=Paper=
{{Paper
|id=None
|storemode=property
|title=Large Scale Agreements via Microdebates
|pdfUrl=https://ceur-ws.org/Vol-918/111110366.pdf
|volume=Vol-918
|dblpUrl=https://dblp.org/rec/conf/at/GabbrielliniT12
}}
==Large Scale Agreements via Microdebates==
https://ceur-ws.org/Vol-918/111110366.pdf
Large scale agreements via microdebates?
Simone Gabbriellini and Paolo Torroni
DEIS - University of Bologna
V.le Risorgimento, 2, 40136, Bologna - Italy
{simone.gabbriellini;paolo.torroni}@unibo.it
Abstract. Argumentative debates are a powerful tool for reaching agree-
ments in open environments. However, in large scale settings, such as
social networks and massive multi-agent systems, making sense of on-
going debates may be a compelling task, and debates risk to lose their
effectiveness. We thus propose “microdebates” to help organizing and
confronting users’ opinions in an automated way.
Keywords: abstract argumentation, negotiation in online debate, social net-
works, agreement facilitation.
1 Introduction
In the last decade, Web 2.0 platforms have rapidly become a mass phenomenon
whereby billions of individuals consume and share resources. In such a setting,
people became accustomed to arguing online in long-lasting debates, mainly in
the form of comments in social network platform, such as FaceBook1 and Twit-
ter,2 but also in the form of structured debates in debate-friendly tools. Among
the latter we mention DebateGraph,3 a powerful visualization tool; DBee,4 a
global debating network which features scoring and ranking with both positive
or negative values; Debate.org,5 a social network platform where users can start
debate and comment with pro/cons rating against the main argument in the
debate; and Deliberatorium,6 a community-moderated system where comments
need a moderator approval to be certified and visible by a larger community of
commenters.
Indeed, Mercier and Sperber’s argumentative theory of reasoning [1] tells
us that people are good at reasoning when they communicate through an ar-
gumentative context. Arguments are used by communicants to convince other
communicants, especially in absence of trust. When debating about an issues
?
AT2012, 15-16 October 2012, Dubrovnik, Croatia. Copyright held by the author(s).
1
http://www.facebook.com
2
http://www.twitter.com
3
http://www.debategraph.org
4
http://dbeelife.com
5
http://www.debate.org
6
http://cci.mit.edu/research/deliberatorium.html
in these online settings, we thus expect that users will not only publish their
opinion (like in a review setting), but also try to convince others by producing
arguments and rebut (attack) each others arguments.
Thus, argumentative debate seems to be a very promising tool for reaching
agreement, with particularly interesting applications in a number of settings,
including e-participation an policy-making. Indeed, a number of different plat-
forms are being developed within EU-funded projects such as ePolicy,7 whose
aims include deriving social impacts through opinion mining on e-participation
data extracted from the web; IMPACT,8 which is developing an innovative argu-
mentation toolbox for supporting open, inclusive and transparent deliberations
about public policy; and WEGOV,9 aiming to provide a toolset for exploiting
existing social networking sites to engage citizens in two-way dialogs as part of
governance and policy-making processes. The idea is that Web 2.0 platforms may
overcome the limitations of traditional opinion gathering methods such as ques-
tionnaires and polls, by allowing for online debates between informed citizens,
who can come up with new ideas and perspectives, as opposed to expressing
preferences upon some predetermined options, and all in a bottom-up fashion
[2]. However, the “freedom of expression” provided by online debates comes at
a cost.
In particular, when we think of settings involving multitudes of interacting
parties, such as social networks or large scale multi-agent systems, it becomes
very expensive for by-standers and external observers to make sense of opinions
emerging from online debates. An alternative approach could be to restrict one-
self to getting a feeling of the general sentiment of an ongoing discussion, without
necessarily having to really understand what is being said an why individuals
make such and such claim and express such and such opinion.
State of the art opinion mining/sentiment analysis techniques and tools look
at sentiment orientation of opinions in terms of values in a positive/negative
scale, typically by looking at corpora that include a certain number of sentences
(e.g., online reviews about some product) [3][4]. Such an approach can be very
effective especially if the domain is well defined (e.g., a product, or a service).
In domains such as customer reviews [5] where the concepts involved can be
defined in terms of specialized ontologies, and the jargon is pretty well defined
and narrow, the classification accuracy of existing sentiment analysis algorithms
is quite high. However, this is not the case in other domains, such as political
debate [6]. Importantly, sentiment analysis does not explicitly tell why certain
opinions are in place and how they relate to other opinions.
Our work goes in the perspective of encouraging free, unconstrained online
debate. In said policy-making context, this could be a tool in the hands of the
citizens, who can use it to voice their opinions, and convey them to the policy-
makers. To achieve this vision, we need to provide the policy-makers with tools
to automatically make sense of possibly very lengthy online debates. Such tools
7
http://www.epolicy-project.eu
8
http://www.policy-impact.eu
9
http://www.wegov-project.eu
should not only show the general sentiment around a specific topic, which is
the approach of current sentiment analysis tools. Instead, they should also be
able to identify specific opinions, and the relations among them. Such relations
could be positive (support) or negative (counter). We identify computational
argumentation, and in particular abstract argumentation [7], as the conceptual
and computational framework to model opinions and reason from them auto-
matically.
In computational abstract argumentation, as defined by Dung [7], an argu-
mentation framework is defined as a pair hX, Ai, where X is a set of atomic
arguments and A is a binary attacks relation over arguments, A ⊆ X × X,
with hx, yi ∈ A interpreted as “argument x attacks argument y.” Collections of
“justified” arguments can be described by various extension-based semantics [8].
Current online debating tools, such as those we cited above, build on and
extend the traditional forum-like structure, where users can reply or quote other
users, by introducing debate-oriented concepts. They are not very different from
a standard discussion forum with reputation, moderators and recommendation
features. Moreover, they require the user to comply and adapt to the abstractions
they are built around, and not vice-versa.
On the contrary, mainstream Web 2.0 social networking environments, such
as Twitter, are very successful in achieving user engagement, by blurring the
boundaries between ludic and serious [9]. Our proposal is thus to develop an
application based on a Twitter dialect that allows users to discuss about topics,
aided (in the back-end) by computational argumentation.
People use Twitter to talk about their daily activities and to seek or share
information [10] by broadcasting brief textual messages (tweets) to people who
“follow” their activity [11], in a micro-blogging fashion. Micro-blogging is a new
form of communication whereby users can describe their current status in short
posts distributed by instant messages, mobile phones, email or the Web [12]. We
therefore introduce the concept of microdebates.
2 Microdebates
Microdebates are inspired by Twitter’s microblogging character. A microdebate
is a stream of tweets where users annotate their messages by using some special
tags. Twitter posts contain terms called hashtags, i.e. a # symbol followed by
a text string, representing the stream of news the tweet belongs to. There may
be more than one hashtag per post (in case the same post is related to multiple
streams).
Users on Twitter started the phenomenon of adding tags to their messages
sometime around February 2008 [13]. Twitter tagging behavior is distinct from
those in other Web 2.0 systems, because users are less likely to index messages
for later retrieval [14], and this is reflected by the fact that tagging patterns in
Twitter have a conversational, rather than organizational, nature [15].
In line with Twitter users’ tagging behavior, we propose a Twitter dialect
consisting of a custom set of tags to be used to annotate tweets in microdebates:
– a hashtag that will identify the discussion (e.g., #debateName): as customary,
this ensures that the tweet will appear in the right stream (microdebate);
– one or more annotation(s) using the $/!$ tags, where
• $opinionName specifies the opinion this tweet supports, while
• !$opinionName specifies the opinion this tweet counters.
The syntax for a microdebate is thus as follows:
h microdebate i ::= h content element i+
h content element i ::= h hashtag i h debate item i+
h debate item i ::= h free text comment i
| $h opinionName i
| !$h opinionName i
Notation h . . . i+ indicates that multiple occurrences of the element in angle
brackets are allowed. A free text comment is any free text not containing the spe-
cial characters #/$. An opinionName is a tag given to a certain opinion; it should
be formatted according to Twitter’s tag syntax (alphanumeric strings with no
spaces). The order of content elements in a debate, and of debate item inside
a content element, is immaterial. An example of a hypothetical microdebate is
shown in Figure 1.
A microdebate is thus a set of elements of content (such as tweets), each con-
taining a contribution to a debate, such as an opinion, and may contain explicit
references to other elements of content. Each element of content in a microde-
bate may use some combinations of characters (similar to hashtags) expressing
positive or negative relations with other content elements. In this way, all that is
asked of the user is to use certain combinations of characters in order to put their
opinion in the context of other opinions. In exchange, users will receive a help
in making sense of a (possibly lengthy) debate: microdebates can be processed
by automatic reasoners, such as argumentation-based reasoning tools [16] and
the output can be visualized graphically as clusters of coherent opinions, where
different cluster may attack each other. This could foster awareness of different
opinions on a topic and encourage arguers to reach an agreement.
This is how microdebates work:
1. content elements are tweets with a suitable hashtag, used to identify the
microdebates users are contributing to. (Twitter then displays such tweet in
the public stream associated with such a hashtag);
2. users annotate their tweets using $/!$ tags. When a user A specifies $opinion1 ,
it means that his comment supports opinion1 , which can be an opinion ex-
pressed by the user himself in the comment, or by another users B. In that
case opinion1 will be seen as based on two comments, A’s and B’s respec-
tively. The opinion name is abstract, and does not need to be a summary of
the user’s opinion;
Fig. 1. An example of microdebate on Twitter. $ tags and !$ tags represent arguments
and attacks between them.
3. users can attack (counter) opinions using the !$ tag, e.g., by adding the
!$opinion2 item in his tweet. This negation states that the tweet is a com-
ment, which supports a certain opinion, and at the same time attacks opinion2 .
This enables establishing relations amongst opinions;
4. if a user adds a tweet with a new $ tag, the user is in fact introducing a new
opinion in the microdebate;
5. reply and re-tweets are handled like new tweets, thus personal replies are
irrelevant to the debate (unless they contain $/!$ tags that are meaningful
for this debate).
3 Microdebates at work
We implemented a first prototype of the system as an agent-based model in
NetLogo [17]. In this model, each agent represents an argument used in the
microdebate. Attacks between arguments are represented by directed links from
an agent to another one. We used the Twitter API to retrieve tweets from
Twitter, and the Netlogo API to bundle our system into an extension with a
basic parser (called microdebate), that enables NetLogo to visualize and analyze
the resulting argumentation framework.
As a first step, we extract and parse the stream of tweets in a selected mi-
crodebate, so that we have:
– for each new $opinionN ame tag, a new argument is created;
– for each new !$opinionN ame tag, a new attack link is created against the
named argument
To retrieve the microdebate, it suffices to enter a debate identifier, in the
form $debateN ame, in the GUI’s debate text box (see Figure 2). In our exam-
ple, the debate identifier is $energyalt. Of course, there is a difference between
an opinion and an argument, the former being a claim without evidence, the
latter being a claim with evidence (supported to convince others that the claim
is supported). At the same time, not all the comments expressed by users can
turn out to be “well-formed” arguments. Nevertheless, at this stage, we turn ev-
ery $opinionN ame into an argument belonging to a preliminary argumentative
framework that we define naive.
In order to improve our framework in this respect, we store inside each ar-
gument all the free text comments that refers to $opinionN ame in the microde-
bate. We then propose argument classification as a way to verify if each claim is
a well-formed argument or not (see Figure 3):
– if, based on the comments it contains, the claim proves to be indeed a well-
formed argument, we keep it in the argumentative framework;
– otherwise, if based in its comments, the claim proves not to be a well-formed
argument, we exclude it from the argumentative framework.
Fig. 2. From a Twitter microdebate to an argumentation framework.
This method allows to obtain a polished up argumentation framework, where
all (and only) well-formed arguments are retained. Being this an initial proto-
type, such processing is currently made by hand. In the concluding section of
this paper we elaborate on how we plan to improve this stage.
Once we have only arguments and attacks among arguments, we can compute
semantic extensions on the argumentative framework.
Our prototype can compute extensions based on a variety of semantics, in-
cluding admissible, complete, grounded, ideal, preferred, stable, semistable, and
stage semantics.
In Figure 4 the complete semantic extension has been calculated, that states:
a set of arguments is a semantic extension iff the set include all the argu-
ments that it defends. As we can see, the two arguments $sugarmills and
$recyclethewaste are the winners over $windmills, demonstrating that the stream
of tweets that compose the microdebate #energyalt can be summarized in a very
compact and efficient way.
Fig. 3. Polished up argumentative framework
4 Conclusions
The purpose of our proposal is to help reaching an agreement in a debate by
formalizing and rationalizing a debate. Recent findings in cognitive science [1]
suggest that people are good at arguing, actually that the main function of
reasoning is argumentative. However, when big numbers are in play, it may
be difficult for by-standers and potential contributors to make sense of online
discussions. By microdebates, we aim to help people understand how a topic
is being discussed, what positions (arguments) are involved in the debate, and
what are the relations of attacks between such arguments. Ultimately, we aim to
help people argue in a better way, defend their reasons and learn how to rebut
each other’s attacks.
The “microdebates” we propose follow the bottom-up argumentation philos-
ophy introduced in [2]. Users contribute to a debate by sending out annotated
comments, and as a result, arguments arise bottom-up. In particular, it is not
Fig. 4. Visualisation of the complete semantics
necessary that the same user defines a well-formed argument, because many
users can contribute, tweet by tweet, to support the same argument, by adding
elements that turn a claim into a well-formed argument, or by finding rebuttals
and counter-attacks to the arguments in place.
All this effort should help to produce a more relevant discussion, in a very
user-friendly way: users can annotate their messages in an everyday-life style,
and they do not have to conform to (rigid) rules of another debate-oriented
interface.
The computational underpinning of our proposal is abstract argumentation,
and is orthogonal to the choice of an extension-based semantics, by design. Dif-
ferent semantics may suit to different applications in various ways.
Our tool is partially implemented. Two NetLogo extensions are already im-
plemented: microdebate, for the processing of tweets, and and arguments, for
computing extensions. We are still at an early stage in argument classification,
whose purpose is to filter arguments and keep well-formed ones only. For that,
we plan to use third-party semantic tagging tools, such as COGITO.10 Our idea
is to define what a “well-formed argument” is by way of COGITO rules, and
delegate to a COGITO module a fully automated argument filtering process.
We also plann to extensively test our method with case-studies, in order to
understand the effectiveness of this approach in a real-world setting. In partic-
ular, we are designing tests with debates concerning renewable energy sources,
environment and sustainability, in the context of the above-mentioned ePolicy
EU project.
The research presented here is high-risk, because many innovations are re-
quired all together for this to succeed. For instance, using our syntax,Twitter
users may develop habits that could be different from what we expect, leading
to unforeseen system behavior. The parser will probably need to get adjusted
once some data from a case-study has been retrieved.
Moreover, since our method (and bottom-up argumentation in general) needs
active engagement from users, we could end up with poor data to analyze if
our users will not get truly involved in the process. However, we hope that
this factor may be mitigated by a unique feature of microdebates: they do not
require a dedicated platform. Users do not need to learn and get accostumed to
new interfaces, because microdebates are only based on tweets (as opposed to
graphic items, such as bubbles and links).
Having said that, we believe that the strengths and potential of our approach
overcome its limitations. First, microdebates allows deep analysis of arguers
position in a debate, an important step toward the reaching of an agreement
between arguers. Furthermore, by-standers may be encouraged to participate,
since they have a clear visualization of what is happening in the debate - and
what position (arguments set) is going to dominate or get defeated.
Second, there is no need to manually analyze documents, because posts are
annotated by users. This form of crowdsourcing reduces the amount of qualified
labor needed. An important bottle-neck is argument classification, but we hope
to be able to set up automated procedures for it.
Third, the microdebate approach develops a technology that may be useful
in many other domains, because it is based on a multi-disciplinary approach
that well suits the needs of diverse domains where debates are allowed, such
as policy-making, Moreover, such technology, initially developed for human-to-
human interaction, may as well be exported to software agents. We can think
of agents communicating with one another in a tweet-like fashion, and new al-
gorithms could be developed to automatically reach agreements between agents
on a variety of domain. This may open a promising strand of research.
Fourth, our approach exploits the so-called wisdom of the crowds (as in
bottom-up argumentation): arguments arise bottom-up from the debate and it is
not necessary for a single user to express the argument entirely, because other
users can contribute to the same argument. Finally, it has an open approach
that allows all users to visualize dynamically the outcome of the analysis.
10
http://www.expertsystem.net/products-technology/cogito-semantic-tagger
Acknowledgments
We thank the anonymous reviewers for their useful and encouraging feedback.
This work was partially supported by the ePolicy EU project FP7-ICT-2011-
7, grant agreement 288147. Possible inaccuracies of information are under the
responsibility of the project team. The text reflects solely the views of its authors.
The European Commission is not liable for any use that may be made of the
information contained in this paper.
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