Controversy on Social Media: Collective Attention, Echo Chambers, and Price of Bipartisanship

How do we discuss controversial topics on social media? 
Answering this question is not only interesting from a societal point of view, but also has concrete implications for policy makers, news agencies, and internet companies.
In this talk, we first take a look at how collective attention, which is typically related to external events that increase the visibility of the topic, changes the debate. Our analysis shows that, in long-lived controversial debates on Twitter, increased collective attention is associated with increased network polarization.
Then, we show how content and network interact in the formation of echo chambers. As expected, Twitter users are mostly exposed to political opinions that agree with their own. In addition, users who try to bridge the echo chambers by sharing content with diverse leaning have to pay a “price of bipartisanship” in terms of their network centrality and content appreciation.

Transcript

1. 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
2. Controversy: from Latin contra (against) vertere (turn) “turned against, disputed”
7. There are two sides to every story
11. Goal
12. Goal Understand how controversies 
 unfold in social media
13. Goal Understand how controversies 
 unfold in social media Network → Measure of Controversy
14. Goal Understand how controversies 
 unfold in social media Network → Measure of Controversy Network + Time → Collective Attention
15. Goal Understand how controversies 
 unfold in social media Network → Measure of Controversy Network + Time → Collective Attention Network + Content → Echo Chambers
16. Quantifying Controversy
 in Social Media 
 
 WSDM 2016
17. Black/Blue or White/Gold?
18. Desiderata In the wild Not necessarily political No domain knowledge Language independent Allows comparison
19. 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)
20. Example
21. Example #beefban #марш #sxsw #germanwings
22. Example #beefban #марш #sxsw #germanwings Controversial Non controversial
23. Pipeline • Retweets • Follow • Mentions • Content • METIS • Spectral • Label 
 propagation • Random walk • Edge betweenness • 2d embedding • Sentiment variance
24. Random Walk
25. Random Walk X Y
26. Random Walk X Y
27. Controversy Detection
28. The Effect of Collective Attention on Controversial Debates on Social media
 
 WebSci 2017 (Best Paper Award)
29. The Effect of Collective Attention on Controversial Debates on Social media
30. The Effect of Collective Attention on Controversial Debates on Social media
31. The Effect of Collective Attention on Controversial Debates on Social media
32. The Effect of Collective Attention on Controversial Debates on Social media
33. "Trump taxes" on Google
34. "Trump taxes" on Google Rachel Maddow show on 2005 tax return
35. Obamacare on Twitter
36. Gun Control on Twitter
37. Literature so far Controversial debates examined in isolation As static snapshots
38. Contribution Controversial debates are dynamic They change with collective attention Analyze controversial debates over time Particularly when collective attention increases When external ‘event’ happens
39. Data Twitter 4 longitudinal polarized topics Obamacare, Abortion, Gun control, Fracking 5 years (2011 -- 2016) Hundreds of thousands of users Millions of tweets
40. Definitions Retweet Graph Reply Graph Core Users
41. Retweet Graph
42. Reply graph
43. Core
44. Core Core Users
45. Experiments
46. Experiments Compare these
 two points
47. Retweet Graph
48. Retweet Graph 1) New users enter the discussion
49. Retweet Graph 2) Most retweets to existing core users 1) New users enter the discussion
50. Retweet Graph 2) Most retweets to existing core users 1) New users enter the discussion 3) Cross-side retweets decrease
51. 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
52. 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
53. 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.
54. Reply Graph Cross-side edges increase: more discussion Attention increases
55. Content Pro Life Pro Choice Normal
 Condition Attention
 Increase
56. Content Pro Life Pro Choice Normal
 Condition Attention
 Increase Content becomes uniform across the sides
57. Long-Term Polarization
58. 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!
59. Political Discourse on Social Media Echo Chambers, Gatekeepers, 
 and the Price of Bipartisanship WWW 2018
60. 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
61. 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
62. 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
63. Production/Consumption Consumption What you receive in your feed What your followees tweet Production What you tweet
64. 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)
65. 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
66. 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.
67. 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
68. 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
69. (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.
70. Correlation: Gun Control
71. (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
72. Variance (b) (c)
73. 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
74. 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)
75. 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
76. 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
77. 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?
78. Thanks! Ask me two questions! 56 @gdfm7 gdfm@acm.org