Conversational based method for tweet contextualization

Bound to 140 characters, tweets are short and ambiguous by nature. It can be hard for a user without any kind of context to effectively understand what the tweet is about. Due to this restriction, it is, therefore, necessary to know the tweet’s context to make it easily understandable to a reader. In this paper, we treat the problem of tweet contextualization. We propose a specific method allowing to automatically contextualize tweets using information coming from social user interactions. Contrary to classical contextualization methods that only consider text information which is insufficient, since text information on Twitter is very sparse, we combine different types of signals (social, temporal, textual). Our experimental results validate the benefits of our approach and confirm that generated contexts contain relevant information with given tweet.