A Meme is not a Virus:

Transcription

1 A Meme is not a Virus: The Role of Cognitive Heuristics in Online Information Diffusion Kristina Lerman USC Information Sciences Institute

2 Networks can amplify the flow of information

3 Networks can amplify the flow of information breaking-bin-laden-visualizing-the-power-of-a-single

4 Information spread as social contagion Standard model of contagion: A meme behaves like a virus, with each exposure of a naïve individual by an informed friend potentially resulting in an infection (meme transmission) [M. Gladwell] infected exposed

5 Information spread as social contagion Standard model of contagion: A meme behaves like a virus, with each exposure of a naïve individual by an informed friend potentially resulting in an infection (meme transmission) [M. Gladwell] infected exposed outbreak size: number of infected people

6 How large are outbreaks? Standard model of contagion (independent cascade model) predicts large outbreaks above some transmissibility [Ver Steeg, Ghosh & Lerman (2011) What stops social epidemics? in ICWSM]

7 How large are outbreaks? Standard model of contagion (independent cascade model) predicts large outbreaks above some transmissibility Most social media cascades fall in this range [Goel, Watts & Goldsteing (2012) The Structure of Online Diffusion Networks in EC.] [Ver Steeg, Ghosh & Lerman (2011) What stops social epidemics? in ICWSM]

8 How large are outbreaks? Standard model of contagion (independent cascade model) predicts large outbreaks above some transmissibility Most cascades fall in this range Puzzle #1: There are few viral outbreaks in social media; even largest ones reach less than 5% of the network. [Ver Steeg, Ghosh & Lerman (2011) What stops social epidemics? in ICWSM]

9 [Romero et al. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter in WWW-2011] Is social contagion complex? Exposure response: Probability of becoming infected after exposure to x infected friends

10 [Romero et al. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter in WWW-2011] Is social contagion complex? Exposure response: Probability of becoming infected after exposure to x infected friends Number of tweeting friends

11 [Romero et al. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter in WWW-2011] Is social contagion complex? Exposure response: Probability of becoming infected after exposure to x infected friends Puzzle Number #2: of tweeting Highly connected friends users appear to be less susceptible to becoming infected.

12 Cognitive heuristics People do not have the cognitive capacity to process all the information they receive Instead, they rely on cognitive heuristics to quickly (and unconsciously) decide what to pay attention to These heuristics introduce predictable biases into behavior

13 Cognitive heuristics Measure cognitive heuristics in online behavior Experimental study on MTurk Empirical analysis of social media Model cognitive heuristics Accounting for cognitive heuristics simplifies models of information diffusion

14 Cognitive heuristics Attentional mechanisms help focus our limited cognitive resources on the most important data cf visual attention Size, motion, and contrast make item salient can be used to nudge people to a decision [1938 Austrian election ballot]

15 Cognitive heuristics: Social influence bias People pay more attention to items that others liked

16 Cognitive heuristics: Position bias People pay more attention to items at the top of a screen or a list of items [Payne, The Art of Asking Questions (1951) ] [Buscher et al, CHI 09] [Counts & Fisher ICWSM 11]

17 [Hodas & Lerman How Limited Visibility and Divided Attention Constrain Social Contagion in SocialCom arxiv: ] Position bias in social media Retweet probability decreases with time since post s arrival Twitter Digg Observation: People are less likely to retweet older messages.

18 Measuring position bias: Mturk experiments Amazon Mechanical Turk workers were asked to recommend stories from a list of 100 science stories Presented in some order Parallel world experiments (inspired by MusicLab expts by Salganik et al., 2006) [Lerman & Hogg Leveraging position bias to improve peer recommendation in PLoS One (2014) arxiv: ]

19 Experimental design Vary item ordering measure popularity (# recommendations) No direct social influence: users not shown # recommendations Control was used to estimate story quality Story 89 Story 1 Story 7 Story 8 Story 11 Story 2 Story 27 Story 56 Story 35 Story 3 Story 56 Story 48 Story 27 Story 4 Story 90 Story 73 Story 8 Story 5 Story 8 Story 15 [random order] control [fixed order] [by popularity] # recommendations [by recency] of recommen.

20 Position bias Attention that items receive simply due to their position rr pp = recs pp recs relative # recommendations position Items in top positions receive 4x as much attention as items in lower positions [Lerman & Hogg (2014) Leveraging position bias to improve peer recommendation in PLoSOne]

21 Position bias in social media new post at the top of user s screen prob. to view post post visibility post near the top is most likely to be seen position

22 Position bias in social media later: newer posts from friends appear at the top post is less likely to be seen position prob. to view post

23 [Hodas & Lerman How Limited Visibility and Divided Attention Constrain Social Contagion in SocialCom arxiv: ] Position bias in social media Retweet probability decreases with time since post s arrival Twitter Digg Observation: Well-connected hubs (i.e., those following many others) are less likely to retweet older posts.

24 Users divide attention over all incoming posts few friends many friends new post at top of user s screen post visibility post near the top is most likely to be seen

25 Users divide attention over all incoming posts few friends many friends later: newer posts from friends appear at the top post is less likely to be seen same age post is even less likely to be seen by a well-connected user

26 Users divide attention over all incoming posts Retweet probability decreases with network connectivity Twitter Digg Observation: Well-connected people (i.e., those following many others) are less likely to retweet a post. [Hodas & Lerman (2012) How Limited Visibility and Divided Attention Constrain Social Contagion in SocialCom. arxiv: ]

27 Position bias and exposure response Highly connected people (i.e., hubs) are less susceptible to infection, due to their increased cognitive load [Hodas & Lerman (2012) How Limited Visibility and Divided Attention Constrain Social Contagion in SocialCom. arxiv: ]

28 Weak response of hubs suppresses outbreaks Uniform susceptibility Decreased susceptibility outbreak size (# infected) transmissibility [Ver Steeg, Ghosh & Lerman (2011) What stops social epidemics? in ICWSM]

29 Modeling social contagion User must first see an item and find it interesting before he/she decides to retweet it See? Interesting? Respond Cognitive Interface Content e. g., retweet

30 How do users respond to multiple exposures? Twitter visibility: a retweet moves the post to top position in follower s stream prob. to view post Digg visibility: a vote does not change position, but increments the social signal for followers prob. to view post position position web site s user interface affects salience of information, but social signals matter too

31 Social influence amplifies response Inferred social influence strength Social influence strength Digg shows number of infected friends Twitter does not, but users may remember earlier exposures Number of exposures [Hodas & Lerman (2014) The Simple Rules of Social Contagion Scientific Reports 4]

32 User response to multiple exposures Probability that a user following n f friends will retweet a post at time t after x exposures, depends on the visibility of exposures and social influence factor F(x) F(x) [Hodas & Lerman (2014) The Simple Rules of Social Contagion Scientific Reports 4]

33 Predict user response to multiple exposures Probability that a user following n f friends will retweet a post at time t after x exposures, depends on the visibility of the exposures and social enhancement factor F(x) Model accurately predicts response regardless of exposures

34 Does content matter? User must first see an item and find it interesting before he/she decides to retweet it See? Interesting? Respond Cognitive Interface Content e. g., retweet PP mmmmmm tt, nn ff rrrrrrrrrrrrrr = II mmmmmm PP(tt, xx, nn ff ) Estimate interestingness of messages from observed retweets

35 Does content matter? ln( interestingness ) interesting messages can reach many or few people

36 Modeling user behavior in social media Can we learn topics of user s interest from observations of his or her retweeting patterns? Does a model incorporating cognitive heuristics, such as position bias, learn more accurate topics? user i

37 The model σ θ σ u D Items topic profiles Relevance Users topic profiles r N VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting [Kang and Lerman 15]

38 The model D E(num of new posts per visit) Visibility r N Law of surfing

39 Model training and testing Training Model Training Data Test Set

40 Evaluation Model Tex t Notes Random No baseline chooses items at random Fitness No uses item fitness values learned by Our Model Relevance No Probabilistic Matrix Factorization [Salakhutdinov and Mnih 2008] VIP No Softmax version of VIP (PMF + Cognitive) [Kang and Lerman 2015] CTR Yes Softmax version of CTR (PMF + Text) [Wang and Blei 2011] Our Model Yes Softmax version (PMF + Cognitive + Text) User Effort and Network Structure Mediate Access to Information in Networks [Kang and Lerman 15] 42

41 Social ties and network diversity Strength of weak ties [Granovetter 1973] Strong ties high frequency of interaction Weak ties low frequency of interaction Help spread information in a network Strong ties vs weak ties Strong ties Friends Family High network diversity Low network diversity Weak ties Acquaintances Co-workers Neighbors

42 Information diversity and network position (p<.01) (p<.01) (p<.01) (p<.01) Network diversity (ND) is higher for bridging positions that linked unconnected communities Users in the higher ND positions are exposed to more diverse information. Active users (who post more messages) receive more diverse information regardless of their network position. User Effort and Network Structure Mediate Access to Information in Networks [Kang and Lerman 15] 44

43 Summary A meme is not a virus Due to human cognitive biases, social contagion differs from viral contagion Limited attention makes the highly connected people less susceptible to infection Rather than amplify outbreaks, as in viral contagion, they suppress them Quantifying cognitive biases Measure the impact of cognitive biases through experiments and empirical analysis of behavioral data Accounting for cognitive biases makes user response more predictable, individually and collectively

44 Acknowledgments Collaborators Nathan Hodas, Greg Ver Steeg, Posdocs and PhD students Farshad Kooti, Jeon-Hyung Kang, Rumi Ghosh Sponsors