DIE 20130724


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DIE 20130724

  1. 1. DIE Jul.24, 2013 Kazutoshi Sasahara Featured Article: Competition among memes in a world of limited attention L.Weng, A. Flammini,A.Vespignani, and F. Menczer, Scientific Reports (2012) doi:10.1038/srep00335
  2. 2. • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
  3. 3. Summary • Question How our limited attention affect meme diffusion in online world? • Approach - Data analysis for statistical properties of meme (hashtag) diffusion - Agent-based model to capture these properties • Result Without exogenous factors, the proposed model (limited attention + social network structure) can account for the observed statistically properties of memes and users.
  4. 4. • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
  5. 5. Introduction (1/2) • The advent of social media has lowered the cost of information production and broadcasting, boosting the potential reach of ideas or memes. • However, the abundance of information is exceeding our cognitive limit (cf. Dumber’s number). • As a result, memes must compete for our limited attention. (cf. economy of attention).
  6. 6. Introduction (2/2) • Social data allows us to quantify meme diffusion; yet it is hard to disentangle the effects of limited attention from other factors: - social network structure - the activity of users - the size of audience - the different degrees of influence of meme spreaders - the quality of memes - the persistence of topics, ... • The authors explicitly model mechanisms of competition among memes, exploring how they drive meme diffusion.
  7. 7. • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
  8. 8. Data Collection and Use • Collection of retweets • 2010.10 ~2011.1 • 120M retweets and 1.3M hashtags from 12.5M users * The user network shows a scale-free degree distribution. • Sampled user network • Users are sampled by a random walk sampling method • 105 nodes, 3×106 links • Parameters for posting (pu, pr, pm) and time window (tw) are estimated from the empirical data for modeling
  9. 9. Data Analysis (1/4) Meme Diffusion Networks Nodes:Twitter users Links: Retweets that carry the meme (i.e., hashtag) Memes about the Japan earthquake Political memes related to the US republican party Arab Spring
  10. 10. Data Analysis (2/4) Limited Attention S = X i f(i) log f(i) f(i): the proportion of tweets about meme (hashtag) i The breadth of attention of a user ~ Shannon entropy A user’s breadth of attention remains constant irrespective of system diversity. → The diversity of memes to which a user can pay attention is limited.
  11. 11. Data Analysis (3/4) User’s Interests and Memory sim(M, I) = 2 log[minx2MIf(x)] log[minx2M f(x)] + log[minx2If(x)] Maximum information path similarity considers shared memes but discounts the more common ones (Markins and Menczer 2009). User’s interest (Iu): The set of all memes that a user (u) has retweeted in the past User’s memory (Mn): The n most recent memes across all users (M0 :The set of new memes) f(x) :The proportion of a meme (x) Users are more likely to retweet memes about which they posted in the past (ρ=0.98). → Memory is important for meme diffusion.
  12. 12. Data Analysis (4/4) Empirical Regularities a, b, c: Long-tailed distributions across different time-scales (=1-CDF) (=1-CDF) d: Peaked but wide distribution Some users have broad attention while others are very focused. Weekly measured
  13. 13. • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
  14. 14. Model Description (1/3) Meme Diffusion Model • Memes ~ hashtags • Twitter user ~ Agent with a screen and a memory (finite size) • Twitter user network ~ A frozen network of agents • Nodes :Agents • Links : Friend-follower relationships e.g.,A→B (= B follows A) • The network structure is determined based on a subset of the empirical data (# of nodes = 105)
  15. 15. Model Description (2/3) Parameters (estimated from the data) • Tweet behaviors • Pn : Probability of posting a new meme 0.45 ± 0.5 • Pr : Probability of retweeting a meme in the screen Standard(0.016), ER(0.029), weak(0.205), strong(0.001) • Pm : Probability of posting a meme in the memory 0.4 ± 0.01 • Time window (tw) in which memes are retained in an agent’s screen or memory • tw < 0 : Less attention Strong competition • tw = 0 : Standard • tw > 0 : More attention Weak competition
  16. 16. A B C D E F A’s friends = {B, C, D} A’s followers = {E, F} Received memesPosted memes Model Description (3/3) Illustration of the Model Post a new meme (Pn) RT a meme (1- Pn): 1) from screen (Pr) or 2) from memory (Pm)
  17. 17. Simulation Results (1/2) Effects of Social Network Structure The observed quantities is greatly reduced when memes spread on a random network. tw= 1 (standard)The model captures the key features of the empirical data.
  18. 18. Simulation Results (2/2) Effects of Limited Attention Strong attention fails to reproduce the meme lifetime distribution (a). (strong) (weak) Weak attention fails to generate extremely popular memes nor extremely active users (b, c).
  19. 19. • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
  20. 20. Discussion (1/2) • The model demonstrates that a combination of limited user attention and social network structure is a sufficient condition for the observed statistical properties of memes and users: • Long-tailed distributions of meme lifetime and popularity, and user activity • The breadth of user attention • At the statistical level, exogenous factors are not necessary: e.g., meme’s quality, user’s personality, external events Source of heterogeneity meme competition
  21. 21. Discussion (2/2) • Related Studies • The decay in news popularity ~ a multiplicative process with a novelty factor (Wu and Huberman 2007) • Bursts of attention toward a video ~ an epidemic spreading process with a forgetting process (Crane and Sornette 2008)   None of them explicitly modeled meme competition • The economy of attention has always been assumed implicitly and never tested.This is the first attempt to explicitly model mechanism of competition.