icegov2012 proceedings

The concepts are illustrated in Figure 2. Technically. given a
corpus of opinions O = {o1, o2, … ,om}, and a universe of possible
terms K ={k1, k2, …, kn}, we consider a topic ti of an opinion oj to
be a nonempty subset of the terms ki1, …, kip contained in K
satisfying a set of criteria for topic quality. In this sense, a topic
can be characterized as a set of cohesive terms associated with
opinions that share a common theme.
Fig. 2. DECIDE 2.0 Concepts
A contextualized search system starts with a piece of text d
reflecting a general context or topic of interest td and identifies a
set N = {t1, t2, …, tq} of new topics related to topic td. The
performance of the main component of the system - a
contextualized topic suggester, can be judged according to various
criteria [10]:
o Global coherence: The new suggested topics (topics in N)
must be relevant to the general topic of interest td.
o Local coherence: Each suggested topic must be of high
quality according to the criteria of the domain. Such criteria
might include: 1) measures of coherence - each topic
description is constituted of tightly related terms and opinions,
2) descriptiveness - the terms used to identify the topics are
good descriptors, 3) discriminative power - the terms help
differentiate among other suggested topics , 4) conciseness -
the topic is summarized in few terms; etc.
o Coverage: The set of new suggested topics (N) must contain
most of the topics considered to be relevant.
o Novelty: The set of new topics (N) must go beyond the
information captured in the initial topic td.
o Diversity: topics in N must be sufficiently diverse from each
other.
Using context-based search, given a set O of opinions provided by
citizens via Twitter and a set of possible terms K on a topic of
community discussion td, we can identify new topics associated
with those opinions. Clearly, users might confront opinions,
including positive or negative remarks within their tweets.
Sentiment analysis techniques [11][12] allow to determine the
attitude of a speaker (citizen) or a writer with respect to some
topic or the overall contextual polarity of a document (e.g.
positive, negative, neutral). As a final result, we will be able to
identify different logical predicates associated with a user’s
opinion, the topics involved, and the attitude of the speaker.
As an example, consider the following piece of text d (extracted
from http://www.conservapedia.com):
ObamaCare, more formally known as "The Patient Protection and
Affordable Care Act," was passed by Congress on March 21,
2010, and signed into federal law by President Barack Obama on
March 23. This law began the process to socialize the United
States health care system. The centerpiece of ObamaCare is the
individual mandate, a provision that makes it mandatory for every
citizen to purchase private health insurance, which is
unprecedented in American history.
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By applying techniques described in [13] it is possible to identify
a set of good topic descriptors and topic discriminators (e.g.
preventive health care , obamacare, etc.) as well as a list of
representative hashtags associated with this topic, such as #hcr
and #obamacare. A context-based search system might then
identify the following set of opinions, among others:
o1: Thanks for the new health law, pregnant women can receive
many free preventive health services and screenings. (tweeted by
user U1)
o2: Health care reform does not make health care more
affordable it makes people buy something they can’t afford.
(tweeted by user U2)
From the opinions above, logical predicates can be extracted,
structuring previous information in logic programming fashion:
opinion(o1, “Thanks for…”)
opinion(o2, “Thanks for…”)
topic(t1, preventive health care)
useropinion(u1,o1, t1, positive)
useropinion(u2,o2, t1,negative)
Such set of logical predicates derived from citizens’ opinions on a
particular context will provide a Citizens’ Opinion Knowledgebase
(see Figure 1), on top of which an argumentative analysis
will be carried out.
3.2. Argumentation using DeLP
Over the last few years, argumentation systems have been gaining
increasing importance in several areas of Artificial Intelligence,
mainly as a vehicle for facilitating rationally justifiable decision
making when handling incomplete and potentially inconsistent
information [7]. Argumentation provides a sound model for
dialectical reasoning, which underlies discussions or opinion
confrontation in social networks.
Rule-Based Argumentation Systems, such as Defeasible Logic
Programming (DeLP) [14] are increasingly being considered for
applications in developing software engineering tools, constituting
an important component of multi-agent systems for negotiation,
problem solving, and for the fusion of data and knowledge. Such
systems implement a dialectical reasoning process by determining
whether a proposition follows from certain assumptions,
analyzing whether some of those assumptions can be disproved by
other assumptions in our premises. In this way, an argumentation
system provides valuable help to analyze which assumptions from
our knowledge base are really giving rise to inconsistency and
which assumptions are harmless.
In DeLP we refer to a knowledgebase as a pair of sets (KS, KD),
distinguishing strict and defeasible knowledge. Strict knowledge
(KS) corresponds to the knowledge which is certain; typical
elements in KS are statements or undisputable facts about the
world (e.g. adopting the representation used in logic
programming, implications of the form Q(x)←P(x)). The strict
knowledge is consistent, i.e. no contradictory conclusions can be
derived from it. Defeasible knowledge (KD) corresponds to that
knowledge which is tentative, modelled through “rules with
exceptions” (defeasible rules) of the form “if P then usually Q”
(e.g., “if somebody is a citizen, it usually votes”). Such rules
model our incomplete knowledge about the world, as they can
have exceptions (e.g., a citizen may be travelling abroad without
access to a voting place, or may not be willing to vote).
Syntactically, a special symbol (⇐) is used to distinguish
“defeasible” rules from logical implications.