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QUANTIFYING AND BURSTING THE ONLINE FILTER BUBBLEKIRAN GARIMELLAKCL, 13 FEB 2017

2HELLO!

2011

2013

2014

BACHELORS &

MASTERS IN

COMPUTER SCIENCE

BARCELONA

DOHA, QATAR

HYDERABAD,INDIA HELSINKI,

FINLAND

RESEARCH ENGINEER

RESEARCH ASSOCIAT

EPHD

ADVISOR: ARISTIDES

GIONIS

EXPECTED: SEPT 2017

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OVERVIEW▸ Motivation▸ Summary of the thesis▸ Shallow dive into one sub-topic

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SOCIAL MEDIA BUBBLE

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FILTER BUBBLE

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ECHO CHAMBERS

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THE POLARIZATION CYCLE

USER HOMOPH

ILY

ALGORITHMIC

PERSONALIZATION

Increased Polarization

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POLARIZATION - TWITTER

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BLOGS

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INSTAGRAM

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US SENATE VOTES

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HOW CAN WE DEAL WITH THE POLARIZATION ON SOCIAL MEDIA?

THIS THESIS

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RESEARCH QUESTIONS1. Identify polarized discussions on social media and

quantify their severity.2. Track evolution of polarized discussions and

understand their properties.3. Design ways to reduce the polarization.

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1. IDENTIFYING AND QUANTIFYING POLARIZED DISCUSSIONS

▸ Using different types of user interactions▸ A. Retweet network▸ B. Reply network

RESEARCH QUESTION I

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1 A. QUANTIFYING CONTROVERSY ON SOCIAL MEDIA [WSDM’16, CSCW’16]

IDENTIFYING AND QUANTIFYING POLARIZED DISCUSSIONS

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QUANTIFYING CONTROVERSY▸ In the wild▸ Not necessarily political controversies▸ Compare across controversies▸ Language independent

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NEED NOT BE POLITICAL …

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NEED NOT BE POLITICAL …

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COMPARING ACROSS CONTROVERSIES

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SOLUTION▸ Graph based formulation▸ Model conversations using a retweet

graph▸ Nodes: users, Edges: retweets

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EXAMPLE

controversial non-controversial

retweet graphs

#beefban #russia march #sxsw #germanwings.

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EXAMPLE

controversial non-controversial

retweet graphs

follow graphs

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PIPELINE

Any Clustering algorithm

• Retweets

• Mentions

• Social network

• Content

• Random walk

• Edge-betweenness

• 2d-embedding

• Sentiment variance

Controversy score

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SENTIMENT VARIANCE▸ Controversy = intensified sentiments▸ Positive and negative sentiments on each side are

higher compared to non-controversial issues▸ Language dependent

25IDENTIFYING AND QUANTIFYING POLARIZED DISCUSSIONS

1 B. A MOTIF-BASED APPROACH FOR IDENTIFYING CONTROVERSY [UNDER REVIEW]▸ Use motifs defined on the reply networks

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REPLY NETWORKS

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CONTROVERSIAL NON-CONTROVERSIAL

REPLY NETWORKS

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MOTIFS

29RESEARCH QUESTION II

2. POLARIZATION OVER TIME▸ A. How do polarized debates change with interest▸ B. Has polarization on Twitter increased over the

years

30POLARIZATION OVER TIME

2 A. HOW DO POLARIZED DEBATES CHANGE WITH INTEREST [UNDER REVIEW]▸ Polarization increases with interest▸ Most retweeting activity occurs within a side▸ Endorsement network becomes more hierarchical

and a large fraction of edges go from periphery to core

▸ Content becomes more similar between the two sides

31POLARIZATION OVER TIME

2 B. HAS POLARIZATION INCREASED OVER THE YEARS? [UNDER REVIEW]▸ Are Twitter users less likely to follow/retweet users

from both sides?▸ Are users less likely to use biased content?▸ Large scale study – 700,000 users, 2B tweets, 8

years

32RESEARCH QUESTION III

3. REDUCING POLARIZATION▸ A. Reducing Controversy by Connecting

Opposing Views

▸ B. Balancing Information Exposure in Social Networks

33REDUCING POLARIZATION

3 A. REDUCING CONTROVERSY BY CONNECTING OPPOSING VIEWS [WSDM’17]

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POLARIZATION - TWITTER

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HOW CAN WE BRIDGE THE DIVIDE?

THIS PAPER

36REDUCING CONTROVERSY BY CONNECTING OPPOSING VIEWS

▸ Connect the two sides▸ Model interactions as a graph

▸ Retweet graph Nodes: users, Edges: retweets

HOW CAN WE BRIDGE THE DIVIDE?

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▸ Quantify degree of polarization in a network▸ How well does information flow between the two

sides?

MEASURE OF POLARIZATION

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RANDOM WALK CONTROVERSY SCORE▸ 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

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RANDOM WALK CONTROVERSY SCORE (RWC)

X Y

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RANDOM WALK CONTROVERSY SCORE (RWC)

X Y

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RANDOM WALK CONTROVERSY SCORE (RWC)

X Y

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RANDOM WALK CONTROVERSY SCORE (RWC)

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RWC SCORE: 0.95RWC SCORE: 0.12

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PROBLEM▸ Given a graph▸ Two sides▸ RWC score

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FIND THE k BEST EDGES TO ADD TO THE GRAPH THAT MAXIMIZE THE REDUCTION IN RWC SCORE

46REDUCING POLARIZATION

Side 1 Side 2

REDUCING CONTROVERSY BY CONNECTING OPPOSING VIEWS

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▸ Greedy▸ Look for all pairs of nodes

▸ Find the k pairs that give the highest reduction in RWC

▸ O(n2), n: number of nodes

ALGORITHMS

48REDUCING CONTROVERSY BY CONNECTING OPPOSING VIEWS

Side 1 Side 2

OUR ALGORITHM

The best edges are between the highest degree nodes

49REDUCING CONTROVERSY BY CONNECTING OPPOSING VIEWS

Side 1 Side 2

OUR ALGORITHM

The best edges are between the highest degree nodesO(p2), p << n

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▸ High degree users Highly retweeted users▸ We can not recommend @realDonaldTrump to follow

@BarackObama▸ Not likely to materialize

NOT PRACTICAL

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▸ Take into account the probability of the user liking the recommendation

▸ Not all users are the same▸ Popular users▸ Highly polarized users

▸ Compute polarity scores for users

ACCEPTANCE PROBABILITY

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ACCEPTANCE PROBABILITY

POLARITY SCORE: -0.99

POLARITY SCORE: 0.95

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based on connections

based on retweets

p(u, v) =

ACCEPTANCE PROBABILITY▸ Learn probabilities from data

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DEMO

55REDUCING POLARIZATION

Side 1 Side 2

3 B. BALANCING INFORMATION EXPOSURE IN SOCIAL NETWORKS

▸ Find a set of seed nodes that can balance the exposure of information

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THANK YOU!@gvrkiran

kiran.garimella@aalto.fi