Be the first to like this
The rapid rate of information propagation on social streams has proven to be an up-to-date channel of communication, which can reveal events happening in the world. However, identifying the topicality of short messages (e.g. tweets) distributed on these streams poses new challenges in the development of accurate classification algorithms.
In order to alleviate this problem we study for the first time a transfer learning setting aiming to make use of two frequently updated social knowledge sources KSs (DBpedia and Freebase) for detecting topics in tweets. In this paper we investigate the similarity (and dissimilarity) between these KSs and Twitter at the lexical and conceptual (entity) level. We also evaluate the contribution of these types of features and propose various statistical measures for determining the topics which are highly similar or different in KSs and tweets.
Our findings can be of potential use to machine learning or domain adaptation algorithms aiming to use named entities for topic classification of tweets. These results can also be valuable in the identification of representative sets of annotated articles from the KSs, which can help in building accurate topic classifiers of tweets.