quantifying and bursting the online filter bubble
TRANSCRIPT
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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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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
