Content uploaded by Adam Zachary Wyner
Author content
All content in this area was uploaded by Adam Zachary Wyner on May 04, 2015
Content may be subject to copyright.
Dimensions of Argumentation in Social Media
Jodi Schneider1, Brian Davis1, and Adam Wyner2
1Digital Enterprise Research Institute, National University of Ireland, Galway,
firstname.lastname@deri.org
2Department of Computer Science, University of Liverpool
A.Z.Wyner@liverpool.ac.uk
Abstract. Mining social media for opinions is important to governments and
businesses. Current approaches focus on sentiment and opinion detection. Yet,
people also justify their views, giving arguments. Understanding arguments in
social media would yield richer knowledge about the views of individuals and
collectives. Extracting arguments from social media is difficult. Messages appear
to lack indicators for argument, document structure, or inter-document relation-
ships. In social media, lexical variety, alternative spellings, multiple languages,
and alternative punctuation are common. Social media also encompasses numer-
ous genres. These aspects can confound the extraction of well-formed knowledge
bases of argument. We chart out the various aspects in order to isolate them for
further analysis and processing.
1 Introduction
In social media, people continually express their opinions. These opinions are used to
help businesses understand their customers and for governments to understand citizen
needs and desires. 80% of data on the Web and on internal corporate intranets is un-
structured, hence analysing and structuring the data is a large and growing endeavour3.
In our view, an important way in which the data can be analysed and further structured
is in terms of argumentation. However, we first have to understand the dimensions of
expression of argument, which can then be isolated for further analysis and processing.
Besides driving the input to knowledge bases, argumentation can also be used for the
output of knowledge bases, providing justification and explanation.
Consistency in knowledge bases is essential since we cannot draw informative in-
ferences with inconsistent knowledge bases. In social media, it is obvious that there is
lots of disputed information concerning matters of fact (what is or is not true) and of
opinion (what individuals believe or prefer). To make use of the knowledge in social
media and reason with it, we must treat inconsistency. While a knowledge base may
be filtered or truncated based on heuristics, some inconsistencies may remain, whether
explicitly or implicitly. Alternatively, users may resolve inconsistencies based on lim-
ited weighting information such as provenance or a preference ranking. But to decide
which fact is correct or which opinion is most relevant to them, consumers need to go
beyond such rankings and to understand how statements are justified and the sources of
disagreement. For this, we believe argumentation theory is crucial.
3http://www.gartner.com/it/page.jsp?id=1454221
Current approaches to extracting and retrieving information from social media use
opinion summarisation (e.g. summing votes for or against), topic-based [8] and feature-
based text summarisation [7], and visualisation [4]. Such approaches discover trends,
relationships, and correlations in data. While they may record inconsistency, they do not
provide the means to articulate an elaborate structure of justification and disagreement.
While social media records arguments, current information extraction and knowl-
edge acquisition systems do not represent these arguments, hence people must assim-
ilate and use them unaided. One approach in the direction of representing argument is
stance detection [9], which concerns identifying which side a party is taking in a debate,
and which responses are rebuttals. While this adds further substance, it does not enable
identifying the structure and layers of rationales for and against a position.
Even though current approaches are highly useful in decision making, the whole
chain of rationale may be crucial. The overall popularity of an opinion is not as impor-
tant as the reasons supporting it: overwhelming numbers of people buying a product
may not matter as much as a particular reason for not buying it. The issue is whether it
is the right product for the buyer, which is a matter not only of the pros and cons, but
also of the explanations and counterarguments given. In our view, current approaches
detect problems, but obscure the chains of reasoning about them.
The challenge is to extract the arguments from the text, turning textual sources into
a representation that we can reason with even in the face of inconsistency. We explore
these issues as follows. In Section 2, we first introduce the goals of argumentation ex-
traction and provide a sample problem. In Section 3, we outline formalisations of argu-
mentation that enable reasoning with inconsistent data. However, we note the gap be-
tween the formalisation and the argument analysis and extraction from source material.
This highlights the need for greater understanding of the dimensions of argumentation
in the social media landscape, which we discuss in Section 4. In closing, we outline the
next steps to bridge between textual sources and the target formal analysis.
2 Goals and Example
Our goal is to extract and reconstruct argumentation into formal representations which
can be entered into a knowledge base. Drawing from existing approaches to subjectiv-
ity, topic identification, and knowledge extraction, we need to indicate disagreements
and other relationships between opinions, along with justifications for opinions. This is
currently done by hand. The goal really is to figure out how to automate the analysis.
Issues include the informality of language in social media, the amount of implicit infor-
mation, and various ‘meta’ information that contributes to the argument reconstruction,
as we later discuss.
Consider the situation where a consumer wants to buy a camera. In reviews, there
may be a high degree of negative sentiment related to the battery, which a consumer
can use to decide whether or not she wants to buy the camera. Yet, in the comments to
a discussion, we may find statements about whether or not this is in fact true, whether
it outbalances other features of the camera, whether the problem can be overcome,
and so on. It is not enough to say “you shouldn’t buy this camera” – one needs to
give the reasons why. Then the debate becomes an argument about the justifications:
“it’s lightweight, you should buy it”, “the lens sucks, you shouldn’t buy it”, “the lens
doesn’t matter, it has a bad battery” and so on. The argument is not just point and
counterpoint; it is also about how each premise is itself supported and attacked. Each of
these justifications may be further discussed, until the discussion ‘grounds out’ with no
further messages. This has the structure of an argument, where points and counterpoints
are presented, each implied by premises, which themselves can be argued about further.
Thus we envision deepening the knowledge bases constructed from social media
based on the justifications given for statements. To do so, we need to better understand
how disagreements and justifications–which we refer to collectively as argumentation–
are expressed in social media. However, we first consider our target formalisation.
3 Formalising Argumentation and Argumentation Schemes
Abstract argumentation frameworks have been well-developed to support reasoning
with inconsistent information starting with [6] and much subsequent research ([1], [2],
[3]). An abstract argument framework, as introduced by Dung, [6] is a pair AF =
hA,attack i, where Ais a set of arguments and attack a binary relation on A. A va-
riety of semantics are available to evaluate the arguments. For example, where AF =
h{A1, A2, A3, A6, A7},{att(A6, A1), att(A1, A6), att(A7, A2)}i, then the preferred
extensions are: {A3, A6, A7}and {A2, A3, A7}.
However, Dung’s arguments are entirely abstract and the attack relation is stipu-
lated. In other words, it is unclear why one argument attacks another argument, as there
is no content to the arguments. In order to instantiate arguments we need argumentation
schemes, which are presumptive patterns of reasoning [10].
An instantiated argumentation scheme, such as Position To Know, has a textual
form such as: 1. Ms. Peters is in a position to know whether Mr. Jones was at the party.
2. Ms. Peters asserts that Mr. Jones was at the party. 3. Therefore, presumptively, Mr.
Jones was at the party. This has a formal representation in a typed logical language with
functions from argument objects to predicates. The language formally represents the
propositions required of the scheme as well as aspects of defeasible reasoning [12].
While this is an attractive approach to tying textual arguments to abstract argumen-
tation, it relies on abstracting away the context and auxiliary aspects. It is far from clear
how an argument such as represented in Section 2 can be transformed into a formal ar-
gumentation scheme so that it can be reasoned in an argumentation framework. To make
use of the formal analyses and related implemented tools for social media discussions,
a range of additional issues must be considered, as we next discuss.
4 Dimensions of Expression
To extract well-formed knowledge bases of argument, we must first chart out the various
dimensions of social media, to point the way towards the aspects that argumentation
reconstruction will need to consider, so that we later can isolate these aspects.
Social media encompasses numerous genres, each with their own conversational
styles, which affect what sort of rhetoric and arguments may be made. One key fea-
ture is the extent to which a medium is used for broadcasts (e.g. monologues) versus
conversations (e.g. dialogues), and in each genre, a prototypical message or messages
could be described, but these vary across genres due to social conventions and techni-
cal constraints. De Moor and Efimova compared rhetorical and argumentative aspects
of listservs and blogs, identifying features such as the likelihood that messages receive
responses, and whether spaces are owned communities or by a single individual, and
the timeline for replies [5]. Important message characteristics include the typical and
allowable message length (e.g. space limitations on microblogs) and whether messages
may be continually refined by a group (such as in StackOverflow).
Metadata associated with a post (such as poster, timestamp, and subject line for
listservs) and additional structure (such as pingbacks and links for blogs) can also be
used for argumentation. For example, a user’s most recent post is generally taken to
identify their current view, while relationships between messages can indicate a shared
topic, and may be associated with agreement or disagreement.
Users are different, and properties of users are factors that contribute not only to
substance of the user’s comment, but as well to how they react to the comments of
others. These include demographic information such as the user’s age, gender, location,
education, and so on. In a specific domain, additional user expectations or constraints
could also be added. Different users are persuaded by different kinds of information.
Therefore, to solve peoples’ problems, based on knowledge bases, when dealing with
inconsistency, understanding the purposes and goals that people have would be useful.
Therefore, the goals of a particular dialogue also matter. These have been consid-
ered in argumentation theory: Walton & Krabbe have categorized dialogue types based
on the initial situation, participant’s goal, and the goal of the dialogue [11]. The types
they distinguish are inquiry, discovery, information seeking, deliberation, persuasion,
negotiation and eristic. These are abstractions–any single conversation moves through
various dialogue types. For example, a deliberation may be paused in order to delve into
information seeking, then resumed once the needed information has been obtained.
Higher level context would also be useful: different amounts of certainty are needed
for different purposes. Some of that is inherent in a task: Reasoning about what kind
of medical treatment to seek for a long-term illness, based on PatientsLikeMe, requires
more certainty than deciding what to buy based on product reviews.
Informal language is very typically found in social media. Generic language pro-
cessing issues, with misspellings and abbreviations, slang, language mixing emoticons,
and unusual use of punctuation, must be resolved in order to enable text mining (and
subsequently argumentation mining) on informal language. Indirect forms of speech,
such as sarcasm, irony, and innuendo, are also common. A step-by-step approach, fo-
cusing first on what can be handled, is necessary.
Another aspect of the informality is that much information is left implicit. There-
fore, inferring from context is essential. Elliptical statements require us to infer com-
mon world knowledge, and connecting to existing knowledge bases will be needed.
We apply sentiment techniques to provide candidates for argumentation mining
and especially to identify textual markers of subjectivity and objectivity. The argu-
ments that are made about or against purported facts have a different form from the
arguments that are made about opinions. Arguments about objective statements provide
the reasons for believing a purported fact or how certain it is. Subjective arguments
might indicate, for instance, which users would benefit from a service or product (those
similar to the poster). Another area where subjective arguments may appear is discus-
sions of the trust and credibility about the people making the arguments.
5 Conclusions
There is intense interest in extracting information from social media, and particularly in
the views people express, and how they express agreement and disagreement, and jus-
tify their views. This motivates us to translate existing approaches for text analysis and
argumentation mining into techniques for identifying and structuring arguments from
social media [13]. But these tools and resources must first be adapted for differences
in social media. Understanding these differences is a critical first step, therefore, we
have discussed the dimensions of argumentation in social media. Our purpose has been
to make explicit the various challenges, so that we can move towards creating knowl-
edge bases of argumentation. Next, the challenges identified should be transformed into
requirements.
Acknowledgements
The first and second authors’ work was supported by Science Foundation Ireland under
Grant No. SFI/09/CE/I1380 (L´
ıon2). The third author was supported by the FP7-ICT-
2009-4 Programme, IMPACT Project, Grant Agreement Number 247228. The views
expressed are those of the authors.
References
1. T. J. M. Bench-Capon. Persuasion in practical argument using value-based argumentation
frameworks. Journal of Logic and Computation, 13(3):429–448, 2003.
2. A. Bondarenko, P. M. Dung, R. A. Kowalski, and F. Toni. An abstract, argumentation-
theoretic approach to default reasoning. Artificial Intelligence, 93:63–101, 1997.
3. M. Caminada and L. Amgoud. On the evaluation of argumentation formalisms. Artificial
Intelligence, 171(5-6):286–310, 2007.
4. C. Chen, F. Ibekwe-Sanjuan, E. San Juan, and C. Weaver. Visual analysis of conflicting
opinions. In Proceedings of IEEE Symposium on Visual Analytics Science and Technology
(VAST), 2006.
5. A. de Moor and L. Efimova. An argumentation analysis of weblog conversations. In The 9th
International Working Conference on the Language-Action Perspective on Communication
Modelling (LAP 2004), Rutgers University, 2004.
6. P. M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic
reasoning, logic programming and n-person games. Artificial Intelligence, 77(2):321–357,
1995.
7. Y. Lu, C. Zhai, and N. Sundaresan. Rated aspect summarization of short comments. In
Proceedings of the 18th International Conference on World Wide Web (WWW ’09), 2009.
8. I. Titov and R. McDonald. Modeling online reviews with multi-grain topic models. In
Proceedings of the 17th International Conference on World Wide Web (WWW ’08), 2008.
9. M. A. Walker, P. Anand, R. Abbott, J. E. F. Tree, C. Martell, and J. King. That’s your
evidence?: Classifying stance in online political debate. Decision Support Sciences, 2011.
10. D. Walton. Argumentation Schemes for Presumptive Reasoning. Erlbaum, N.J., 1996.
11. D. N. Walton. Commitment in dialogue. State University of New York Press, Albany, 1995.
12. A. Wyner, K. Atkinson, and T. Bench-Capon. A functional perspective on argumentation
schemes. In Proceedings of Argumentation in Multi-Agent Systems (ArgMAS 2012), 2012.
13. A. Wyner, J. Schneider, K. Atkinson, and T. Bench-Capon. Semi-automated argumentative
analysis of online product reviews. In Proceedings of the Fourth International Conference
on Computational Models of Argument (COMMA ’12), 2012.
