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Data-driven modeling
of Complex Systems
Walter Quattrociocchi

walterquattrociocchi@gmail.com
OBSERVING SOCIAL PHENOMENA
The Twitter of Babel: Mapping World Languages through
Microblogging Platforms
(Mocanu et al PlosOne 2013)
Sentiment of emojis
(P. Novak et al. PLoS One 2015 )
The dynamics of protest recruitment through an
online network.
(S. González-Bailón et al” Sci rep 1 (2011).)
Structural Patterns of the Occupy Movement on Facebook
(Del Vicario et al. Complex Networks and their application 2015)
Agenda Setting is the
process of the mass media
presenting certain issues
frequently and prominently
with the result that large
segments of the public
perceive those issues as more
important than others.
MORE COVERAGE
THE ROLE OF THE MEDIA
MORE IMPORTANT
OLD MEDIA
- Follow the “Ritual of
Objectivity”
- Publication patterns are
driven by most followed
sources (imitation) (Marlow
2005)
NEW MEDIA
- Information production is the
work of interconnected actors
spanning over organizations,
professional identity and
geographical location
A SHIFT OF PARADIGM
MEDIATED DISINTERMEDIATED
“We're not thinking about ourselves as a community
— we're not trying to build a community — we're not
trying to make new connections. [...]
What we're trying to do is just make it really efficient
for people to communicate, get information and
share information.
We always try to emphasize the utility component.”
Mark Zuckerberg Jul. 2007
WHAT ABOUT THE QUALITY OF INFORMATION?
METHODOLOGY
Questions framing (Sociology, Comm., Psych., Semiotics, Anthropology )
Data Collection and Transformation (Surveys,Algorithms, Databases)
Quantitative Analysis (Statistical mechanics, Net Sci, Machine Learning)
Modeling and Validation (Statistical mechanics, Multi agent systems)
SETTING UP EXPERIMENTS WITH BIG DATA
FOCUS
(MIS)INFORMATION SPREADING ONLINE
(Social Contagion, Collective Framing of Narratives,
Content Consumption, Opinion Dynamics)
CONFIRMATION BIAS AND INFORMATION CONSUMPTION
The cognitive attitude to search for, interpret, favor, and
recall information in a way that confirms one's beliefs
THE DATASET(s)
FB ITALY TOTAL SCIENCE CONSPIRACY TROLL
Pages 73 34 39 2
Posts 271,296 62,705 208,591 4,709
Likes 9,164,781 2,505,399 6,659,382 40,341
Comments 1,017,509 180,918 836,591 58,686
Likers 1,196,404 332,357 864,047 15,209
Commentsers 279,972 53,438 226,534 43,102
FB USA TOTAL SCIENCE CONSPIRACY DEBUNKING
Pages 478 83 330 66
Posts 679,948 262,815 369,420 47,780
Likes 603,332,826 453,966,494 145,388,117 3,986,922
Comments 30,828,705 22,093,692 8,304,644 429,204
Likers 52,172,855 39,854,663 19,386,131 702,122
Commentsers 9,790,906 7,223,473 3,166,726 118,996
Facebook ITALY and USA from Jan 2010 to Dec 2014
Bessi, A., Petroni, F., Del Vicario, M., Zollo, F., Anagnostopoulos, A., Scala, A., ... & Quattrociocchi, W. (2015, May). Viral
misinformation: The role of homophily and polarization. In Proceedings of the 24th International Conference on World Wide Web
(pp. 355-356). ACM.
webSci@WWW (Bessi et al. 2015)
Bessi, A., Petroni, F., Del Vicario, M., Zollo, F., Anagnostopoulos, A., Scala, A., ... & Quattrociocchi, W. (2016). Homophily and
polarization in the age of misinformation. The European Physical Journal Special Topics, 225(10), 2047-2059.
CONTENT CONSUMPTIONS AND FRIENDS
Polarization on contents. Probability density
function (PDF) of users’ polarization. Notice the
strong bimodality of the distribution, with two sharp
peaks localized at 0 <︎ ρ <︎ 0.005 (science users) and
at 0.95 ︎< ρ < ︎ 1 (conspiracy users).
Homophily. Fraction of polarized friends
with the same polarization respect to the
number of likes log(θ(u)) of user u.
RESPONSE TO 4,709 INTENTIONAL FALSE CLAIMS
(TROLLS)
Polarized users on false information.
Percentage of likes and comments on intentional false information posted by a satirical
page from polarized users of the two categories.
Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015).

Science vs conspiracy: Collective narratives in the age of misinformation. PloS one, 10(2), e0118093.
Mocanu, D., Rossi, L., Zhang, Q., Karsai, M., & Quattrociocchi, W. (2015).
Collective attention in the age of (mis) information. Computers in Human Behavior, 51, 1198-1204.
8,899%
91,101%
Science Conspiracy
RESPONSE TO 47,780 DEBUNKING POSTS (1)
4,953%
95,047%
Science Conspiracy
LIKES
COMMENTS
Debunking information are ignored by users in the conspiracy
echo-chamber
(out of 9,790,906 polarized conspiracy users only 5, 831 interact )
Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., ... & Quattrociocchi, W. (2017).
Debunking in a world of tribes. PloS one, 12(7), e0181821.
0.0
0.2
0.4
0.6
10−3
10−2
10−1
100
101
rate
PDF
Likes
0.0
0.2
0.4
0.6
10−3
10−2
10−1
100
101
rate
PDF
Comments
Before Debunking After Debunking
Exposure to debunking: comments and likes
rate. Rate –i.e.,average number of likes (left)
(resp., comments (right)) on conspiracy posts
over time of users exposed to debunking posts.
Exposure to debunking: survival functions and
attention patterns. Top panel: Kaplan-Meier
estimates of survival functions of users exposed
and not exposed to debunking. Users lifetime is
computed both on their likes (left) and
comments (right).
Bottom panel: Complementary cumulative
distribution functions (CCDFs) of the number of
likes (left) and comments (right), per each user
exposed and not exposed to debunking.
RESPONSE TO 47,780 DEBUNKING POSTS (1)
Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., ... & Quattrociocchi, W. (2017).
Debunking in a world of tribes. PloS one, 12(7), e0181821.
VIRAL PROCESSES AND THE SIZE OF ECHO-CHAMBERS
VIRAL PROCESSES AND ECHO CHAMBERS
0
200
400
600
0 500 1000 1500 2000 2500
Conspiracy Cascade Size
Lifetime
(hours)
0
200
400
0 250 500 750
Science Cascade Size
Lifetime
(hours)
0
1
2
3
4
0.1 1.0
Mean Edge Homogeneity
PDF
Science
Conspiracy
Lifetime as a function of the cascade size for
conspiracy news (left) and science news (right).
Science news quickly reach a higher diffusion,
a longer lifetime does not correspond to a
higher level of interest.
Conspiracy rumors are assimilated more slowly
and show a positive relation between lifetime
and size.
Probability density function (PDF) of edge
homogeneity for science (orange) and
conspiracy (blue) news.
Homophilic paths are dominant on the
whole cascades for both scientific and
conspiracy news.
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2016).
The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554-559.
EMOTIONAL DYNAMICS AND ECHO-CHAMBERS
Sentiment and commenting activity.
Average sentiment of polarized users
as a function of their number of
comments. Negative (respectively,
neutral, positive) sentiment is denoted
by red (respectively, yellow, blue) color.
The sentiment has been regressed
w.r.t. the logarithm of the number of
comments.
DISCUSSION AND GROUP POLARIZATION
“It is well known that when like-minded groups deliberate, they tend to polarize, in
the sense that they generally end up in a more extreme position in line with their
predeliberation tendencies” (Sunstein, 2008) Going to extremes: how like minds unite and
divide. Oxford University Press
Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., ... & Quattrociocchi, W. (2015). 

Emotional dynamics in the age of misinformation. PloS one, 10(9), e0138740.
WHEN THE ECHO CHAMBERS MEET
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y = −0.18 −0.048 log(x)
Positive
Neutral
Negative
101
102
103
post comments
−1.0−0.5 0.0 0.5 1.0
Sentiment and discussion.
Aggregated sentiment of posts as a
function of their number of
comments. Negative (respectively,
neutral, positive) sentiment is
denoted by red (respectively,
yellow, blue) color.
Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., ... & Quattrociocchi, W. (2015). 

Emotional dynamics in the age of misinformation. PloS one, 10(9), e0138740.
376 Million of Facebook Users (Jan 2010- Dec 2015)
10
−0.5
10
0
10
0.5
10
1
0.00 0.25 0.50 0.75 1.00
standardizedlifetime
monthly
rate
10
−0.5
10
0
10
0.5
10
1
0.00 0.25 0.50 0.75 1.00
standardizedlifetime
weekly
rate
10
−0.5
10
0
10
0.5
10
1
0.00 0.25 0.50 0.75 1.00
standardizedactivity
weekly
rate
10
−0.5
10
0
10
0.5
10
1
0.00 0.25 0.50 0.75 1.00
standardizedactivity
monthly
rate
10
0.6
10
0.8
10
1
10
1.2
10
1.4
10
1.6
10
1.8
0.00 0.25 0.50 0.75 1.00
standardizedactivity
unique
pages
10
0.6
10
0.8
10
1
10
1.2
10
1.4
10
1.6
10
1.8
0.00 0.25 0.50 0.75 1.00
standardizedlifetime
unique
pages
Users tend to focus on a limited set of information sources
Schmidt, A. L., Zollo, F., Del Vicario, M., Bessi, A., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2017).
Anatomy of news consumption on Facebook. Proceedings of the National Academy of Sciences, 114(12), 3035-3039.
Likes Comments
Clusters and Users Polarization
Schmidt, A. L., Zollo, F., Del Vicario, M., Bessi, A., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2017).
Anatomy of news consumption on Facebook. Proceedings of the National Academy of Sciences, 114(12), 3035-3039.
Community Structure
Backbone of the projections
on pages of the users likes
(left) and comments (right).
Polarization: Distribution of Users likes and comments on the 2 communities
Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., & Quattrociocchi, W. (2017).
Mapping social dynamics on Facebook: The Brexit debate. Social Networks, 50, 6-16.
IS POLARIZATION DOMINANT?
WHAT ABOUT VACCINES?
Schmidt, A. L., Zollo, F., Scala, A., Betsch, C., & Quattrociocchi, W. (2018).
Polarization of the vaccination debate on Facebook. Vaccine, 36(25), 3606-3612.
Understand and develop tools allowing for the early
detection of potential triggers
- Psychometry of the Echo-Chambers
- Polarizing Topics
- Inaccurate information
- Users dynamics (selective exposure and Conf
bias)
- Detection of the core narrative (postulates) of
any echo chamber
- Fake rank algorithm by combining users and
information sources dynamics
- Quantify the impact of specific misinformation
cascades
Vicario, M. D., Quattrociocchi, W., Scala, A., & Zollo, F. (2019). Polarization and fake news: Early warning of potential misinformation
targets. ACM Transactions on the Web (TWEB), 13(2), 1-22.
POLARIZATION ON DIFFERENT PLATFORMS
NEWS AND POLARIZATION
Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo chambers on social media: A comparative
analysis. arXiv preprint arXiv:2004.09603. under revision to Science Advances
RESEARCH IMPACT
COLLABORATIONS
Ministero Innovazione: Data Task Force Head of the group on
social and economic impact



Agcom: Data Task Force on Information 



Facebook: Data For Good Program
Human Mobility in Response to
COVID-19 in Italy, France and
UK
Misinformation. Guida alla societa' dell'informazione e della credulità
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Misinformation. Guida alla societa' dell'informazione e della credulità

  • 1. Data-driven modeling of Complex Systems Walter Quattrociocchi walterquattrociocchi@gmail.com
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. OBSERVING SOCIAL PHENOMENA The Twitter of Babel: Mapping World Languages through Microblogging Platforms (Mocanu et al PlosOne 2013) Sentiment of emojis (P. Novak et al. PLoS One 2015 ) The dynamics of protest recruitment through an online network. (S. González-Bailón et al” Sci rep 1 (2011).) Structural Patterns of the Occupy Movement on Facebook (Del Vicario et al. Complex Networks and their application 2015)
  • 7. Agenda Setting is the process of the mass media presenting certain issues frequently and prominently with the result that large segments of the public perceive those issues as more important than others. MORE COVERAGE THE ROLE OF THE MEDIA MORE IMPORTANT
  • 8. OLD MEDIA - Follow the “Ritual of Objectivity” - Publication patterns are driven by most followed sources (imitation) (Marlow 2005) NEW MEDIA - Information production is the work of interconnected actors spanning over organizations, professional identity and geographical location A SHIFT OF PARADIGM MEDIATED DISINTERMEDIATED
  • 9. “We're not thinking about ourselves as a community — we're not trying to build a community — we're not trying to make new connections. [...] What we're trying to do is just make it really efficient for people to communicate, get information and share information. We always try to emphasize the utility component.” Mark Zuckerberg Jul. 2007
  • 10. WHAT ABOUT THE QUALITY OF INFORMATION?
  • 11. METHODOLOGY Questions framing (Sociology, Comm., Psych., Semiotics, Anthropology ) Data Collection and Transformation (Surveys,Algorithms, Databases) Quantitative Analysis (Statistical mechanics, Net Sci, Machine Learning) Modeling and Validation (Statistical mechanics, Multi agent systems) SETTING UP EXPERIMENTS WITH BIG DATA FOCUS (MIS)INFORMATION SPREADING ONLINE (Social Contagion, Collective Framing of Narratives, Content Consumption, Opinion Dynamics)
  • 12. CONFIRMATION BIAS AND INFORMATION CONSUMPTION The cognitive attitude to search for, interpret, favor, and recall information in a way that confirms one's beliefs
  • 13. THE DATASET(s) FB ITALY TOTAL SCIENCE CONSPIRACY TROLL Pages 73 34 39 2 Posts 271,296 62,705 208,591 4,709 Likes 9,164,781 2,505,399 6,659,382 40,341 Comments 1,017,509 180,918 836,591 58,686 Likers 1,196,404 332,357 864,047 15,209 Commentsers 279,972 53,438 226,534 43,102 FB USA TOTAL SCIENCE CONSPIRACY DEBUNKING Pages 478 83 330 66 Posts 679,948 262,815 369,420 47,780 Likes 603,332,826 453,966,494 145,388,117 3,986,922 Comments 30,828,705 22,093,692 8,304,644 429,204 Likers 52,172,855 39,854,663 19,386,131 702,122 Commentsers 9,790,906 7,223,473 3,166,726 118,996 Facebook ITALY and USA from Jan 2010 to Dec 2014
  • 14. Bessi, A., Petroni, F., Del Vicario, M., Zollo, F., Anagnostopoulos, A., Scala, A., ... & Quattrociocchi, W. (2015, May). Viral misinformation: The role of homophily and polarization. In Proceedings of the 24th International Conference on World Wide Web (pp. 355-356). ACM. webSci@WWW (Bessi et al. 2015) Bessi, A., Petroni, F., Del Vicario, M., Zollo, F., Anagnostopoulos, A., Scala, A., ... & Quattrociocchi, W. (2016). Homophily and polarization in the age of misinformation. The European Physical Journal Special Topics, 225(10), 2047-2059. CONTENT CONSUMPTIONS AND FRIENDS Polarization on contents. Probability density function (PDF) of users’ polarization. Notice the strong bimodality of the distribution, with two sharp peaks localized at 0 <︎ ρ <︎ 0.005 (science users) and at 0.95 ︎< ρ < ︎ 1 (conspiracy users). Homophily. Fraction of polarized friends with the same polarization respect to the number of likes log(θ(u)) of user u.
  • 15. RESPONSE TO 4,709 INTENTIONAL FALSE CLAIMS (TROLLS) Polarized users on false information. Percentage of likes and comments on intentional false information posted by a satirical page from polarized users of the two categories. Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015).
 Science vs conspiracy: Collective narratives in the age of misinformation. PloS one, 10(2), e0118093. Mocanu, D., Rossi, L., Zhang, Q., Karsai, M., & Quattrociocchi, W. (2015). Collective attention in the age of (mis) information. Computers in Human Behavior, 51, 1198-1204.
  • 16. 8,899% 91,101% Science Conspiracy RESPONSE TO 47,780 DEBUNKING POSTS (1) 4,953% 95,047% Science Conspiracy LIKES COMMENTS Debunking information are ignored by users in the conspiracy echo-chamber (out of 9,790,906 polarized conspiracy users only 5, 831 interact ) Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., ... & Quattrociocchi, W. (2017). Debunking in a world of tribes. PloS one, 12(7), e0181821.
  • 17. 0.0 0.2 0.4 0.6 10−3 10−2 10−1 100 101 rate PDF Likes 0.0 0.2 0.4 0.6 10−3 10−2 10−1 100 101 rate PDF Comments Before Debunking After Debunking Exposure to debunking: comments and likes rate. Rate –i.e.,average number of likes (left) (resp., comments (right)) on conspiracy posts over time of users exposed to debunking posts. Exposure to debunking: survival functions and attention patterns. Top panel: Kaplan-Meier estimates of survival functions of users exposed and not exposed to debunking. Users lifetime is computed both on their likes (left) and comments (right). Bottom panel: Complementary cumulative distribution functions (CCDFs) of the number of likes (left) and comments (right), per each user exposed and not exposed to debunking. RESPONSE TO 47,780 DEBUNKING POSTS (1) Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., ... & Quattrociocchi, W. (2017). Debunking in a world of tribes. PloS one, 12(7), e0181821.
  • 18. VIRAL PROCESSES AND THE SIZE OF ECHO-CHAMBERS
  • 19. VIRAL PROCESSES AND ECHO CHAMBERS 0 200 400 600 0 500 1000 1500 2000 2500 Conspiracy Cascade Size Lifetime (hours) 0 200 400 0 250 500 750 Science Cascade Size Lifetime (hours) 0 1 2 3 4 0.1 1.0 Mean Edge Homogeneity PDF Science Conspiracy Lifetime as a function of the cascade size for conspiracy news (left) and science news (right). Science news quickly reach a higher diffusion, a longer lifetime does not correspond to a higher level of interest. Conspiracy rumors are assimilated more slowly and show a positive relation between lifetime and size. Probability density function (PDF) of edge homogeneity for science (orange) and conspiracy (blue) news. Homophilic paths are dominant on the whole cascades for both scientific and conspiracy news. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554-559.
  • 20. EMOTIONAL DYNAMICS AND ECHO-CHAMBERS Sentiment and commenting activity. Average sentiment of polarized users as a function of their number of comments. Negative (respectively, neutral, positive) sentiment is denoted by red (respectively, yellow, blue) color. The sentiment has been regressed w.r.t. the logarithm of the number of comments. DISCUSSION AND GROUP POLARIZATION “It is well known that when like-minded groups deliberate, they tend to polarize, in the sense that they generally end up in a more extreme position in line with their predeliberation tendencies” (Sunstein, 2008) Going to extremes: how like minds unite and divide. Oxford University Press Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., ... & Quattrociocchi, W. (2015). 
 Emotional dynamics in the age of misinformation. PloS one, 10(9), e0138740.
  • 21. WHEN THE ECHO CHAMBERS MEET ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● y = −0.18 −0.048 log(x) Positive Neutral Negative 101 102 103 post comments −1.0−0.5 0.0 0.5 1.0 Sentiment and discussion. Aggregated sentiment of posts as a function of their number of comments. Negative (respectively, neutral, positive) sentiment is denoted by red (respectively, yellow, blue) color. Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., ... & Quattrociocchi, W. (2015). 
 Emotional dynamics in the age of misinformation. PloS one, 10(9), e0138740.
  • 22. 376 Million of Facebook Users (Jan 2010- Dec 2015)
  • 23. 10 −0.5 10 0 10 0.5 10 1 0.00 0.25 0.50 0.75 1.00 standardizedlifetime monthly rate 10 −0.5 10 0 10 0.5 10 1 0.00 0.25 0.50 0.75 1.00 standardizedlifetime weekly rate 10 −0.5 10 0 10 0.5 10 1 0.00 0.25 0.50 0.75 1.00 standardizedactivity weekly rate 10 −0.5 10 0 10 0.5 10 1 0.00 0.25 0.50 0.75 1.00 standardizedactivity monthly rate 10 0.6 10 0.8 10 1 10 1.2 10 1.4 10 1.6 10 1.8 0.00 0.25 0.50 0.75 1.00 standardizedactivity unique pages 10 0.6 10 0.8 10 1 10 1.2 10 1.4 10 1.6 10 1.8 0.00 0.25 0.50 0.75 1.00 standardizedlifetime unique pages Users tend to focus on a limited set of information sources Schmidt, A. L., Zollo, F., Del Vicario, M., Bessi, A., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2017). Anatomy of news consumption on Facebook. Proceedings of the National Academy of Sciences, 114(12), 3035-3039.
  • 24. Likes Comments Clusters and Users Polarization Schmidt, A. L., Zollo, F., Del Vicario, M., Bessi, A., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2017). Anatomy of news consumption on Facebook. Proceedings of the National Academy of Sciences, 114(12), 3035-3039.
  • 25. Community Structure Backbone of the projections on pages of the users likes (left) and comments (right). Polarization: Distribution of Users likes and comments on the 2 communities Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., & Quattrociocchi, W. (2017). Mapping social dynamics on Facebook: The Brexit debate. Social Networks, 50, 6-16. IS POLARIZATION DOMINANT?
  • 27. Schmidt, A. L., Zollo, F., Scala, A., Betsch, C., & Quattrociocchi, W. (2018). Polarization of the vaccination debate on Facebook. Vaccine, 36(25), 3606-3612.
  • 28. Understand and develop tools allowing for the early detection of potential triggers - Psychometry of the Echo-Chambers - Polarizing Topics - Inaccurate information - Users dynamics (selective exposure and Conf bias) - Detection of the core narrative (postulates) of any echo chamber - Fake rank algorithm by combining users and information sources dynamics - Quantify the impact of specific misinformation cascades Vicario, M. D., Quattrociocchi, W., Scala, A., & Zollo, F. (2019). Polarization and fake news: Early warning of potential misinformation targets. ACM Transactions on the Web (TWEB), 13(2), 1-22.
  • 29. POLARIZATION ON DIFFERENT PLATFORMS NEWS AND POLARIZATION Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo chambers on social media: A comparative analysis. arXiv preprint arXiv:2004.09603. under revision to Science Advances
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  • 33. COLLABORATIONS Ministero Innovazione: Data Task Force Head of the group on social and economic impact 
 Agcom: Data Task Force on Information 
 Facebook: Data For Good Program
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  • 37. Human Mobility in Response to COVID-19 in Italy, France and UK