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

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

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Controversy: from Latin contra (against)
vertere (turn) “turned against, disputed”

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The gap grows

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Goal
Understand how controversies  
unfold in social media

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Outline

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Outline
Quantify

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Outline
Quantify
Graph

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Outline
Quantify
Graph
Polarization
Measure

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Outline
Quantify Evolve
Graph
Polarization
Measure

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Outline
Quantify Evolve
Time
Graph
Polarization
Measure

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Outline
Quantify Evolve
Time
Graph
Polarization
Measure
Collective
Attention

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Outline
Quantify Evolve
Cause
Time
Graph
Polarization
Measure
Collective
Attention

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Outline
Quantify Evolve
Cause
Time
Graph
Opinion
Polarization
Measure
Collective
Attention

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Outline
Quantify Evolve
Cause
Time
Graph
Opinion
Echo
Chambers
Polarization
Measure
Collective
Attention

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Quantifying Controversy 
in Social Media  
 
WSDM 2016, TSC 2018

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Black/Blue or White/Gold?

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Desiderata
In the wild
Not necessarily political
No domain knowledge
Language independent
Allows comparison

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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)

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Endorsement Graph
#марш #sxsw

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Pipeline
• Retweets
• Follow
• Mentions
• Content
• METIS
• Spectral
• Label  
propagation
• Random walk
• Edge betweenness
• 2d embedding
• Sentiment variance

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Example

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Example
#beefban #марш #sxsw #germanwings

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

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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

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Random Walk

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Random Walk
X Y

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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

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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)

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Controversy Detection

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Controversy Score

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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

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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

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

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The Effect of Collective Attention on
Controversial Debates on Social media

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"Trump taxes" on Google

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"Trump taxes" on Google
Rachel Maddow
show on 2005
tax return

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Obamacare on Twitter

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Gun Control on Twitter

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Literature so far
Controversial debates examined in isolation
As static snapshots

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Contribution
Controversial debates are dynamic
They change with collective attention
Analyze controversial debates over time
Particularly when collective attention increases
When external ‘event’ happens

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Data
Twitter
4 longitudinal polarized topics
Obamacare, Abortion, Gun control, Fracking
5 years (2011 -- 2016)
Hundreds of thousands of users
Millions of tweets

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Deﬁnitions
Retweet Graph
Reply Graph
Core Users

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Retweet Graph

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Reply graph

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Core

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Core
Core Users

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Experiments

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Experiments
Compare these 
two points

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Retweet Graph

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Retweet Graph
1) New users enter
the discussion

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

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Retweet Graph
2) Most retweets to
existing core users
1) New users enter
the discussion
3) Cross-side
retweets decrease

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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

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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

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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.

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Reply Graph
Cross-side edges increase: more discussion
Attention
increases

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Content
Pro Life
Pro Choice
Normal 
Condition
Attention 
Increase

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Content
Pro Life
Pro Choice
Normal 
Condition
Attention 
Increase
Content becomes uniform across the sides

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Long-Term Polarization

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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!

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Political Discourse on Social Media
Echo Chambers, Gatekeepers,  
and the Price of Bipartisanship
WWW 2018

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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

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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

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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

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Production/Consumption
Consumption
What you receive in your feed
What your followees tweet
Production
What you tweet

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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)

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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

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δ-partisanship
f
s
n-
r
e
m-
.,
s
n
e
e
k
Figure 1: Example showing the denition 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.

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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

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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

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(a) (b) (c) (d) (e)
(f) (g) (h) (i) (j)
Figure 3: Distribution of production and consumption polarity, for P (rst row) and NP (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)

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Correlation: Gun Control

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(f) (g) (h) (i) (j)
(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, dierently from the rest of the side they align with, they
show a lower clustering coecient, 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 signicant dierence: partisans have a higher PageRank
compared to gatekeepers (gure not shown). This eect is more
Variance
(f) (g) (h) (i) (j)
(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, dierently from the rest of the side they align with, they
show a lower clustering coecient, 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 signicant dierence: partisans have a higher PageRank
compared to gatekeepers (gure not shown). This eect is more

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Variance
(b) (c)

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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 signicantly 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
nicantly 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 signicantly 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
nicantly 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

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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)

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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 signicantly 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 signicantly lower.
Features Partisans Gatekeepers
PageRank 3 3
clustering coecient 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 signicantly lower

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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

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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

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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?

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Thanks!
Questions please!
64
@gdfm7
[email protected]

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

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

Gianmarco De Francisci Morales

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