-triggers events that have been inactive for

some time if the similarity score without the time-decay factor is strong enough.

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+Experimental Evaluation

nDatan Upcoming datasetn 273,842 multi-featured Flickr photos that

correspond to 9,613 real-world events from the Upcoming event.

nthe BurstyV + TimeDec technique obtained the highest quality results.

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+Conclusion

nThis article discussed the event identification task under two different scenarios, known- and.

nWe showed how to identify event content effectivelyn how we can exploit rich features of the social

media documentsn revealing temporal patterns of the relevant

content

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+Thought25

+Thanks for listening.2014 / 4 / 27(Mon.) @ MakeLab Group [email protected]