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Polarization on Social Media Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis Slides available at: http://bit.ly/polarization-icwsm

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Page 1: Polarization on social media

Polarization on Social MediaKiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis,

Michael Mathioudakis

Slides available at: http://bit.ly/polarization-icwsm

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Social Media Bubble?

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Social Media Bubble?

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Social Media Bubble?

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Social Media Bubble?

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This tutorial

• Give a better understanding of polarization and related terms• Filter bubble, echo chambers, etc.

• Social theories behind polarization

• Measuring polarization

• Reducing polarization

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Outline

• Introduction• What the tutorial is about

• What the tutorial is not about

• Basic definitions

• Part 1: Social mechanisms and models

• Part 2: Case studies on Polarization on the Web

• Part 3: Quantifying Polarization

• Part 4: Mitigating Polarization

• Part 5: Conclusions & Future Work

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What the tutorial is about

• High level understanding of polarization• Mostly political polarization

• Mostly US

• Mostly social media

• Something for everyone• Learn about social theories and processes of polarization

• Learn about advances in algorithms for identifying and reducing polarization

• Overall picture on polarization from various fields

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What we don’t cover

• The field of polarization is a giant mammoth• Spans multiple fields, including social/political/psychological sciences

• We prefer breadth over depth

• Not many algorithmic details

• Not many details on evaluation

• The tutorial is not exhaustive• We will try to provide a complete list of references

• Report existing work, not judging

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What is polarization?

• The term is used in various domains with somewhat different meaning

• Political polarization (wikipedia) - “the divergence of political attitudes to ideological extremes”

• Social polarization - “the segregation within a society that may emerge from income inequality, real-estate fluctuations, economic displacements etc”

• Oxford Dictionary - “Division into two sharply contrasting groups or sets of opinions or beliefs.”

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Why is it important to study?

• Because polarization might be linked to adverse effects

• Social segmentation and stereotypes

• Decrease in deliberation• Bad for deliberative democracy

• Need to be aware of our biases• Sometimes there is no way to find the complete information

• Biases around us (e.g. algorithmic personalization)

• Limits people’s liberties (censorship)

• Not necessarily negative in itself, though

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Outline

• Introduction

• Part 1: Social mechanisms and models driving polarization• Individual biases

• Group biases

• System biases

• Part 2: Case studies on Polarization on the Web

• Part 3: Quantifying Polarization

• Part 4: Mitigating Polarization

• Part 5: Counter-findings

• Conclusions & Future Work

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What drives polarization - outline

1. Individual biases• Homophily

• Confirmation bias, Closure

• Cognitive dissonance

• Selective exposure theory

• Information overload

• Biased assimilation

2. Group biases• Social identity complexity, Social identity theory

• Group polarization, Groupthink

• In-group favoritism

3. System biases• Algorithmic filtering

• Media bias

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Individual biases

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Cognitive dissonance

• People experience positive feelings when presented with information that confirms that their belief / decision is correct.

• The effects of this phenomenon extend to the level of individual news items:

• the presence of opinion-reinforcing information is expected to increase the likelihood of exposure.

• Justify behavior that opposes their views

• Related term: ‘Closure’• ‘an individual's desire for a firm answer to a question and an aversion toward

ambiguity.’

P Fischer, D Frey, C Peus, and A Kastenmuller. “The theory of cognitive dissonance: state of the science and directions for future research” (2008)

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Homophily

• ‘The tendency of individuals to associate and bond with similar others’.

• Could be based on various facets• Gender, age, race, status, religion, region, etc.

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Confirmation bias

• ‘The tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses.’

• Related: Selective exposure theory• ‘Individuals' tendency to favor information which reinforces their pre-existing

views while avoiding contradictory information.’

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Confirmation bias/Selective exposure

• Selective exposure – people keep away from communication of opposite hue.

• Selective perception – If people are confronting unsympathetic material, they do not perceive it, or make it fit for their existing opinion.

• Selective retention – refers to the process of categorizing and interpreting information in a way that favors one category or interpretation over another. Furthermore, they just simply forget the unsympathetic material.

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Biased Assimilation

• ‘Tendency to interpret information in a way that supports a desired conclusion.’

• Related to Selective perception and retention.

• Supporting facts may seem overwhelmingly strong and negating facts may seem automatically weak.

• The result of exposing contending factions in a social dispute to an identical body of relevant empirical evidence may be not a narrowing of disagreement but rather an increase in polarization.

• Related: ‘Motivated skepticism’, ‘Backfire effect’

Lord C, Ross L, Lepper M. “Biased Assimilation and Attitude Polarization.” (1979)

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Biased Assimilation Examples

• Respondents supporting and opposing capital punishment• Shown evidence that opposes and supports capital punishment

• Increase in polarization

• Vaccination attempts by CDC, Iraq war, Science vs. conspiracy

• Neuroimaging shows evidence of increased activity in the brain for certain types of beliefs (e.g. political)

Lord C, Ross L, Lepper M. “Biased Assimilation and Attitude Polarization.” (1979)

C Yoon, DC Park, N Schwarz, “How warnings about false claims become recommendations.” (2005)

Bessi A, et al “Social determinants of content selection in the age of (mis)information” (2014)

Kaplan T, et al. “Neural correlates of maintaining one’s political beliefs in the face of counterevidence” (2016)

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Is there a tipping point?

Redlawsk D, The Affective Tipping Point: Do Motivated Reasoners Ever “Get It”? (2010)

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Information overload

• Difficulty of understanding an issue and effectively making decisions when one has too much information about that issue.

• Social media/internet accentuates this.

• Acts as a catalyst for other measures described above.

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Echo Chambers

• ‘A situation in which information, ideas, or beliefs are amplified or reinforced by communication and repetition inside a defined system.’

23

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Group biases

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Social identity complexity

• Individuals associate themselves with social identities (race, religion, gender, class)

• Similar to homophily, but at an group level

• Related: Social identity theory

Brewer S, “Social Identity Complexity” (2002)

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In-group favoritism

• Favoring members of one's in-group over out-group members.

• This can be expressed in evaluation of others, in allocation of resources, and in many other ways.

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Group polarization

• The tendency for a group to make decisions that are more extreme than the initial inclination of its members.

• These more extreme decisions are towards greater risk if individuals' initial tendencies are to be risky and towards greater caution if individuals' initial tendencies are to be cautious.

• Related:• Groupthink

• Communal reinforcement

Sunstein C, ‘Law of Group polarization’ (2001)

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System biases

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Media bias• Fox news vs. MSNBC

• Also known as Operator bias

• Old phenomenon

• Interesting case study American News LLC• Biased news creation as a business model

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Algorithmic bias

• Algorithmic personalization

• Filter bubble

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FILTER BUBBLE

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FILTER BUBBLE

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FILTER BUBBLE

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Catalysts

Social

identity

complexity

Selective

exposure

Information

overload

Homophily

• Media bias

• Algorithmic

biasGroup

polarization

Biased

assimilation

Cognitive Dissonance

Polarization

Causes

Leads to

Echo

chambersFilter

bubbles

Summary

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Global Village or Cyber-Balkans?

• Internet overcomes geographical borders but can introduce interest based ones

• Communities are now formed on interest not geography

• Homophily can create interest-based groups that are more homogeneous

• Due to bounded rationality and information overload only a limited number of information can be absorbed

• Availability of filters to self-sort into like-minded groups as an enabler

Van Alstyne, M. & Brynjolfsson, E. “Global Village or Cyber-Balkans? Modeling and Measuring the Integration of Electronic Communities.” (1999)

Papacharissi, Z. “The virtual sphere: The internet as a public sphere.” (2002)

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Global Village or Cyber-Balkans?

• Group polarization with more extreme positions as a consequence

• Agent-based simulation based on topic vectors shows that homophilytogether with information overload can create more balkanized groups

• Especially when communication cost grows smaller (e.g., the Web)

Van Alstyne, M. & Brynjolfsson, E. “Global Village or Cyber-Balkans? Modeling and Measuring the Integration of Electronic Communities.” (1999)

Papacharissi, Z. “The virtual sphere: The internet as a public sphere.” (2002)

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Why the Web might increase polarization

• Increase in available information• People tend to read agreeable information first

• Increase in filtering power• People tend to avoid reading conflicting information

• Increase in social feedback (with social media)• Homogeneity and group-think reinforced

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Opinion-formation models

• How are some of the previous concepts captured in popular opinion-formation models?

• Do popular opinion-formation models capture polarization?

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DeGroot’s opinion-formation model

• . J Am Stat Assoc 69(345):118–121

• Individuals are organized in a network structure G=(V,E)

• Individual i has a set of neighbors N(i)={j | (i,j) in E}

• Individual i has opinion x_i(t) at time t=0,1,2,...

• Opinions x_i(t) take values in interval [0,1]

• Individual i weights the opinion of a neighbor individual j by w_ij• also individual i weights her own opinion by w_ii

DeGroot MH (1974) “Reaching a consensus” (1974)

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DeGroot’s opinion-formation model

• DeGrout proposes that individuals update their opinion in each step to the weighted average of their neighbors’ opinions and their own opinions in the previous step

• w_i(t+1) = (w_ii x_i(t) + \sum_{j\in N(i)} w_ij x_j(t)) / (w_ii + \sum_{j\in N(i)} w_ij)

• Social graph models homophily (stronger influence among peers)

• Repeated-averaging process expresses social influence

• Many variants of the DeGroot model have been proposed, e.g., • Individuals have persistent internal opinions, while external opinions are the

result of the repeated-averaging process (Friedkin and Johnsen 1990)

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Polarization in DeGroot’s opinion-formation model

• D. Pranav, A. Goel, and D. T. Lee. Biased assimilation, homophily, and the dynamics of polarization. PNAS 110.15 (2013): 5791-5796.

• Given an opinion vector x, define the network disagreement index (NDI) as

• NDI(x) = \sum_{(i,j\in E)} w_ij (x_i - x_j)^2

• Each term w_ij (x_i - x_j)^2 in NDI is disagreement cost imposed upon i and j

• Result: DeGroot’s process is not polarizing• I.e., the disagreement index at time t+1 is no larger than that at time t

• Lemma : NDI(x(t+1)) \le NDI(x(t)

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Biased assimilation in the opinion-formation model

• D. Pranav, A. Goel, and D. T. Lee. Biased assimilation, homophily, and the dynamics of polarization. PNAS 110.15 (2013): 5791-5796.

• Modify DeGroot’s model to incorporate biased assimilation

• In particular, modify weighted average to be non-linear• Neighbors with similar opinions are weighted more

• So, opinions of individuals are reinforced by like-minded neighbors

• Opinion formation model with biased assimilation can lead to polarization

• Under certain conditions:

• Opinion of moderate individuals can go to extremes (0 or 1)

• Network disagreement index can increase with time t

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Summary of part 1

• Polarization is a result of an interdependent and complex set of phenomena…

• ... of individual, group and systemic biases, which reinforce each other.

• Polarization can exist independently of these mechanisms (i.e., people can simply disagree on an issue)

• but these mechanisms: (a) reinforce polarization (b) allow it to manifest / be detected.

• The interaction between polarization and these mechanisms is complex

• can be modeled

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Part 2 – Case studies of Polarization on the Web

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Outline

• Introduction• Part 1: Social mechanisms and models• Part 2: Case studies of polarization on the Web

• Blogs and News• Web search• Social media

• Part 3: Quantifying polarization• Part 4: Mitigating Polarization• Part 5: Conclusions & Future Work

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Purpose of this part

• Show that polarization on the Web is a real phenomenon

• Social theories we studied previously have been observed in real world

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Echo Chambers in Online Forums

• Studies a forum of climate change skeptics• Group selection (in-group vs out-group formation)• As shown in user interaction with dissidents which are chased away

Edwards, A. "(How) do participants in online discussion forums create ‘echo chambers’?:

The inclusion and exclusion of dissenting voices in an online forum about climate change." (2013)

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Ideological Selectivity in News

• People prefer to read news from sources close to their leaning (selective exposure)

• Online user study with randomized experiments in US• 380 news stories, 1020 users

• Headlines for 4 articles, labeled randomly as coming from 4 different sources:

• Fox News, CNN, NPR, BBC• Control group sees same stories with no media logo

• Tendency to select news based on anticipated agreement as predictedby cognitive dissonance theory

• Effect stronger for hard news

Iyengar, S., & Hahn, K. S. "Red media, blue media: Evidence of ideological selectivity in

media use." (2009)

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Confirmation bias vs. Cognitive dissonance

• Test if opinion reinforcement (confirmation bias) is stronger than aversion to challenge (cognitive dissonance)

• Effect of positive opinion reinforcement seeking (confirmation bias) is stronger than negative opinion challenge avoidance (due to cognitive dissonance)

Garrett, R. K. "Echo chambers online?: Politically motivated selective exposure among Internet news users." (2009)

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• Effect both on news article selection and reading (linger) time (biased assimilation)

How does it effect read time?

Garrett, R. K. "Echo chambers online?: Politically motivated selective exposure among Internet news users." (2009)

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How do events influence polarization? (Search)

• News access pattern after Sandy Hook shooting (browser logs)• People access information that reinforces their stance on controversial

topics• Following shocks, selective exposure increases

Koutra, D., Bennett, P. N., & Horvitz, E. "Events and controversies: Influences of a shocking news event on information seeking." (2015)

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• Single website offers only a narrow point of view (either gun-control or gun-rights) (media bias)

• Users peek outside their bubble if affected personally by the shock

How do events influence polarization? (Search)

Koutra, D., Bennett, P. N., & Horvitz, E. "Events and controversies: Influences of a shocking news event on information seeking." (2015)

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How do events influence polarization? (Twitter)

• What happens to a polarized discussion when there is a sudden increase in attention?

Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. "The Effect of Collective Attention on Controversial Debates on Social Media." (2017)

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How do events influence polarization? (Twitter)

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Group Polarization on Twitter Replies

• Case study: shooting of late-term abortion doctor in US

• Pro-life vs pro-choice groups• Both within-group and cross-group

replies happen• Replies between like-minded

individuals strengthen group identity• Replies between different-minded

individuals reinforce ingroup vs outgroup affiliation

• “People are exposed to broader viewpoints than they were before, but are limited in their ability to engage in meaningful discussion.”

Yardi, S., & Boyd, D. "Dynamic debates: An analysis of group polarization over time on Twitter." (2010)

Ratio of like-minded (light) to opposite-minded

(dark) replies over the first 24 hours.

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Echo Chambers in Blog Writing

• Political blogs during 2004 USpresidential election

• Liberal and conservative blogs link to different news sources (selective exposure + media bias)

• Blogs mostly link internally to thesame side (echo chambers due to homophily)

• Conservative blogs link more and more densely within the community

• Cross-community links used to argue

Adamic, L. A., & Glance, N. "The political blogosphere and the 2004 US election: divided they blog." (2005)

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Echo Chambers in Blog Readership

• Data from large survey (N=36,000)

• Blog readers are attracted to blogs aligned with their political views(94%, Selective exposure)

• Polarization both by party identification andself-reported ideology

Lawrence, E., Sides, J., & Farrell, H. “Self-segregation or deliberation? Blog readership, participation, and polarization in American politics.” (2010)

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Echo Chambers in Blog Readership

• Polarization is larger in blog readers than TV watchers

• Readers of political blogs are more politically polarized than non-readers

• However, blog readers more politically active

Lawrence, E., Sides, J., & Farrell, H. “Self-segregation or deliberation? Blog readership, participation, and polarization in American politics.” (2010)

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Echo Chambers in Blog Comments

• Study comments in blogs

• Most comments on blog posts express agreement rather than disagreement (3 to 1 ratio)

• Echo chambers due to homophily

Gilbert, E., Bergstrom, T., & Karahalios, K. “Blogs are echo chambers: Blogs are echo chambers.” (2009)

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Echo Chambers in Blog Comments

• Level of agreement depends on the topic• Tech least polarized and less echo-chamber-ish• Politics most polarized and echo-chamber-ish• Meta-blogs (blogs about blogs) are extremely

self-referential and a clear echo chamber

• Can detect linguistic features of agreement algorithmically

Gilbert, E., Bergstrom, T., & Karahalios, K. “Blogs are echo chambers: Blogs are echo chambers.” (2009)

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Partisan interactions on Facebook

• Case study in Thai reform-before-election vs. right-to-vote campaign• Facebook pages and users and their network of like/share/comments• Polarization in pages users like/share/comment• Similar results on conspiracy-theory vs science-dissemination pages in US

and Italy• Echo chambers due to homophily

Grömping, M. "‘Echo Chambers’ Partisan Facebook Groups during the 2014 Thai Election." (2014)

Quattrociocchi, W., Scala, A., & Sunstein, C. R. “Echo chambers on Facebook.” (2016)

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Partisan Exposure on Facebook

• US Facebook users with self-reported ideological affiliation • Analysis on hard news (national news, politics, world affairs)• Each news associated with a political alignment

• Average of the affiliation of users who shared the story• Cross-cutting news if the alignment of the news and the user differ

Bakshy, E., Messing, S., & Adamic, L. A. "Exposure to ideologically diverse news and opinion on Facebook." (2015)

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Partisan Exposure on Facebook

• Measure the fraction of cross-cutting news among:

• ones posted in a user’s network (potential)• ones shown in the user’s timeline

(exposed)• one the user clicked on (selected)

• Compared to random from the whole set, each step reduces the exposure and creates a narrower echo chamber

• Largest reduction from network (social), rather than algorithmic (filtering), selective exposure still plays a role

Bakshy, E., Messing, S., & Adamic, L. A. "Exposure to ideologically diverse news and

opinion on Facebook." (2015)

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Partisan Sharing on Facebook

• What happens after partisan exposure?• Selective exposure and media bias

generate polarized sharing on social media• A form of biased assimilation• Evidence of polarized sharing for political

(controversial) news (not for general news)

An, J., Quercia, D., & Crowcroft, J. "Partisan sharing: Facebook evidence and societal consequences." (2014)

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Partisan Sharing on Facebook

• Level of polarization also depends on individual personality• liberals are more partisan

• Activity• active users are more partisan

• Time• lowest during elections, more peeking outside the bubble happens

• Associated with perceiving the opposing side as more biased (group polarization)

An, J., Quercia, D., & Crowcroft, J. "Partisan sharing: Facebook evidence and societal consequences." (2014)

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Echo Chambers on Twitter Retweet

• Political hashtags during 2010 US congressional election• Retweet network (endorsements) shows heavy polarization between democrats and

republicans• Mention network not as polarized, exhibits cross-talk• Similar results for German, Thai, Canadian, Spanish Twitter, etc.

Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., & Flammini, A. "Political Polarization on Twitter." (2011)

Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. "Partisan asymmetries in online political activity." (2012)

Feller, A., Kuhnert, M., Sprenger, T. O., & Welpe, I. M. "Divided They Tweet: The Network Structure of Political Microbloggers and Discussion Topics." (2011)

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Political Controversies on Twitter

• 12 topics discussed on Twitter, retweet networks

• Echo chambers emerge for political topics• Cross-talk for non-political ones• Homophily in action, color encodes user

ideological point as estimated by a model• Polarization measure based on this model

shows most controversial topics

Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. "Tweeting from left to right: Is online political communication more than an echo chamber?." (2015)

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General Controversies on Twitter

• Not just politics vs non-politics• Echo chambers emerge only in controversial debates

• Bi-clustered structure of retweet network• Same for follow network

• Non-controversial debates do not show the same pattern

Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. "Quantifying Controversy in Social Media." (2016)

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Echo Chambers on Twitter Follow

• Twitter social-network clusters politically homogeneous due to homophily

• 10 controversial political topics in US, analyzes the fraction of liberal/conservative-leaning messages within the cluster

• Echo chambers: When discussing a given topic (GOP), users are exposed to mostly one-sided views within their friends clusters (conservative in GOP1 and GOP2, liberal in GOP3)

• Low level of cross-ideological exposure

Himelboim, I., McCreery, S., & Smith, M. "Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter."

(2013)

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Echo Chambers on Twitter Follow

Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. "Quantifying Controversy in Social Media." (2016)

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Echo Chambers on Social Media

• Social media exposes to a narrower range of information sources (filter bubble?)

• Data from referrers in a click log• Entropy of links clicked from

social media is lower than either from email or Web search

• Even more evident for news links• Evidence of collective social bubble

Nikolov, D., Oliveira, D. F., Flammini, A., & Menczer, F. "Measuring Online Social Bubbles." (2015)

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End of Part 2

• Discussed Homophily, Selective Exposure, Biased Assimilation, and Group Selection, media bias, etc

• Their effect in creating Echo Chambers and Filter Bubbles, and in fostering Group Polarization on the Web (Blogs, Search, Social Media)

• The role of Algorithmic and Media Bias, and the influence of Information Overload

• Most studies agree: can find signs of polarization• Difference in what is measured and how, and in ascribing causes• Next part will be more technical

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Answer with a yes/no

• Is polarization detrimental to the society?

• Do we have to worry about polarization?

• Is news consumption partisan?

• Is the internet making users more biased?

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Studies on polarization are polarized!!

•For almost every study we mentioned, there is a counter study

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Is polarization detrimental to the society?

• The fragmentation debate generally assumes that deliberation within groups of similar identity, among ‘like-minded’ people, is ultimately a serious danger to democracy and society at large

• The reasoning is that such deliberation leads to the formation of ‘extreme’ views, which in turn leads to ‘polarization’ between groups, followed by a failure of the public sphere and, finally, to social destabilization

• This idea is wrong. For example, deliberative enclaves can foster ideas that are not mainstream (anti-slavery, gender equality), and extremes might be positive

• Contestation, rather than agreement, should be the basis of democracy. Diversification rather than homogenization. In this sense, Internet is helping

Dahlberg, L. “Rethinking the fragmentation of the Cyberpublic: from consensus to contestation.” (2007)

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Is News Consumption partisan?

• Web-browsing history, through Bing Toolbar

• Compared to aggregators and direct browsing…

• Social media and search lead to larger ideological distance (‘segregation’) among users

• But also larger exposure to news from opposing side (‘within-user variation’)

Flaxman, Goel, Rao, ‘Filter bubbles, echo chambers, and online news consumption’, 2016

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Is the internet causing polarization to increase?

• Data: American National Election Studies

• Increased polarization over the years

• But mostly due to old cohort, with lower

• internet use.

Boxell, Gentzkow, Shapiro, ‘Is the internet causing political polarization? Evidence from demographics’, 2017

Polarization index over the years.

Index is a combination of nine different measures

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Is Personalization causing filter bubbles?

• Partisan users of personalized news portals do not report narrower partisan exposure than others.

• They report: • higher number and range of news sources;

• less information overload.

• Possible explanation: • personalization might allow users to explore wider range of sources, as it cuts

through the clutter.

Beam, Kosicki, ‘Personalized news portals: Filtering Systems and Increased News Exposure’, 2014

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Is there a backfire effect?

• “backlash may occur under some conditions with some individuals, it is the exception, not the rule.”

• Three representative sample surveys• Capital punishment

• Minimum wage

• Gun control

• No evidence of backfire effect!

Guess A, et al “The Exception, Not the Rule? The Rarely Polarizing Effect of Challenging Information” (2016)

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Why the stark difference?

• Different data sources

• Different ways of measuring polarization

• Different assumptions

• Different settings

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Break

• Introduction• Part 1: Social mechanisms and models• Part 2: Case studies of polarization on the Web• Part 3: Quantifying polarization• Part 4: Mitigating Polarization• Open Questions & Future Work

See you back at 11:00

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Part 3 – Quantifying Polarization

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Outline

• Introduction

• Part 1: Social mechanisms and models

• Part 2: Case studies of polarization on the Web

• Part 3: Quantifying polarization• Identifying and Quantifying

• Content vs. Network based methods

• User polarization

• Polarization over time

• Part 4: Mitigating Polarization

• Part 5: Conclusions & Future Work

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Structure

• What is a polarized topic?• Defining what polarized/controversial is hard/subjective.

• Topic polarization• Identifying/Quantifying polarization

• Content based measures

• Network based measures

• User polarization

• Polarization over time

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Why do we want to do this?

• To create a balanced news diet

• To design recommender systems

• To model / understand social processes

• To reduce polarization

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Identifying polarized topics

• Can we identify a polarized discussion?

• How polarized is a discussion? (quantify)

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Defining polarization is hard

• E.g. read ‘Disambiguation of social polarization concepts and measures’, Bremson et al (2016).

• nine senses of polarization

• different definitions based on the domain

• Distribution of attitudes: creating a histogram of the number of individuals holding a specific attitude value along the spectrum

• Controversy vs. polarization

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Measurement of Polarization

• Axioms of polarization• Similar to Gini coefficient

• Take antagonism into account

Esteban, Ray. ”On the measurement of polarization." (1994)

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Identifying polarization - content

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Sentiment variance

• Controversial issue - a concept that invokes conflicting sentiments

• Subtopic - factor that gives a particular sentiment (+ve or -ve)

• Assumption - assume that a controversial issue receives sentiment of various sorts

• (e.g. positive vs. negative feelings, pros vs. cons, or rightness vs. wrongness in their judgments)

• Similar results observed by Garimella et al. WSDM 2016, Klenner et al. 2014

Choi Y., Jung Y., and Myaeng S. "Identifying controversial issues and their sub-topics in news articles.." (2010)

Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. "Quantifying Controversy in Social Media." (2016)

M. Klenner, M. Amsler, N. Hollenstein, and G. Faaß, “Verb Polarity Frames: a New Resource and its Application in Target-specific Polarity Classification” (2015)

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Sentiment variance

• Method:• Identify candidate entities (noun phrases)

• Compute sentiment in sentences involving these entities

• If positive_sentiment + negative_sentiment > \delta and |positive - negative| > \gamma

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Controversy language in news

• Controversial issues have:• higher biased words

• more negative terms

• less strong emotions

• “we show that we can indicate to what extent an issue is controversial, by comparing it with other issues in terms of how they are portrayed across different media.”

Mejova Y, Zhang A, Diakopoulos N, Castillo C, “Controversy and Sentiment in Online News” (2014)

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Detecting Controversy on the Web

• Find out if a web page discusses a (known) controversial topic.

• Idea: Map topics (named entities) in a web page to Wikipedia articles• A web page is controversial if it is similar to a controversial Wikipedia article.

• E.g. If a news article mentions Abortion it is labelled controversial.

• Related: Jang et al. show that in addition to this, language models can be built to detect controversy.

• Related: There is a lot of work on identifying controversial topics on wikipedia.

• Idea: Edit wars, hyperlink structure, etc

Dori-Hacohen, S., & Allan, J. "Detecting Controversy on the Web." (2013)

Jang M, Foley J, Allan J., “Probabilistic Approaches to Controversy Detection” (2016)

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Identifying - network

• Methods based on network structure

• Idea: Controversial/polarized topics have a clustered structure in their discussions

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Political Polarization on Twitter

• Retweet network has a bi-clustered structure• retweet network exhibits a highly modular structure, segregating users into

two homogenous communities corresponding to the political left and right

• Users mention/reply to others from their opposing viewpoint

Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., & Flammini, A. "Political Polarization on Twitter." (2011)

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A Motif-based Approach for Identifying Controversy

• Define reply trees

• Identify frequency of motifs in these trees

Coletto M., Garimella K., Luchesse C., Gionis A., A Motif-based Approach for Identifying Controversy (2017)

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Motifs

Controversial Non-Controversial

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Motifs

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Quantifying polarization

• Defining what polarized/controversial is hard/subjective.

• Quantifying might help to get a sense of the degree

• Basic idea:• Interactions have a clustered structure

• Can we measure how well clustered the interactions are?

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Quantifying polarization

• Conover 2011 - modularity

• Modularity -• the fraction of the edges that fall within the given groups minus the expected

fraction if edges were distributed at random.

• Compares the number of edges inside a cluster with the expected on a random graph

• Captures the strength of division of a network into modules

Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., & Flammini, A. "Political Polarization on Twitter." (2011)

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Quantifying polarization

• Conover 2011 - modularity

• Modularity -• the fraction of the edges that fall within the given groups minus the expected

fraction if edges were distributed at random.

• Compares the number of edges inside a cluster with the expected on a random graph

• Captures the strength of division of a network into modules

Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., & Flammini, A. "Political Polarization on Twitter." (2011)

Modularity: 0.48

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Modularity is not a direct measure of Polarization

• Want to capture the in group vs out group interaction preference

Modularity: 0.42 Modularity: 0.24

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A Measure based on community boundary

• have at least one edge that connecting to the other community

• have at least one edge connecting to a member of its community which does not link to the other community

• P(v) = internal(v)/(external(v) + internal(v))

Guerra P. H. C, Meira Jr W., Cardie C, and Kleinberg R. "A Measure of Polarization on Social Media Networks Based on Community Boundaries." (2013)

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A Measure based on community boundary

• P(v) = internal(v)/(external(v) + internal(v))

• P(v) > 0 → v prefers internal connections (antagonism?)

• P(v) < 0 → v prefers connections with members of the other group (increased homophily!)

Guerra P. H. C, Meira Jr W., Cardie C, and Kleinberg R. "A Measure of Polarization on Social Media Networks Based on Community Boundaries." (2013)

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Label propagation

• Identify a set of ‘seed’ users and propagate until convergence

Morales A. J., Borondo J., Losada J. C., Benito R. M. . "Measuring political polarization: Twitter shows the two sides of Venezuela." (2015)

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Based on information flow

• Random walk controversy measure (RWC)

• Authoritative users exist on both sides of the controversy

• How likely a random user on either side is to be exposed to authoritative content from the opposing side

Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. "Quantifying Controversy in Social Media." (2016)

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Random walk controversy score

107

X Y

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Random walk controversy score

108

X Y

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Random walk controversy score

109

X Y

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Random walk controversy score (RWC)

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User level polarization

• Can we assign how a user will lean towards a polarized topic?

• Mostly - ‘can we identify political affiliation of users on twitter’

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User level - content

• Simple binary classifier based on various features: text, hashtags, clusters, etc.

• Cohen and Ruths show that its not as simple and depends on who you measure the polarity for and what you train on.

Conover, M. D. et al. . “Predicting the political alignment of twitter users” (2011)

Cohen, Ruths. “Classifying Political Orientation on Twitter: It’s Not Easy!” (2013)

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Bayesian ideal point estimation

• Ideal point estimation (continuous) vs ideology/polarity (binary)

• Assumption: Twitter users prefer to follow politicians whose position on the latent ideological dimension are similar to theirs.

• Parameters to control for popularity of the politician and activity of the user

• E.g. everyone follows @barackobama

• A politically active user is more likely to follow both sides

Barbera P. "Birds of the Same Feather Tweet Together. Bayesian Ideal Point Estimation Using Twitter Data." (2013)

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Combine content and network

• Not necessarily political affiliation, but bias towards a polarizing topic

• Method:• Find bias anchors (e.g. #prochoice vs #prolife)

• Construct a ‘user similarity network’ based on content and retweets

• Propagate bias on a this user similarity network

• Correct for noise

Lu H., Caverlee J., and Niu, W. “Biaswatch: A lightweight system for discovering and tracking topic-sensitive opinion bias in social media” (2015)

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Polarization over time

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Political Polarization in the American Public

• ~10k adults nationwide

• 10 political values questions

Pew Research Center, “Political Polarization in the American Public” (2014)

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Political Polarization in the American Public

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Political Polarization in the American Public

• People with less interest in politics are less involved

• People with higher interest are more involved and more polarized• these people vote and hence matter the most

• Polarized politics = polarized everything

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Partisanship of US House of Representatives

Andris C., Lee D., Hamilton M., Martino M., Gunning C., Selden J.. "The Rise of Partisanship and Super-Cooperators in the U.S. House of Representatives." (2015)

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Partisanship of US House of Representatives

Andris C., Lee D., Hamilton M., Martino M., Gunning C., Selden J.. "The Rise of Partisanship and Super-Cooperators in the U.S. House of Representatives." (2015)

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Long-term trends in polarization on Twitter

• Are twitter users more/less likely to follow/retweet political figures/media accounts from both sides now compared to 8 years ago?

• Are users more/less likely to use biased content? (hashtags)

Garimella, K., & Weber, I. "A long term Analysis of Polarisation on Twitter." (2017)

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Long-term trends in polarization on Twitter

• Are twitter users more/less likely to follow/retweet political figures/media accounts from both sides now compared to 8 years ago?

• Are users more/less likely to use biased content? (hashtags)

Garimella, K., & Weber, I. "A long term Analysis of Polarisation on Twitter." (2017)

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Summary

• Methods to identify polarized topics• And quantify the degree of polarization.

• From content, network.

• Methods to identify the polarity of users• From content and network

• Evolution of polarization over time

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Part 4Mitigating Polarization

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Outline

• Introduction

• Part 1: Social mechanisms and models

• Part 2: Case studies of polarization on the Web

• Part 3: Quantifying polarization

• Part 4: Mitigating Polarization• Examples of existing solutions

• Research methods

• Why is it hard?

• Part 5: Conclusions & Future Work

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Purpose of this part

• Sometimes people are not aware of the other side (filter bubbles/echo chambers)

• Sometimes they are aware but do not want to know

• Goal: • Allow users to see other viewpoints (outside their bubble).

• Inform web users of the biases in their news/information diets.

• Correct misinformation.

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Seeing the other side

‘The two most discussed concerns this past year

were about diversity of viewpoints we see (filter

bubbles) and accuracy of information (fake news)’.Mark Zuckerberg, Facebook Community Letter, 16 February 2017

‘Filter bubbles are a serious problem with news’.Bill Gates, Quartz, 21 February 2017

‘The internet has exacerbated phenomenon of

people having conversations in their own silos’.

‘If you’re liberal, then you’re on MSNBC. If you’re a

conservative, you’re on Fox News’. Barack Obama, 24 April 2017

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Bubble Bursting Initiatives

• Online ‘bubbles’ are seen as a problem.

• Initiatives spring up to counter the problem.

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Wall Street Journal

Blue Feed, Red Feed

• Liberal & Conservative Facebook Feed

• Curated using a list of left/right websites

• Aims to show how different the feed can be for different users

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The Guardian

Burst Your Bubble

• The Guardian is left-wing

• The column shows selected conservative articles from around the web

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The New York Times

• Right and Left: Partisan Writing You Shouldn’t Miss

• Selected opinion articles

• Right, Center, Left

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Buzzfeed – Outside your bubble

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EscapeYourBubble.com

• Browser (Chrome) Extension

• Injects content into the Facebook feed

• Republican / Democrat-leaning articles from the other side

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politecho.org

• Browser (Chrome) Extension

• Shows political distribution of own Facebook feed vs. that of friends

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FlipFeed

• Browser (Chrome) Extension

• Allows Twitter users to see a feed that resembles that of another user

• Laboratory for Social Machines at MIT Media Lab

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Read Across the Aisle

• Mobile (iPhone) app

• News reader for select sources

• Keeps track of personal reading history

• Informs user of news diet bias

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Balancer

• Browser (Chrome) Extension

• Monitors news articles visited by user

• Reports left-vs-right balance

• Doesn’t help much in changing users perspective!

S. A. Munson, S. Y. Lee, P. Resnick. "Encouraging Reading of Diverse Political Viewpoints with a Browser Widget" (2013)

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Algorithmic Recommendations

Previously discussed initiatives consist ofEditorial content

&Self-monitoring tools

How would we produceautomatic, algorithmic recommendations?

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Graph-based approach

PreviouslyEndorsement (retweet) graph

reveals polarization

IdeaRecommend connections to

Destroy bi-clustering structure

Garimella, De Francisci Morales, Gionis, Mathioudakis, ‘Reducing Controversy by Connecting Opposing Views’, 2016

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Graph-based approach

• How

• Connecting central nodes would reduce polarization score the most...

• But is also less likely to materializeWould Trump retweet Obama?

• Find new edges that satisfy two criteriaCentrality & high probability to connect

Garimella, De Francisci Morales, Gionis, Mathioudakis, ‘Reducing Controversy by Connecting Opposing Views’, 2016

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Examples

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Intermediate topics exist

• #prolife users hardly interact with #prochoice users in a debate context

• Engage in conversation about other interests, such as #musicmonday

• Find these ‘intermediate topics’ automatically

Graells-Garrido et al, ‘People of Opposing Views can Share Common Interests’, 2014

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The role of language

• We’ve already seen selective exposure on search engines

• Found evidence that the effect could be mitigated via language• There was higher chance to click if the language model was similar to their

side’s language model (used similar terms)

• Among pages of opposite viewpoint, ones with similarity higher than average had 38% larger probability to be clicked than the rest

Elad Yom-Tov, et.al, ‘Promoting Civil Discourse through Search Engine Diversity’, 2014

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Its not so easy!

• We do not have a good idea about the effectiveness of the aforementioned efforts and algorithms in real settings

• Acceptance of content that challenges own viewpoint might depend on other factors - E.g., language (previous slide) or trust into content source (next slide), user biases, etc.

• Remember Biased Assimilation?

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https://www.americanpressinstitute.org/publications/reports/survey-research/trust-social-media/

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Correcting misinformation

• A recent study shows that partisans do not like fact checking that challenges their views

• Analyzed fact-checking tweets of the 2012 campaign.

• Finding 1: Fact-checking results is used by partisans in a partisan manner

• Partisans hand-pick and promote (retweet) fact-checking tweets that serves their view.

• Finding 2: Fact-checkers receive hostility from the side that is negatively affected by fact-checking.

J. Shin, K. Thorson, ‘Partisan Selective Sharing: The biased Diffusion of Fact-Checking Messages on Social Media’, 2017

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Things could backfire

• Research from psychology shows thatattempting to change the worldview of a person might lead to reinforcing their view (*)

• Vaccination fact drive by CDC lead to reduction in the number of children vaccinated!

(*) Short literature survey: J. Cook, S. Lewandowsky, The Debunking Handbook, 2011. http://sks.to/debunk

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Summary

• Reducing polarization is necessary

• Lots of initiatives sprung up after the US elections/Brexit shocks

• Algorithms can help in connecting to the other side

• Connecting people with the other side is more a psychological challenge than an algorithmic one!

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Part 5Conclusions

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Outline

• Introduction

• Part 1: Social mechanisms and models

• Part 2: Case studies of polarization on the Web

• Part 3: Quantifying polarization

• Part 4: Mitigating Polarization

• Part 5: Conclusions & Future Work

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Conclusions

• Polarization and associated phenomena are an active area ofinterdisciplinary research

• We saw efforts to...• Study instances of polarization on the Web

• Distill the mechanisms behind polarization

• Quantify polarization in algorithmic manner from web activity

• Mitigate its effects

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List of take aways

• Definition of polarization and related terms

• Why we should worry about increasing polarization

• Social theories underlying the ecosystem of polarization

• Case studies to show the study of these principles

• How do you quantify the degree of polarization

• How do we reduce polarization

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Future work

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Ethics of bubble bursting in Search

• Search engine → Filter bubble → Confirmation bias → Echo chambers

• How can search engines present results for controversial topics better?

• Medical controversies: Should we censor “unproven” claims?

• Challenges• Identify query about controversial topic (more on this later)• Cultural elements in deciding what is controversial

• What is controversial in Iran vs in Israel?

• Decide what to do. Show both sides? Even if one side can be harmful? (vaccines-autism)

• No solution, however exposing users to opposing opinions increases their interest in seeking diverse opinions, and their interest in news in general

Dori-Hacohen, S., Yom-Tov, E., & Allan, J. "Navigating Controversy as a Complex Search Task." (2015)

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Future Work

• There are still open questions about polarization and related mechanisms / phenomena

• And the Web is constantly evolving

• We’ve seen examples of conflicting results!

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Future Work

• What research questions would you pursue next?

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Future research directions

• Modeling polarization• Evidence from real data

• E.g. What happens after being exposed to content from the other side

• Psychological/Design challenges • (users do not want to see content from the other side)

• Dynamics of polarization/echo chambers• Do the users come out of them automatically?

• Can they come out?

• Can we predict this based on their news diet?

• Biases in data• Impact of bots?

• Social data is not representative - what about minority voice

• US bias

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Future directions

• Challenges in the current context• Impact of high profile polarizing figures

• If things change at a rapid pace, is it worth studying?

• Is it only politics and related to politics?• How does political polarization effect other domains?

• Combine offline and online data

• Real life consequences of polarization

• Ethics of bubble bursting

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Questions?/Suggestions?

• What could we cover (more)

• What could have been stressed

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Thank you!Slides at bit.ly/polarization-icwsm