Effective Event Identification in Social Media @ DEB 2013

1. +
Effective Event Identification in
Social Media
2014/4/27(Mon.)
Chang Wei-Yuan @ MakeLab Lab Meeting
Fotis Psallidas, Hila Becker
DEB’13

2. +
Outline
n Introduction
n Method
n Known-Event Identification
n Unknown-Event Identification
n Improving Identification Effectiveness
n Experimental Evaluation
n Conclusion
n Thought
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3. +
Introduction
nOnline 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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4. +
Introduction
nOnline 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

5. +
Introduction
nGeneral approach: group similar
documents via clustering
n Each cluster corresponds to one event and
its associated social media documents
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6. +
Introduction
nChallenges
n Uneven data quality
n Highly heterogeneous
n Dynamic data stream of event information
n Number of events unknown
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7. +
Event Identification
nKnown-Event Identification
nUnknown-Event Identification
nImproving Identification Effectiveness
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8. +
Known-Event Identification
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Known-Event Identification
nSocial media content related to known
events
n reside in multiple social media sites, each
contributing different information
nTo retrieve cross-site social media
documents for same event
n miss many relevant event documents
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10. +
Known-Event Identification
nIn the first step, using the known event
properties to achieve high-precision
results.
nIn the second step, using term extraction
and frequency analysis to improve
recall.
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11. +
Unknown-Event Identification
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na Twitter stream may contain many
tweets related to an event
n with messages related to other events
n with messages unrelated to events

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Unknown-Event Identification
nThe proposed online clustering
framework
n leverages the multiple features to decide
when two social media documents
correspond to the same event
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13. Social Media Document Clustering
Framework
Document feature
representation
Social media
documents
Event clusters
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14. Ensemble Algorithm
nThe proposed online clustering framework
n 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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15. Consensus Function:
combine ensemble
similarities
Wtitle
Wtags
Wtime
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f(C,W)
Ctitle
Ctag
s
Ctime
Ensemble
clustering
solution
Learned in a
training step
Ensemble Algorithm

16. Event Classification
nThe proposed online clustering framework
n deployed event classification to distinguish
between event-related clusters and non-event
ones
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17. Event Classification 17
Ensemble
clustering
solution
Event
unrelated to events
related to an event
event classification

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Improving Identification
Effectiveness
nHow events behave over time have a
significant impact on the effectiveness
of the document clustering procedure?
nHow to refine the clustering procedure
to benefit from these factors is a
challenging task?
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URLs
nURLs in event-related social streams
are ubiquitous. Individuals use them to
share meaningful event-related external
content.
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Bursty Vocabulary
nThe 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
nThe social media content related to an
event tends to revolve around a central
topic.
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Time Decay
na time decay function to the clustering
framework
n 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
nData
n Upcoming dataset
n 273,842 multi-featured Flickr photos that
correspond to 9,613 real-world events from
the Upcoming event.
nthe BurstyV + TimeDec technique
obtained the highest quality results.
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Conclusion
nThis article discussed the event
identification task under two different
scenarios, known- and.
nWe showed how to identify event
content effectively
n how we can exploit rich features of the social
media documents
n revealing temporal patterns of the relevant
content
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25. +
Thought
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Thanks for listening.
2014 / 4 / 27(Mon.) @ MakeLab Group Meeting
v123582@gmail.com