die 20130724

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.