Raheleh Makki Niri
Axel J. Soto
Evangelos E. Milios
Stephen Brooks
Overview
Users of microblogging social networks produce millions of short messages every day. Retrieving relevant information to a particular event from this sheer volume of data is not a trivial task. In this paper, we present a framework for the retrieval of Twitter posts that are relevant to a set of political debates. Our main contribution is the proposal of a set of strategies for involving the user in the retrieval process, so that by presenting to her meaningful posts to be labeled, the method achieves a noticeably higher accuracy. The correct retrieval or labeling could be provided by an external information source such as a domain expert, or simulated with an oracle. A key aspect of active retrieval methods is to request the labels of the instances that help improve the retrieval accuracy the most, while keeping the number of labeling requests to a minimum. The proposed strategies for selecting labeling requests make use of the textual content of tweets and their structural information. The experimental results show the advantages of the proposed methods and the effectiveness of the selection strategies for involving the user in the retrieval process.
Publications
Raheleh Makki, Axel J. Soto, Stephen Brooks, Evangelos E. Milios, Active Information Retrieval for Linking Twitter Posts with Political Debates, 14th IEEE International Conference on Machine Learning and Applications (ICMLA). December 9-11, 2015; Miami, USA.