This document summarizes a method for effective event identification in social media. It describes using clustering to group similar social media documents into events, both for known and unknown events. Key aspects of the method include using multiple document features like text, hashtags and timestamps. An ensemble algorithm learns weights for these features. Techniques like considering bursty vocabulary terms and time decay help improve clustering effectiveness over time. An evaluation on real-world event data from Flickr found the method using bursty vocabulary and time decay obtained the highest quality results.

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

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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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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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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

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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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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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Event Identification
nKnown-Event Identification
nUnknown-Event Identification
nImproving Identification Effectiveness
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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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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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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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Thought
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Thanks for listening.
2014 / 4 / 27(Mon.) @ MakeLab Group Meeting
v123582@gmail.com