DIEJul.24, 2013
Kazutoshi Sasahara
Featured Article:Competition among memes in a world of limited attentionL. 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
• QuestionHow 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
• ResultWithout 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 usersLinks: 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[min
x2M\I
f(x)]
log[min
x2M
f(x)] + log[min
x2I
f(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-
CD
F)
(=1-
CD
F)
d: Peaked but wide distributionSome 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 relationshipse.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 structureis 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.
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