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DIE 20130724

DIE 20130724



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    DIE 20130724 DIE 20130724 Presentation Transcript

    • 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
    • • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
    • 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.
    • • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
    • 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).
    • 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.
    • • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
    • 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
    • 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
    • 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.
    • 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.
    • 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
    • • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
    • 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)
    • 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
    • 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)
    • 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.
    • 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).
    • • Summary • Introduction • Twitter Data Analysis • Agent-based Simulation • Discussion
    • 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
    • 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.