1. 1. A typical snapsho The white spots of the beads floa waves. cedaysan@gmail.com 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. cedaysan@gmail.com 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. cedaysan@gmail.com 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. 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. 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. 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. 5. Key Results: Local Variation Local Variation Spike Trains 4C. Sanli, CompleXity Networks, UNamur 0 5 10 15 20 25 30 15 20 25 30

   =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)