does love change on twitter? the dynamics of topical conversations in microblogging
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Presentation at SocialCom 2013TRANSCRIPT
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The Dynamics of Topical Conversations in Microblogging
Victoria Lai and William RandSocialCom 2013September 11, 2013
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Keyword and trend identification Background noise Importance of time, subject, geography?
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Twitter’s trending topics Real-time trend identification◦ Mathioudakis and Koudas (2010)◦ Cataldi, Di Caro, and Schifanella (2010)
Other trend detection methods◦ Benhardus and Kalita (2013)
Trends in a geographic area◦ Wilkinson and Thelwall (2012)
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Daily Twitter REST API queries, 47 weeks Topics◦ 11 keywords/phrases chosen for time, subject, and
geographic variety (baseball, economy, global warming, love, London riots)◦ 5 control (I, the, and, a, of)
Geography◦ Global◦ 9 cities (Boston, D.C.,
London, Sydney)
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Term frequency
Inverse document frequency
TF-IDF
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Keyword lists as target/control sets vary Time, subject, geography Tf-idf and rank correlation
𝑓𝑓 𝑇𝑇𝑡𝑡∗, 𝑠𝑠∗, 𝑔𝑔∗ 𝐶𝐶𝑡𝑡, 𝑠𝑠, 𝑔𝑔 )
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…
tf phrase 1 #phrase 2 #phrase 3 #C0, ∀sc, ∀g
C1, ∀sc, ∀g
C2, ∀sc, ∀g
C3, ∀sc, ∀g
idf
phrase 1 #phrase 2 #phrase 3 #
phrase 1 #phrase 2 #phrase 3 #
…
T0, s*, ∀g
* SC = {I, the, and, a, of }
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Control set variation by time◦ Single week◦ Cumulative week
Control set variation by geography Target set variation by time
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Topic matters, time doesn’t.t
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Top keywords are very consistent, particularly
for subjective topics.
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The correlation decays as we add tweets from another week back in time.
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For trends relative to the current background noise, we only need a few weeks of control data.
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Trends are independent of local background noise and more dependent on global background noise.
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Some topics are more similar to background conversations than others.
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Subjective topics exhibit less vocabulary change over time.
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The results of this analysis provide suggestions for how to build a trend monitoring tool.
These recommendations help define and understand: Frequency of collection Short-term vs. long-term collections Local collections vs. global collections Keyword selection for best results
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Subject variation Other methods of comparison Blogging data
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