Modeling News Trust: A Defeasible Logic Programming Approach

FERNANDO SAGUI; ANA MAGUITMAN; CARLOS IVÁN CHESÑEVAR; GUILLERMO SIMARI,

INTELIGENCIA ARTIFICIAL. IBERO-AMERICAN JOURNAL OF ARTIFICIAL INTELLIGENCE

AEPIA (Asociación Española Para la Inteligencia Artificial)

Lugar: Madrid; Año: 2008 p. 63 - 72

1137-3601

Deciding whether to trust an information sources on the Web has been recognized as one of the main problems in today’s Information Society. In particular, assessing the credibility of news is a major research challenge. Typically, criteria such as freshness, relevance and viewer profile have been used by news services to rank news. However, these services do not deal with credibility from a qualitative perspective, and do not provide mechanisms to cope with controversial news reports. To fill this gap, this paper proposes a novel framework that brings the notions of trust and pluralism into play. In our proposal, we integrate dialectical reasoning into a news recommender system. The system is based on a set of basic principles characterizing the nature of trust. We use Defeasible Logic Programming (DeLP), a general–purpose defeasible argumentation formalism based on logic programming, to model the notion of trust. Our approach helps identify antagonism among sources of news and facilitates the analysis of opposing positions.