A Probabilistic Author-Centered Model for Twitter Discussions.

In a recent work some of the authors have developed an argumentative approach for discovering relevant opinions in Twitter discussions with probabilistic valued relationships. Given a Twitter discussion, the system builds an argument graph where each node denotes a tweet and each edge denotes a criticism relationship between a pair of tweets of the discussion. Relationships between tweets are associated with a probability value, indicating the uncertainty on whether they actually hold. In this work we introduce and investigate a natural extension of the representation model, referred as probabilistic author-centered model. In this model, tweets by a same author are grouped, describing his/her opinion in the discussion, and are represented with a single node in the graph, while edges stand for criticism relationships between author’s opinions. In this new model, interactions between authors can give rise to circular criticism relationships, and the probability of one opinion criticizing another is evaluated from the criticism probabilities among the individual tweets in both opinions.