Research Seminar at Queen Mary University of London (CogSci) 2nd December 2020. In this talk, we present and discuss various research and development projects focused on addressing some of the societal challenges of today’s world (misinformation spreading, extremism, child grooming) by means of social data science. These problems are complex, dynamic and heterogeneous, and cannot be looked at from a single lens. We will discuss how these problems are addressed from a multidisciplinary angle, combining theories, models and methods from social science, computer science, or psychology; bringing a deeper understanding of the problems, and their relations to users and their behaviours, to the proposed solutions.

1. On the Application of Social Data
Science to Address Societal
Challenges
Presenting: Miriam Fernandez
@miriam_fs
fernandezmiriam
@miriamfs
1

2. Who we are?
2

3. “All human beings
are born free and equal in dignity and
rights. They are endowed
with reason and conscience and
should act towards one another in
a spirit of brotherhood”
Universal Declaration of Human Rights. Paris 10th Dec 1948
3

4. Discrimination
Extremism and Violence
Inequality
Polarisation
MisinformationClimate Crisis
Societal Challenges
Hate

5. Technology: Perpetuating and
Exacerbating the problem
5

6. Social
Data
Science
Social Data Science
6

7. 7
Policing

8. 8

9. 9

10. 10
Child Grooming
• Premeditated
behaviour intending
to secure the trust of
a minor as a first step
towards future
engagement in
sexual conduct.

11. Claire Lilley, Ruth Ball, Heather Vernon,
The experiences of 11-16 year olds on
social networking sites, NSPCC 2014
“findings show
that approximately
190,000 UK children
(1 in 58) will suffer contact
sexual abuse by a non-related
adult before turning 18, with
approximately 10,000 new child
victims of contact sexual abuse
being reported in the UK each
year.”
Child Grooming
11

12. Child Grooming
Olson’s psychological theory of luring communication (LCT)
Grooming data
• Classification results:
– Trust development: 79% P, 82% R, 81% F1
– Grooming stage: 88% P, 89% R, 88% F1
– Physical approach: 87% P, 89% R, 88% F1
Cano et al. Detecting child grooming behaviour patterns on social media. SocInfo 2014
12

13. 13
Online Radicalisation

14. Social Media Propaganda and
Recruiting
Islamic State in Iraq and Syria (ISIS)
14

15. 15

16. Radical Terminology: Usage
Divergence
112 pro-ISIS
Twitter
accounts
112 “general”
Twitter accounts
similar
terminology
Radical Terminology
Fernandez., et al. Contextual Semantics For Radicalisation Detection on Twitter. SW4SG Workshop. International
16

17. Semantic Graph-based Approach for
Pro-ISIS Stance Detection
Tweets
Conceptual.
Semantics.
Extraction
DBpedia
Semantic.Graph.
Representation
Frequent.Semantic.
Subgraph.Mining
Classifier.Training
ISIS
Syria
Jihadist Group
Country (Military Intervention Against ISIL, place, Syria)
Entities Concepts Semantic Relations
86.3 86.3
84.8
86
91.7
84.4 84.4
81
87.1
92.8
80
82
84
86
88
90
92
94
Unigrams Sentiment Topics Network Semantics
anti-ISIS pro-ISIS
Saif, H., et al. A semantic graph-based approach for radicalisation detection on social media. European semantic web conference. Springer, 2017
17

18. Micro or
Individual roots
Macro or
Global roots
Meso or
Group roots
Radicalisation
Influence
Content from
friends
Authored
posts
Content from news
and other websites
Radicalisation Influence
• Individual: similarity of own content to
radicalisation terminology
• Social: similarity of retweeted content from
followees to radicalization terminology
• Global: similarity of content shared from news
and websites to radicalization terminology
Fernandez, M.; Asif M; Alani, H. Understanding the roots of radicalisation on Twitter. WebScience 2018 Best paper award!
Cooperation
Social Science & Computer Science
18
Radicalisation Influence

19. Radicalisation Influence
Radicalisation Lexicon
w1
w2
w3
19

20. Social Influence
Fernandez, M.; Gonzalez-Pardo, A; Alani, H. Radicalisation Influence in Social Media. Journal of Web Science, 2019.
• The network is the essence of social media
platforms.
• Harm propagates across the network, and
influences recipients over time.
• Need to protect and alert users to harmful
influences and influencers.
• Monitor and regulate the use of
networking recommendation algorithms.
Individual influence: similarity of own content to radicalisation terminology
Socialinfluence:similarityofretweeted
contenttoradicalisationterminology
Tools to measure radicalisation influence and behaviour
20

21. Challenges
Fernadez et al. Artificial Intelligence and Online Extremism: Challenges and Opportunities. Taylor and Francis. 2020.
Be very aware of
the challenges!
21

22. 22
Extreme Misogyny

23. Justification of sexual violence "If a woman
gets impregnated by a rapist then the child will
have a high probability of reproductive success.
This is why it is so common for women to have
rape fantasies, and why a woman's fertility
skyrockets when she is being raped."
Attribution of blame “Primary Education is Essentially Hiring
Overpaid Whores to Babysit and Brainwash your Children”
Objectification of women "Every female is born
with the key to our salvation between her legs...
When they get imprisoned, their precious
resources waste away while we incels starve here.
The solution is for the government to employ
incels to rape female criminals."
23

24. 24Farrell et all. Exploring Misogyny across the Manosphere in Reddit. WebScience 2019
Farrell et all. On the use of Jargon and Word Embeddings to Explore Identity within the Reddit's Manosphere. WebScience 2020
● Grounded on exploring feminist models at scale
● Utilising a dataset of 6M posts, 300K conversations on
Reddit
● Observing that data over a longer period of time (2011-
2018)
● Lexicon of Hate 1,300 annotated terms from 9 compiled
lexicons under 9 categories from feminist studies
● Using word embeddings to understand identify and culture
(jargon and neologisms).
Analysing the Manosphere
Groups
r/MGTOW
r/Braincels
r/BadWomensAnatomy
r/IncelsInAction
r/IncelTears
r/Trufemcels
r/IncelsWithoutHate

25. 25
Misinformation

26. 26

27. Types of Mis- and Dis- Information
7 Types of Mis- and Dis-information (Credit: Claire Wardle, First Draft)
27

28. Dimensions of combating online
misinformation
• Misinformation content detection
– Are misinformation content and sources
automatically identified? Are streams of information
automatically monitored? Is relevant corrective
information identified as well?
• Misinformation dynamics
– Are patterns of misinformation flow identified and
predicted? Is demographic and behavioural
information considered to understand and predict
misinformation dynamics?
• Content Validation
– Is misinformation validated and fact checked? Are
the users involved in the content validation process?
• Misinformation management
– Are citizens’ perceptions and behaviour with regards
to processing and sharing misinformation studied
and monitored? Are intervention strategies put in
place to handle the effects of misinformation?
Simply presenting people with
corrective information is likely to fail in
changing their salient beliefs and
opinions, or may, even, reinforce them
Fernandez et al. Online Misinformation Challenges and Future Directions. The 2018 Web Conference Companion.
28

29. Misinfo.me: Who is interacting with
misinformation?
Mensio et al. MisinfoMe: Who’s Interacting with Misinformation? In: 18th International Semantic Web Conference (ISWC 2019): Posters & Demos
29

30. 30
Education

31. What is OUAnalyse?
We are hereHistory Future we can affect
StartOpening
VLE
A1 A3A2 End
VLE1 VLE2 VLE3VLE0 VLEn
Identify students at risk of failing the module as early as possible so that OU
intervention is efficient and meaningful.
https://analyse.kmi.open.ac.uk/AnalyseDashboard
31

32. Social Data Science to Address Societal
Challenges
• Understand the problems and their multiple dimensions
– Look at the problem from multiple lenses
• Computer Science
• Social Science
• Psychology
• Policy
• Law
• Understand the diversity of users & their behaviours
– Bring them closer to the proposed solutions (co-create!)
– User-Centric metrics
• Capture the context!
– Understand the semantics behind user needs and content meanings
• Ethics
32

33. 33
On the Application of User Centric Data Science to
Address Societal Challenges
Miriam Fernandez
Knowledge Media Institute
Open University, UK
@miriam_fs
@miriamfs
Credit to all these fantastic people!