Local Variation of Collective Attention in Hashtag Spike Trains
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[email protected] http://fcxn.wordpress.com http://xn.unamur.be Local Variation of Collective Attention in Hashtag Spike Trains (collaboration with Renaud Lambiotte) International AAAI Conference on Weblogs and Social Media (ICWSM-15) Workshop 3: Modeling and Mining Temporal Interactions, 26th May 2015, Oxford, the UK. @CeydaSanli
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Transcript of Local Variation of Collective Attention in Hashtag Spike Trains
- 1. A typical snapsho The white spots of the beads floa waves. [email protected] http://fcxn.wordpress.com http://xn.unamur.be r driving want to be mobile. As a witter users collectively advertise and orm groups to move together. Both f-organize and create dynamic ty. e, the interpretation of the dynamic eity of the beads in a critical limit elp to characterize viral memes (#hashtags) in twitter. Refs: 1 C. Sanl et al. (a 2 L. Berthier (201 A typical snapshot o The white spots indi of the beads floating waves. [email protected] http://fcxn.wordpress.com http://xn.unamur.be driving want to be mobile. As a ter users collectively advertise and m groups to move together. Both organize and create dynamic the interpretation of the dynamic ty of the beads in a critical limit p to characterize viral memes hashtags) in twitter. Refs: 1 C. Sanl et al. (arXi 2 L. Berthier (2011). Local Variation of Collective Attention in Hashtag Spike Trains ! (collaboration with Renaud Lambiotte)5mm Inset: The displacement field demonstrates local heterogeneities in the flow. A typical snapshot of an experiment: The white spots indicate the positions of the beads floating on surface waves. [email protected] http://fcxn.wordpress.com http://xn.unamur.be es social sages and As a ertise and er. Both mic s: dynamic al limit mes Refs: 1 C. Sanl et al. (arXiv - 2013). 2 L. Berthier (2011). International AAAI Conference on Weblogs and Social Media (ICWSM-15) Workshop 3: Modeling and Mining Temporal Interactions, 26th May 2015, Oxford, the UK. @CeydaSanli
- 2. Online SEPT. 29, 2014 Photo Credit Tomi Um Brendan Nyhan Information Diffusion in Twitter Local Variation Spike Trains 1C. Sanli, CompleXity Networks, UNamur tweets retweets mentions Tomi Um following - followers
- 3. y Rumors Outrace the Truth ne 014 Brendan Nyhan Hashtag Diffusion in Twitter Local Variation Spike Trains 2C. Sanli, CompleXity Networks, UNamur Tomi Um hashtag hashtag spike train time count
- 4. What do we address in this talk? Local Variation Spike Trains 3C. Sanli, CompleXity Networks, UNamur How can we measure local temporal behaviour of the hashtag diffusion? Is there a difference in the dynamics between popular and less used hashtags? Can we measure (and predict) collective attention by the hashtag dynamics?
- 5. Key Results: Local Variation Local Variation Spike Trains
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=91127
=18553
= 1678
= 318
= 174
= 117
= 86
= 68
= 56
= 47
= 41
= 35
=91127
=18553
= 1678
= 318
= 174
= 117
= 86
= 68
= 56 P()LVP()LV Real activity(a) Random activity(b) 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 3 LV(t1) LV(t2) 0.4 0.6 0.8 1 (LV(t1),LV(t2)) (a) (b)