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

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

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”

  3. None
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  6. None
  7. The gap grows

  8. The gap grows

  9. Goal Understand how controversies 
 unfold in social media

  10. Outline

  11. Outline Quantify

  12. Outline Quantify Graph

  13. Outline Quantify Graph Polarization Measure

  14. Outline Quantify Evolve Graph Polarization Measure

  15. Outline Quantify Evolve Time Graph Polarization Measure

  16. Outline Quantify Evolve Time Graph Polarization Measure Collective Attention

  17. Outline Quantify Evolve Cause Time Graph Polarization Measure Collective Attention

  18. Outline Quantify Evolve Cause Time Graph Opinion Polarization Measure Collective

    Attention
  19. Outline Quantify Evolve Cause Time Graph Opinion Echo Chambers Polarization

    Measure Collective Attention
  20. Quantifying Controversy
 in Social Media 
 
 WSDM 2016, TSC

    2018
  21. Black/Blue or White/Gold?

  22. Desiderata In the wild Not necessarily political No domain knowledge

    Language independent Allows comparison
  23. 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)
  24. Endorsement Graph #марш #sxsw

  25. Pipeline • Retweets • Follow • Mentions • Content •

    METIS • Spectral • Label 
 propagation • Random walk • Edge betweenness • 2d embedding • Sentiment variance
  26. Example

  27. Example #beefban #марш #sxsw #germanwings

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

  29. RWC Rationale Random Walk Controversy score Zaller's RAS model (Receive,

    Accept, Sample) 
 "The Nature and Origins of Mass Opinion" Response Axiom: "Individuals form opinions by averaging across the considerations that are immediately salient or accessible to them" Authoritative (influential) users with high degree set opinions Measure likelihood of user to be exposed to opinions from influential users on either side
  30. Random Walk

  31. Random Walk X Y

  32. Random Walk X Y

  33. RWC Definition we select one partition at random (each with

    walk that starts from a random vertex in tha visits any high-degree vertex (from either sid dom Walk Controversy (RWC) measure as fo ending in partition X and one ending in par obabilities of two events: (i) both random wa in and (ii) both random walks started in a pa The measure is quantified as RWC = PXX PY Y PY X PXY , X, Y } is the conditional probability AB = Pr[start in partition A | end in partition probabilities have the following desirable prop Consider two random walks, one ending in partition X and one ending in partition Y , RWC is the difference of the probabilities of two events: (i) both random walks started from the partition they ended in and (ii) both random walks started in a partition other than the one they ended in (a) (b) Partitions obtained for (a) #beefban, (b) #russia march by using the hybrid graph ch. The partitions are more noisy than those in Figures 3(a,b). Subsequently, we select one partition at random (each with probability 0 er a random walk that starts from a random vertex in that partition. Th nates when it visits any high-degree vertex (from either side). define the Random Walk Controversy (RWC) measure as follows. “Consi m walks, one ending in partition X and one ending in partition Y , RWC nce of the probabilities of two events: (i) both random walks started fr on they ended in and (ii) both random walks started in a partition other t ey ended in.” The measure is quantified as RWC = PXX PY Y PY X PXY , PAB , A, B 2 {X, Y } is the conditional probability PAB = Pr[start in partition A | end in partition B]. orementioned probabilities have the following desirable properties: (i) they d by the size of each partition, as the random walk starts with equal pro ach partition, and (ii) they are not skewed by the total degree of vertices
  34. RWC Properties Probabilities are conditional on ending in either partition

    Random walks end on either side with equal probability Not skewed by size of each partition Not skewed by total degree of vertices in each partition Close to 1 when probability of crossing sides low (high controversy) Close to 0 when probability of crossing comparable to that of staying (low controversy)
  35. Controversy Detection

  36. Controversy Score

  37. A:19 (a) (b) Fig. 10: RWC scores for synthetic Erd¨

    os-R´ enyi graphs planted with two communities. p1 is the intra-community edge probability, while p2 is the inter-community edge probability. generate random Erd¨ os-R´ enyi graphs with varying community structure, and compute the RWC score on them. Specifically, to mimic community structure, we plant two separate communities with intra-community edge probability p1 . That is, p1 defines how dense these communities are within themselves. We then add random edges between these two communities with probability p2 . Therefore, p2 defines how connected the two communities are. A higher value of p1 and a lower value of p2 create a clearer two-community structure. Figure 10 shows the RWC score for random graphs of 2000 vertices for two different settings: plotting the score as a function of p1 while fixing p2 (Figure 10a), and vice-versa Planted Synthetic Graphs
  38. Summary RWC: a measure for how controversial a discussion on

    a topic is on social media Graph-based measure: no domain knowledge, language agnostic Intuitive semantics founded on opinion formation models Captures controversy better than state-of-the-art User-level polarization measure easy to derive
  39. The Effect of Collective Attention on Controversial Debates on Social

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

    media
  41. The Effect of Collective Attention on Controversial Debates on Social

    media
  42. The Effect of Collective Attention on Controversial Debates on Social

    media
  43. The Effect of Collective Attention on Controversial Debates on Social

    media
  44. "Trump taxes" on Google

  45. "Trump taxes" on Google Rachel Maddow show on 2005 tax

    return
  46. Obamacare on Twitter

  47. Gun Control on Twitter

  48. Literature so far Controversial debates examined in isolation As static

    snapshots
  49. Contribution Controversial debates are dynamic They change with collective attention

    Analyze controversial debates over time Particularly when collective attention increases When external ‘event’ happens
  50. Data Twitter 4 longitudinal polarized topics Obamacare, Abortion, Gun control,

    Fracking 5 years (2011 -- 2016) Hundreds of thousands of users Millions of tweets
  51. Definitions Retweet Graph Reply Graph Core Users

  52. Retweet Graph

  53. Reply graph

  54. Core

  55. Core Core Users

  56. Experiments

  57. Experiments Compare these
 two points

  58. Retweet Graph

  59. Retweet Graph 1) New users enter the discussion

  60. Retweet Graph 2) Most retweets to existing core users 1)

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

    New users enter the discussion 3) Cross-side retweets decrease
  62. 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
  63. 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
  64. 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.
  65. Reply Graph Cross-side edges increase: more discussion Attention increases

  66. Content Pro Life Pro Choice Normal
 Condition Attention
 Increase

  67. Content Pro Life Pro Choice Normal
 Condition Attention
 Increase Content

    becomes uniform across the sides
  68. Long-Term Polarization

  69. 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!
  70. Political Discourse on Social Media Echo Chambers, Gatekeepers, 
 and

    the Price of Bipartisanship WWW 2018
  71. 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
  72. 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
  73. 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
  74. Production/Consumption Consumption What you receive in your feed What your

    followees tweet Production What you tweet
  75. 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)
  76. 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
  77. 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.
  78. 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
  79. 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
  80. (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.
  81. Correlation: Gun Control

  82. (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
  83. Variance (b) (c)

  84. 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
  85. 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)
  86. Partisans vs Bipartisans
 Gatekeepers vs Non-gatekeepers ions 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
  87. Summary Find echo chambers in political discussion on Twitter Definition

    of echo chambers with two elements: Content (echo) + Network (chamber) Data supports the selective exposure theory Bi-partisan users pay a price in terms of network centrality and content appreciation
  88. Conclusions How do controversies unfold on social media? Measuring is

    the first step (RWC) Controversies are dynamic (time is an important factor) Collective attention increases polarization Echo chambers associated with controversies Evidence of selective exposure and price of bi-partisanship
  89. What's next? Joint opinion formation + network generation model Adding

    data to opinion dynamics models Temporal dynamics of the process Application to other contexts (Reddit, Facebook) Interventions: can we do something about it?
  90. Thanks! Questions please! 64 @gdfm7 gdfm@acm.org