Controversy on Social Media: Collective Attention, Echo Chambers, and Price of Bipartisanship Gianmarco De Francisci Morales ISI Foundation with
 Kiran Garimella
 Aristides Gionis 
 Michael Mathioudakis 

Controversy: from Latin contra (against) vertere (turn) “turned against, disputed” 

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There are two sides to every story 

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Goal 

Goal Understand how controversies 
 unfold in social media 

Goal Understand how controversies 
 unfold in social media Network → Measure of Controversy 

Goal Understand how controversies 
 unfold in social media Network → Measure of Controversy Network + Time → Collective Attention 

Goal Understand how controversies 
 unfold in social media Network → Measure of Controversy Network + Time → Collective Attention Network + Content → Echo Chambers 

Quantifying Controversy
 in Social Media 
 
 WSDM 2016 

Black/Blue or White/Gold? 

Desiderata In the wild Not necessarily political No domain knowledge Language independent Allows comparison 

Problem Formulation Graph-based unsupervised formulation Conversation graph for a topic (endorsements) Find partition of graph (represents 2 sides) Measure distance between partitions (random walks) 

Example 

Example #beefban #марш #sxsw #germanwings 

Example #beefban #марш #sxsw #germanwings Controversial Non controversial 

Pipeline • Retweets • Follow • Mentions • Content • METIS • Spectral • Label 
 propagation • Random walk • Edge betweenness • 2d embedding • Sentiment variance 

Random Walk 

Random Walk X Y 

Random Walk X Y 

Controversy Detection 

The Effect of Collective Attention on Controversial Debates on Social media
 
 WebSci 2017 (Best Paper Award) 

The Effect of Collective Attention on Controversial Debates on Social media 

The Effect of Collective Attention on Controversial Debates on Social media 

The Effect of Collective Attention on Controversial Debates on Social media 

The Effect of Collective Attention on Controversial Debates on Social media 

"Trump taxes" on Google 

"Trump taxes" on Google Rachel Maddow show on 2005 tax return 

Obamacare on Twitter 

Gun Control on Twitter 

Literature so far Controversial debates examined in isolation As static snapshots 

Contribution Controversial debates are dynamic They change with collective attention Analyze controversial debates over time Particularly when collective attention increases When external ‘event’ happens 

Data Twitter 4 longitudinal polarized topics Obamacare, Abortion, Gun control, Fracking 5 years (2011 -- 2016) Hundreds of thousands of users Millions of tweets 

Definitions Retweet Graph Reply Graph Core Users 

Retweet Graph 

Reply graph 

Core 

Core Core Users 

Experiments 

Experiments Compare these
 two points 

Retweet Graph 

Retweet Graph 1) New users enter the discussion 

Retweet Graph 2) Most retweets to existing core users 1) New users enter the discussion 

Retweet Graph 2) Most retweets to existing core users 1) New users enter the discussion 3) Cross-side retweets decrease 

Retweet Graph 2) Most retweets to existing core users 1) New users enter the discussion 3) Cross-side retweets decrease 4) Within-side retweets increase 

Controversy Measure Figure 2: RWC score as a function of the activity in the retweet network. An increase in interest in the controversial topic corresponds to an increase in the controversy score of the retweet network. 5.1 Network F t r s w 

Core-Periphery Openness Figure 12: Core–periphery openness as a function of activity in the retweet network. As the interest increases, the num- ber of core-periphery edges, normalized by the expected number of edges in a random network, increases. This sug- gests a propensity of periphery nodes to connect with the core nodes when interest increases. 

Reply Graph Cross-side edges increase: more discussion Attention increases 

Content Pro Life Pro Choice Normal
 Condition Attention
 Increase 

Content Pro Life Pro Choice Normal
 Condition Attention
 Increase Content becomes uniform across the sides 

Long-Term Polarization 

Summary Controversial debates during external events Polarization increases Retweet graph becomes hierarchical (core-periphery) More replies across sides Content becomes more uniform Many more results in the paper! 

Political Discourse on Social Media Echo Chambers, Gatekeepers, 
 and the Price of Bipartisanship WWW 2018 

Political Discourse on
 Social Media Characterized by heavy polarization Emergence of echo chambers ("Hear your own voice") Might hamper deliberative process in democracy Lack of shared world view Concern expressed by former US Presidents, Facebook, Twitter, and more 

Polarization Cause Selective exposure? People see only content that agrees with their pre- existing opinion Biased assimilation? People pay more attention to content that agrees with their pre-existing opinion 

Echo Chamber Definition Echo = opinion Chamber = network Joint content + network definition Echo chamber = political leaning of content that users receive from network agrees with that of content they share to the network 

Production/Consumption Consumption What you receive in your feed What your followees tweet Production What you tweet 

Political Leaning Scores Based on source of the content (500 domains) Score derived by self-declared affiliation of sharers on FB FoxNews.com is aligned with conservatives (CP = 0.9),
 HuffingtonPost.com is aligned with liberals (CP = 0.17) 

Production/Consumption Scores Polarity scores based on “content” leaning (from source) Production score Average political leaning of the content the user tweets Consumption score Average political leaning of the content the user receives on their feed Results of selection by the user 

δ-partisanship f s n- r e m- ., s n e e k Figure 1: Example showing the de￿nition of -partisan users. The dotted red lines are drawn at and 1- . Users on the left of the leftmost dashed red line or right of the rightmost one are -partisan. 

δ-{partisan,consumer,gatekeeper} δ-partisan: produces content with polarity beyond δ δ-bipartisan: produces content with polarity within δ δ-consumer: consumes content with polarity beyond δ δ-gatekeeper: δ-partisan but not δ-consumer consumes from both sides but produces content aligned with only one side blocks information flow towards its community 

Network Measures Network-based latent-space user polarity Based on following politicians with aligned ideology Network centrality (PageRank) Local clustering coefficient Retweet/favorite rates and volumes 

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 3: Distribution of production and consumption polarity, for P￿￿￿￿￿￿￿￿ (￿rst row) and N￿￿￿P￿￿￿￿￿￿￿￿ (second row) datasets. The scatter plots display the production ( x -axis) and consumption ( -axis) polarities of each user in a dataset. Colors indicate user polarity sign, following [6] (grey = democrat, yellow = republican). The one-dimensional plots along the axes show the distributions of the production and consumption polarities for democrats and republicans. Correlation (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 3: Distribution of production and consumption polarity, for P￿￿￿￿￿￿￿￿ (￿rst row) and N￿￿￿P￿￿￿￿￿￿￿￿ (second row) datasets. The scatter plots display the production ( x -axis) and consumption ( -axis) polarities of each user in a dataset. Colors indicate user polarity sign, following [6] (grey = democrat, yellow = republican). The one-dimensional plots along the axes show the distributions of the production and consumption polarities for democrats and republicans. 

Correlation: Gun Control 

(f) (g) (h) (i) (j) Figure 3: Distribution of production and consumption polarity, for P￿￿￿￿￿￿￿￿ (￿rst row) and N￿￿￿P￿￿￿￿￿￿￿￿ (second row) datasets. The scatter plots display the production ( x -axis) and consumption ( -axis) polarities of each user in a dataset. Colors indicate user polarity sign, following [6] (grey = democrat, yellow = republican). The one-dimensional plots along the axes show the distributions of the production and consumption polarities for democrats and republicans. (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 4: Top: Production polarity variance vs. production polarity (mean). Bottom: Consumption polarity variance vs. con- sumption polarity (mean). However, di￿erently from the rest of the side they align with, they show a lower clustering coe￿cient, an indication that they are not completely embedded in a single community. Given that they receive content also from the opposing side, this result is to be Finally, given that both partisans and gatekeepers sport higher centrality, we compare their PageRank values directly and ￿nd that there is a signi￿cant di￿erence: partisans have a higher PageRank compared to gatekeepers (￿gure not shown). This e￿ect is more Variance (f) (g) (h) (i) (j) Figure 3: Distribution of production and consumption polarity, for P￿￿￿￿￿￿￿￿ (￿rst row) and N￿￿￿P￿￿￿￿￿￿￿￿ (second row) datasets. The scatter plots display the production ( x -axis) and consumption ( -axis) polarities of each user in a dataset. Colors indicate user polarity sign, following [6] (grey = democrat, yellow = republican). The one-dimensional plots along the axes show the distributions of the production and consumption polarities for democrats and republicans. (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 4: Top: Production polarity variance vs. production polarity (mean). Bottom: Consumption polarity variance vs. con- sumption polarity (mean). However, di￿erently from the rest of the side they align with, they show a lower clustering coe￿cient, an indication that they are not completely embedded in a single community. Given that they receive content also from the opposing side, this result is to be Finally, given that both partisans and gatekeepers sport higher centrality, we compare their PageRank values directly and ￿nd that there is a signi￿cant di￿erence: partisans have a higher PageRank compared to gatekeepers (￿gure not shown). This e￿ect is more 

Variance (b) (c) 

0.0 1.0 2.0 0.2 0.3 0.4 Large Threshold δ partisan bipartisan (a) 0.0 1.0 2.0 0.2 0.3 0.4 Combined Threshold δ partisan bipartisan (b) −0.5 0.5 1.5 2.5 0.2 0.3 0.4 Guncontrol Threshold δ partisan bipartisan (c) 0.0 1.0 2.0 0.2 0.3 0.4 Obamacare Threshold δ partisan bipartisan (d) 0.0 1.0 2.0 0.2 0.3 0.4 Abortion Threshold δ partisan bipartisan (e) Figure 5: Absolute value of the user polarity scores for -partisan and -bipartisan users. 5e−07 2e−06 1e−05 0.2 0.3 0.4 Large Threshold δ partisan bipartisan (a) 2e−05 2e−04 2e−03 0.2 0.3 0.4 Combined Threshold δ partisan bipartisan (b) 2e−05 2e−04 2e−03 0.2 0.3 0.4 Guncontrol Threshold δ partisan bipartisan (c) 1e−05 1e−04 1e−03 0.2 0.3 0.4 Obamacare Threshold δ partisan bipartisan (d) 1e−05 1e−04 1e−03 0.2 0.3 0.4 Abortion Threshold δ partisan bipartisan (e) Figure 6: Pagerank for -partisan and -bipartisan users. ble 3: Comparison between -gatekeeper users and a ran- m sample of normal users. A 3 indicates that the corre- onding property is signi￿cantly higher for gatekeepers < 0.001) for at least 4 of the 6 thresholds used. A mi- s next to the checkmark (-) indicates that the property is ni￿cantly lower. Table 4: Accuracy for prediction of users who are pa sans ( p ) or gatekeepers ( ). (net) indicates network and p ￿le features only, ( n -gram) indicates just n-gram featur The last two columns show results for all features combin p (net) (net) p (n-gram) (n-gram) p Price of Bipartisanship 0.0 1.0 2.0 0.2 0.3 0.4 Large Threshold δ partisan bipartisan (a) 0.0 1.0 2.0 0.2 0.3 0.4 Combined Threshold δ partisan bipartisan (b) −0.5 0.5 1.5 2.5 0.2 0.3 0.4 Guncontrol Threshold δ partisan bipartisan (c) 0.0 1.0 2.0 0.2 0.3 0.4 Obamacare Threshold δ partisan bipartisan (d) 0.0 1.0 2.0 0.2 0.3 0.4 Abortion Threshold δ partisan bipartisan (e) Figure 5: Absolute value of the user polarity scores for -partisan and -bipartisan users. 5e−07 2e−06 1e−05 0.2 0.3 0.4 Large Threshold δ partisan bipartisan (a) 2e−05 2e−04 2e−03 0.2 0.3 0.4 Combined Threshold δ partisan bipartisan (b) 2e−05 2e−04 2e−03 0.2 0.3 0.4 Guncontrol Threshold δ partisan bipartisan (c) 1e−05 1e−04 1e−03 0.2 0.3 0.4 Obamacare Threshold δ partisan bipartisan (d) 1e−05 1e−04 1e−03 0.2 0.3 0.4 Abortion Threshold δ partisan bipartisan (e) Figure 6: Pagerank for -partisan and -bipartisan users. ble 3: Comparison between -gatekeeper users and a ran- m sample of normal users. A 3 indicates that the corre- onding property is signi￿cantly higher for gatekeepers < 0.001) for at least 4 of the 6 thresholds used. A mi- s next to the checkmark (-) indicates that the property is ni￿cantly lower. Table 4: Accuracy for prediction of users who are pa sans ( p ) or gatekeepers ( ). (net) indicates network and p ￿le features only, ( n -gram) indicates just n-gram featur The last two columns show results for all features combin p (net) (net) p (n-gram) (n-gram) p 

Price of Bipartisanship: PR hreshold δ (b) Threshold δ (c) Thresh (d) value of the user polarity scores for -partisan and 0.3 0.4 ombined hreshold δ partisan bipartisan (b) 2e−05 2e−04 2e−03 0.2 0.3 0.4 Guncontrol Threshold δ partisan bipartisan (c) 1e−05 1e−04 1e−03 0.2 0.3 Obama Thresh (d) 

Partisans vs Bipartisans
 Gatekeepers vs Non-gatekeepers ons d of g of pro- urce pro- the and ddi- for ties. (for and mp- also pro- rces. ach The Table 2: Comparison of various features for partisans & bi- partisans and gatekeepers & non-gatekeepers. A 3 indicates that the corresponding feature is signi￿cantly higher for the group of the column ( p < 0.001) for at least 4 of the 6 thresh- olds used, for most datasets. A minus next to the check- mark (-) indicates that the feature is signi￿cantly lower. Features Partisans Gatekeepers PageRank 3 3 clustering coe￿cient 3 (-) 3 (-) user polarity 3 (-) 3 (-) degree 3 3 retweet rate 3 7 retweet volume 3 7 favorite rate 3 7 favorite volume 3 7 # followers 7 7 # friends 7 7 # tweets 7 7 age on Twitter 7 7 datasets).9 A “3 (-)” means that the property is signi￿cantly lower 

Conclusions How to quantify controversy of a topic discussed in social media Collective attention increases polarization Periphery users tend to retweet core ones Interplay between content and network in echo chambers Evidence of selective exposure Bipartisan users pay a price in terms of network centrality 
 and content appreciation 

What's next? Joint opinion formation + network generation model Temporal dynamics of the process Application to other contexts (Reddit, Facebook) Interventions: can we do something about it? 

Thanks! Ask me two questions! 56 @gdfm7 [email protected]