The document discusses the mining of arguments from online debating systems, focusing on a workshop presentation that outlines argumentation frameworks, techniques for mining argumentation graphs, and an application to a Reddit thread. It highlights the challenges posed by large data sets and the importance of automated techniques in argument mining, including sentiment and opinion analysis. The presentation concludes with insights on enhancing the quality of argument graphs through further research and techniques in opinion mining and deep learning.

1. MINING ARGUMENTS
FROM ONLINE DEBATING SYSTEMS
6TH MLDM.IT WORKSHOP @ AI*IA 2017
Andrea Pazienza, Stefano Ferilli
14th November 2017 – Bari, Italy

2. Overview
1. Introduction to Argumentation
2. Mining Argumentation Graphs from Online Debating
Systems
3. Application to a Reddit Thread
4. Conclusions and future works

3. INTRODUCTION TO ARGUMENTATION

4. Introduction to Argumentation
Argument Mining
Analyzing argumentation
structures in the discourse
Abstract
Argumentation
A framework for practical
and uncertain reasoning
able to cope with partial and
inconsistent knowledge

5. Argument Mining
Where are we?
Computational linguistics: statistical or rule-based modeling of natural
language
NLP: interactions between computers and human languages
Data mining: automatic extraction of data (often numerical)
Text mining: automatic extraction of data from natural language texts
Argument mining: automatic extraction of arguments from natural language
texts
Why to do it?
Big Data problem: the ever increasing amounts of data on the web mean that
manual analysis of this content seems to become increasingly infeasible.
How to process Big Data?
By mining information

6. Argument Mining
Existing approaches in NLP primarily focus on micro-level
(monological) rather than macro-level (dialogical) perspective
What can we mine?
Sentiment Anaysis: mining attitudes towards something (positive, neutral,
negative)
Opinion Mining: mining opinions about something
Graph-based dialogical model

7. (Macro-level) Argument Mining
Argument Mining
Segmenting texts into argumentative units
Identifying relations between units
Analyzing Polarity and classifying Stance
◦ Sentiment Analysis: e.g. number of
people liking new Mercedes vs. number
of people not liking new Mercedes
◦ Opinion Mining: e.g. people thinking
new Mercedes is too expensive, people
thinking new Mercedes is reliable
Mining arguments pro- and con- people’s
opinions
◦ e.g. not only the information that people
like Mercedes but also why: people like
new Mercedes, because they think it is
reliable

8. Abstract Argumentation
Argumentation Framework (AF)
encapsulates arguments as nodes in a digraph
connects them through a relationship of attack
defines a calculus of opposition for determining
what is acceptable
allows a range of different semantics
a
b
e
c
d
f
g
h
Generalizations of Argumentation Frameworks
Bipolar: add support relation
Weighted: add weights on attacks
Values, Preferences
etc.
Extension-based vs Ranking-based Semantics
extension-based semantics do not fully exploit the weight of relations
rank arguments from the most to the least acceptable ones

9. MINING ARGUMENTATION GRAPHS
FROM ONLINE DEBATING SYSTEMS

10. Online Debating Systems (ODS)
Classical Thread Discussion
A tree (i.e., hierarchical)
structure consisting of
Root node: the discussion
topic, i.e. the major claim,
shared by a user, followed by
Comments from other users
Each of these comments has
as children the comments in
response to it.
Step 1: Identification of arguments
Each content is an abstract argument

11. Problem Approach
Step 2: Identification of relations between arguments
Sentiment and Tone Analysis
◦ systematically identify, extract and quantify Polarity
◦ 5 Polarity classes: very negative, negative, neutral, positive, very positive
◦ StanfordCoreNLP provides the sentiment tool
Text Similarity
◦ check if two arguments are addressing the same topic
◦ Semantic Similarity via word embeddings
◦ GloVe algorithm for obtaining vector representations for words
Given α, β ∈ Atwo abstract arguments, representing two
consecutive comments of the online debate:
w( α, β ) similarity( α, β ) · sentiment(β) (1)
similarity: A× A → [0, 1]
sentiment: A → [−1, 1]

12. Bipolar Weighted Argumentation Framework
Bipolar Weighted Argumentation Framework (BWAF)
attack relations with a negative
weight in the interval [−1, 0[
support relations with a positive
weight in the interval ]0, 1]
a
b
0.7
e
-0.7
c0.9
d
-0.4
0.3
f
-0.5 g
-0.3
h
-0.5
-0.1
-0.7
BWAF Ranking-based Semantics by means of Strength Propagation

13. APPLICATION TO A REDDIT THREAD

14. Application to a Reddit Thread
We consider a Reddit discussion of an episode of Black Mirror, a
popular TV series
-0.49
-0.48
0.16
0.32
-0.1
-0.17
-0.1
-0.42
0.16
-0.47
0.16
-0.13
0.5
0.06
0.05
-0.5 0.5
0.45
-0.15
0.08
0.08
-0.5
-0.44
-0.5
-0.5
-0.5
-0.1
-0.08
-0.5
-0.48
-0.46
-0.13
-0.12
-0.12
-0.5
-0.16
-0.32
-0.33
-0.16
0.32
-0.16
0.17
-0.4
-0.5
-0.17
-0.25
0.25
0.24
0.22
-0.25
-0.16
-0.34
-0.48
-0.24
-0.16
-0.45
-0.16
-0.14
-0.48
0.43
-0.45
-0.46
-0.44
-0.47
-0.48
-0.48
-0.48
-0.19
-0.45
a17
a22
a80
a8
a75
a10
a24
a64
a26
a48
a47
a4
a29
a50
a68
a73
a65
a84
a74
a54
a78
a58
a66
a49
a41
a51
a55
a5
a40
a56
a62
a57
a61
a69
a60
a63
a59
a9
a71
a53
a45
a72
a27
a0
a85
a39
a38
a34a37
a36
a35
a28
a33
a43
a31
a42
a32
a13a44
a11
a16
a15
a14
a12
a18
a20
a76
a19
a21
a23
Sentiment Polarity + Text
Similarity to build the BWAF
Arguments acceptability via
sp-ranking Semantics
Construction procedure may
embed some noise but is simple
and computationally fast, so that
the argumentation model
instantiation will be still quite
reliable.

15. Application to a Reddit Thread
arg sp arg sp arg sp arg sp arg sp arg sp
a4 1.45 a80 1.0 a59 1.0 a38 1.0 a13 1.0 a32 0.8176
a43 1.43 a78 1.0 a57 1.0 a36 1.0 a8 1.0 a34 0.76
a22 1.32 a76 1.0 a55 1.0 a35 1.0 a5 1.0 a11 0.7077
a10 1.108 a73 1.0 a53 1.0 a33 1.0 a58 0.9641 a39 0.68
a19 1.0855 a72 1.0 a51 1.0 a29 1.0 a28 0.9595 a56 0.67
a50 1.08 a71 1.0 a49 1.0 a26 1.0 a18 0.9589 a37 0.66
a48 1.05 a69 1.0 a47 1.0 a24 1.0 a74 0.954 a75 0.58
a9 1.0425 a66 1.0 a45 1.0 a23 1.0 a60 0.9488 a68 0.56
a17 1.0197 a65 1.0 a44 1.0 a21 1.0 a0 0.9225 a20 0.55
a27 1.0035 a64 1.0 a42 1.0 a16 1.0 a31 0.9047 a12 0.54
a85 1.0 a63 1.0 a41 1.0 a15 1.0 a54 0.8492
a84 1.0 a62 1.0 a40 1.0 a14 1.0 a61 0.84
The (collective) strength propagation of all paths ending to nodes takes advantages of
weight of relations and, in particular, of weighted support relations

16. CONCLUSIONS AND FUTURE WORKS

17. Conclusions and Future Works
Argument Mining to extract arguments and relations between
them, to build a graph-based dialogical model
Considering the similarity between the comments, the sentiment
associated with them and their hierarchical structure, extract an
AF that models an online debate by identifying weighted attacks
and supports depending on their strength
To improve the quality of the argument graph construction,
further argument mining techniques may be exploited, even
though this may drastically impact on the computational cost.

18. Perspectives
Opinion mining:
understanding what people think about something VS understanding why
Going beyond critical thinking, i.e., a set of rational, deductive
arguments:
How to influence a real audience?
What is the role of emotions?
How to analyze human reasoning processes?
Big data:
social network posts, forums, blogs, product reviews, user comments to
newspapers articles, etc.
Deep learning:
fast and efficient machine learning algorithms large and unsupervised corpora
e.g., word embeddings: automatically learned feature spaces encoding
high-level, rich linguistic similarity between terms