This document summarizes Clare Llewellyn's research on analyzing argumentation structures in user-generated online content. It discusses identifying topics and core points in discussions, classifying text spans into argumentation relationships like claims and counterclaims, and using machine learning techniques to automatically classify tweets into an argument structure. Key steps involve topic modeling, rule-based extraction of argument components, and training classifiers on feature sets like n-grams and part-of-speech tags to identify argumentative moves. The goal is to structure unstructured online discussions to better understand persuasive conversations on the web.

1. Clare Llewellyn
University of Edinburgh
Argumentation on the web - always vulgar
and often convincing?

2. User Generated Content

4. Various Conversations

5. Various Conversations
Main points of discussion:

RM is bad / old / Australian / has power over politicians / owns newspapers

RM does / doesn’t understand the internet

Free content is good / bad

The joke belongs to Tim Vine or Stuart Francis

Wider context discussion – PIPA / SOPA, Levenson Enquiry, phone hacking, TVShack

6. The Problem
Can we somehow structure this data so we can read it
and add to it at the most relevant point?

7. Solutions?

8. Argumentation
A participant makes a claim that represents their position
The participant backs up that claim with evidence
A counter claim challenges the position
The composer of the original claim may evaluate their position.

9. Claim
Counter Claim
Evidence
Counter Evidence
Evaluation

10. Macro / Micro Argumentation
Micro-level:
Simple claim
Qualified claim
Grounded claim
Grounded and qualified claim
Non-argumentative moves
Macro-level:
Argument
Counter argument
Integration (reply)
Non-argumentative moves
Weinberger and Fischer (2006)

11. Methodology*
* Adapted from Bal & Saint-Dizier (2009) and Mochales & Moens (2009, 2011)
1. Identify discussions on different topics
2. Identify spans of text that represent the core points in the discussion
3. Classify into a structure so as to define the relationships between spans of text
4. Present this information to users

12. Data Sets
Hand annotated corpus of tweets from the London Riots (7729)
www.analysingsocialmedia.org
Comments from the Guardian newspaper (partially hand annotated for topic)
Tweets with the #OR2012 (5416)

13. • Extract individual discussion
• Unsupervised clustering – very objective
• Selection of algorithm
Unigram / Bigram Frequency
Incremental Clustering
K-means
Topic modelling
Possible tools
NLTK (nltk.org)
Weka (www.cs.waikato.ac.nz/ml/weka/)
Mallet (mallet.cs.umass.edu)
Twitter Workbench (www.analysingsocialmedia.org/projects)
1. Topic Identification

14. Example Clusters
Topic Modelling Incremental Clustering

15. Are you doing what a human would do?
Results for comments data:
Evaluation

16. 2. Text Span Identification
Define a set of rules that allows the extraction of macro level argumentation
Annotated text you can use machine learning
Non-annotated you can define rules – is there something specific in the
language that indicates claim / counter claim
Claim
Counter Claim

17. Rules production
Method:
Rules are a generalisation from a large amount of data (14000 quotes)
Use Words / POS / Negation / Symbols
Use the rules to find this patterns where not explicitly mentioned in text
Examples:
– Before:
• @USERNAME:
– After:
• i don't
• i think you
• PRP VBP RB (Personal Pronoun, Verb singular present, Adverb)
– Both
• START X i 'm not
Tools:
LTT- TTT2 www.ltg.ed.ac.uk/software/

18. 3. Classify into a structure
Method
Based on Rose et al. (2008)
Use supervised machine learning to classify tweets into an argument structure
Using TagHelper tool kit (based on Weka)
– www.cs.cmu.edu/~cprose/TagHelper.html
– LightSide lightsidelabs.com
– Decide on a machine learning algorithm
– Define feature sets
– Train and test

19. Data Set Tweets
Coded with the classification system:
1. Claim without evidence
2. Claim with evidence
3. Counter-claim without evidence
4. Counter-claim with evidence
5. Implicit request for verification
6. Explicit request for verification
7. Comment
8. Other

20. Classification – Feature Selection
Features
Unigrams
+ line length
+ POS Bigrams
+ bigrams
+ punctuation
+ stemming
+ no stemming
+ rare words
+ line length, punctuation and rare words
+ no stop list
Algorithms
Support Vector Machine
Decision Tree
Naive Bayes

21. QUESTIONS?
Clare Llewellyn
University of Edinburgh
c.a.llewellyn@sms.ed.ac.uk