Haris Papageorgiou talks about Data Analytics in Social Sciences Workshop title:TDM unlocking a goldmine of information Training overview: Text and Data Mining (TDM) is a natural ‘next step’ in open science. It can lead to new and unexpected discoveries and increase the impact of publications and repositories. This workshop showcases examples of successful TDM and infrastructural solutions for researchers. We will also discuss what is needed to make most of infrastructures and how publishers and repositories can open up their content. DAY 2 - PARALLEL SESSION 4 & 5

1. Data Analytics meets
Social Sciences:
New Frontiers of
collaboration
Haris Papageorgiou
ATHENA RC

2. Computational Social Science
 Computational social science means that computers are used to model,
simulate, and analyze social phenomena. It refers to the academic sub-disciplines
concerned with computational approaches to the social sciences.
 Computational social science, revolutionizes both fundamental legs of the
scientific method: empirical research, especially through big data, by analyzing
the digital footprint left behind through social online activities; and scientific
theory, especially through computer simulation model building through social
simulation.
Wikipedia

3. Text Analytics
 The term text analytics describes a set of linguistic
statistical, and machine learning techniques that model and
structure the information content of textual sources. (Wikipedia)
 In other words, enhancing the value of text content by
extracting entities, features, context, relationships and emotion.

4. Goals
 Predict the outcome of elections, referendum (BREXIT) etc.
 Examine xenophobia during the economic and migration crisis
 Correlate the political points of view and perspectives with the financial crisis
 Predict conflict regions
 Describe & Analyze the evolution and dynamics of social movements
 Causal relationships between disasters and job offers
 Correlate radicalization with verbal aggressiveness on religious-related events
 Explain collective behaviour
 Test and Validate hypotheses etc. ………….

6. The Big three Ingredients

7. Methodology
Data Collection, Filtering
Data Exploration - Visualization
Data Analytics
Data Modeling, Stats, Prediction
Validation – Evaluation - Viz
Interpretation

8. PALOMAR
PALOMAR Architecture

9. Data
User Requests
Analytics operation workflow
Processing Layer
Kafka stream
data transport…
Crawler Server 1
OSN Data Server 2
Data Server n
aggregator
2
2
2
5
3
3
3
4
Data
6
ElasticSearch Indexers
Data
Data
Data Collection
Tier
Mongo DB
sync
Text
process
or
NLP/Text pipelines
Tok Sent
Sentiment
Lemmatiser
Entity
Linking
Parser Tagger NERC
Summari
ser
Tokeniser
Lexica
Storage HDFS
Job scheduler
Text Analytics Stack services3rd Party
Distributed arch
Big Data arch
Data Access
Tier
…
Web Server 1
Web Server 2
Web Server n

12. SoDaMap1,
Xenophobia2
Data Collection, Filtering
Data Exploration - Visualization
Data Analytics
Data Modeling, Stats, Prediction
Validation – Evaluation - Viz
Interpretation
1. The Sodamap Project: a longitudinal study on social movements, in collaboration with sociologist Theoni Stathopoulou at National Center for Social Research-NCSR.
2. The Xenophobia Project: examinining xenophobia during the economic and migration crisis in Greece, in collaboration with Professor of Political Science, Vassiliki Georgiadou, at the
Department of Political Science and History of the Panteion University.

13. Data Collection: Overview
Gathering & Web
crawling
Cleansing &
Preparation Preprocessing
Management &
Storage
Data
Sources
News articles
Comments of
the public
Social Media
(Twitter)
Opinion
Columns &
Blogs
Parliamentary
debates
Videos with politicians
interviews and
speeches
Political Party
manifestos
retrieval
Explorative
Analysis
Data Analytics

14. Data Collection
• Νewspapers, ≈ 2.541.000 articles, time
period 1996-2014
• ≈ 564.000.000 tweets, time period
2012-2016

17. Data Exploration
QUERIES
SEARCH
ENGINE
Data
Collections

18. Data Analytics
• Topics / Themes
• Event Extraction
Methodology
• Verbal Aggressiveness
/Opinion Mining

19. SCOPE
• Code specific event types
• Build computational tools for extracting event instances
• Record results to a database
• Visualize results
• Analyze outcome
• Correlate with impact KPIs

20. Claim vs Event
 A claim is defined as a purposive unit of strategic or communicative
action in the public sphere aiming at a collective stake and having the form of
physical or verbal action.
 In NLP, an event denotes a specific instance of an event type, e.g. a killing
incident happening at a specific location, time and involving certain participants.

21. Claim
 A valid claim comprises the following components:
 The Form (How), namely the form of action through which the claim is introduced in the public
sphere, or the realization of a protest event is located in a text.
 The Actor (Who), or the claimant, meaning the entity that is making the claim.
 The Addressee (To Whom), namely the entity to whom the action is addressed.
 The Issue (What) of a Claim, i.e. what the protest is about, the subject matter of a Claim.
 The Location (Where), viz where the event took place, the location of a claim in space.
 The Time (When) at which the event happened, or location of a claim in time.

22. Event Extraction Workflow
1
2
3
Codebook design
Tools design and
development
Event Database
population
Events
Data
Explorati
on
Codebo
ok
Data
Analytics
Results
-
Evaluati
on

23. Several Event Categories
1
Physical Violence
Events
2
Protest Events
3
Quotations -
Statements

24. EVENT TAXONOMY
Physical Attack
• Assault
• Assault against life
• Violent Assault
• Sexual Assault
• Verbal Attack
• Attack against Property
• Attack against Personal
Property
• Attack against Religious
Property
Protest
• Protest
• Demonstration/March
• Motorized March
• Sit Down
• Strike
• Hunger Strike
• Blockade
• Occupation
• Signature Collection
• Boycott
• Symbolic Violence
• Revolt
• Violent Demonstration

25. VARIOUS TYPES OF INFORMATION
• Medium
• Title
• Date (Day, Month, Year)
• Location (Category)
• Event (Event type)
• Confidence (Degree)
• Issue

26. ACTOR & TARGET ATTRIBUTES
• Target Group
• Nationality
• Category
• Age
• Sex
• Status

27. Protest Events Codebook
CLAIM=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
The Law Society of Piraeus has voted to
occupy the Mortgage Registries of Piraeus
and Salamis, on April 26th and 27th 2006, in
protest against the serious operational
problems it is facing.

28. Protest Events Codebook
CLAIM=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
The Law Society of Piraeus has voted to
occupy the Mortgage Registries of Piraeus
and Salamis, on April 26th and 27th 2006, in
protest against the serious operational
problems it is facing.

29. Protest Events Codebook
CLAIM=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
The Law Society of Piraeus has voted to
occupy the Mortgage Registries of Piraeus
and Salamis, on April 26th and 27th 2006, in
protest against the serious operational
problems it is facing.

30. Protest Events Codebook
CLAIM=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
The Law Society of Piraeus has voted to
occupy the Mortgage Registries of Piraeus
and Salamis, on April 26th and 27th 2006, in
protest against the serious operational
problems it is facing.

31. Protest Events Codebook
CLAIM=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
The Law Society of Piraeus has voted to
occupy the Mortgage Registries of Piraeus
and Salamis, on April 26th and 27th 2006, in
protest against the serious operational
problems it is facing.

32. Protest Events Codebook
CLAIM=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
The Law Society of Piraeus has voted to
occupy the Mortgage Registries of Piraeus
and Salamis, on April 26th and 27th 2006, in
protest against the serious operational
problems it is facing.

33. Event Extraction
Form
Actor/
Addressee
Issue
Time/
Location
Events
Named
Entity
Recognition
Chunking
Dependency
Parsing
Co-reference
Resolution
Aggregation
Analytics
Stack
ILSP-NLP
IE workflow
Summarize/
Export

34. Form – Event types
• Labor related protests
• Strike
• Abstention from duties
• ...
• Demonstrative protests
• March/Demonstration
• Sit-down
• ...
• Confrontational protests
• Hunger strike
• Boycott
• ...
• Violent protests
• Arson, bomb attacks
• Symbolic violence
• ...

35. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
Recorded coverage of the statement made
in the House of Commons by home
secretary Theresa May on the 1989
Hillsborough disaster, from Wednesday 27
April.

36. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
Recorded coverage of the statement made
in the House of Commons by home
secretary Theresa May on the 1989
Hillsborough disaster, from Wednesday 27
April.

37. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
Recorded coverage of the statement made
in the House of Commons by home
secretary Theresa May on the 1989
Hillsborough disaster, from Wednesday 27
April.

38. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
Recorded coverage of the statement made
in the House of Commons by home
secretary Theresa May on the 1989
Hillsborough disaster, from Wednesday 27
April.

39. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Loc, Time>
Recorded coverage of the statement made
in the House of Commons by home
secretary Theresa May on the 1989
Hillsborough disaster, from Wednesday 27
April.

40. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Time, Loc>
"Of course a rise in tension and terrorist
activity cannot but heighten concern. It is
further proof of how fragile the situation is
in Syria and demonstrates the necessity to
continue active steps towards resuming
talks," Kremlin spokesman Dmitry Peskov
told reporters yesterday in Moscow.

41. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Time, Loc>
"Of course a rise in tension and terrorist
activity cannot but heighten concern. It is
further proof of how fragile the situation is
in Syria and demonstrates the necessity to
continue active steps towards resuming
talks," Kremlin spokesman Dmitry Peskov
told reporters yesterday in Moscow.

42. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Time, Loc>
"Of course a rise in tension and terrorist
activity cannot but heighten concern. It is
further proof of how fragile the situation is
in Syria and demonstrates the necessity to
continue active steps towards resuming
talks," Kremlin spokesman Dmitry Peskov
told reporters yesterday in Moscow.

43. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Time, Loc>
"Of course a rise in tension and terrorist
activity cannot but heighten concern. It is
further proof of how fragile the situation is
in Syria and demonstrates the necessity to
continue active steps towards resuming
talks," Kremlin spokesman Dmitry Peskov
told reporters yesterday in Moscow.

44. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Time, Loc>
"Of course a rise in tension and terrorist
activity cannot but heighten concern. It is
further proof of how fragile the situation is
in Syria and demonstrates the necessity to
continue active steps towards resuming
talks," Kremlin spokesman Dmitry Peskov
told reporters yesterday in Moscow.

45. Coding Quotations
QUOTATION=EVENT
<Actor, Form, Addressee, Issue, Time, Loc>
"Of course a rise in tension and terrorist
activity cannot but heighten concern. It is
further proof of how fragile the situation is
in Syria and demonstrates the necessity to
continue active steps towards resuming
talks," Kremlin spokesman Dmitry Peskov
told reporters yesterday in Moscow.

46. Outline
Introduction
PALOMAR Architecture
Data Exploration - Visualization
Data Analytics, Prediction, Stats
Validation – Evaluation - Viz
Infrastructure

47. 0
100
200
300
400
500
600
700
800
900
1000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Event Types Timeline (Pr)
Protest Demonstration/March Motorized March Sit Down Strike
Hunger Strike Blockade Occupation Signature Collection Boycott
Symbolic Violence Revolt Violent Demonstration
Visualizations

48. Event Analysis
The post-election lows seem to be related to what political scientists call the electoral cycle
(Miller and Mackie, 1973), referring to the fact that a newly elected government usually enjoys
a brief honeymoon period characterised by increased popular support due to its fresh
mandate.

49. Verbal Aggressiveness rate per Target Group
0
0.01
0.02
0.03
0.04
0.05
0.06
TG1: Pakistani TG2: Albanians TG3: Romanians TG5: Islam/Muslims TG4: Syrians TG6: Jews TG7: Germans TG8: Roma TG9: Immigrants TG0: Refugees
2013 2014 2015 2016

50. Analysis: Examining Xenophobia in Greece
Verbal Aggressiveness
as an Indicator of
Xenophobic Attitudes in
Twitter
The economic crisis encourages the development of defensive
nationalism and the perception of vulnerability.
Not significant increase in overall VAMs. In terms of
formulation, continuity for certain TGs (Albanians) and
resurgence of new TGs (Germans).
Anti-Muslim attitudes on the rise. (Could Islamophobia
evolve into an important component of Greek xenophobia?).
The strength of xenophobic beliefs seems to be rooted in Greek
political culture and is not triggered by the economic crisis.
The effect of the refugee crisis on public beliefs is an open question.

51. Issues of concern

53. Data is just as
biased as the
People who
collected it

54. Data Bias

55. Media Bias

56. Media Bias

57. ..our approach

59. Interpretability

62. Haris Papageorgiou
ILSP/Athena R.C.
@xpapag xaris@ilsp.gr