Information Filtering on Micro-blogging Services

Micro-blogging is an emerging form of communication and became very popular in recent years. Micro-blogging services allow users to publish updates as short text messages that are broadcasted to the followers of users in real-time. Twitter is currently the most popular micro-blogging service. It is a rich and real-time information source and a good way to discover interesting content or to follow recent developments. However, the service is fairly simple, and rely on the concept of following other users. With the lack of classification or filtering tools, the user receives all messages posted by the users she follows. In most cases, the user receive a noisy stream of updates. In this paper, an information filtering system for Twitter is introduced. The system focuses on one kind of feeds on Twitter: Lists which are a manually selected group of users on Twitter. List feeds tend to be focused on specific topics, however it is still noisy due to irrelevant messages. Therefore, we propose an online filtering system, which extracts the niche topics in a list, filtering out irrelevant messages. To classify messages as relevant or irrelevant, next to text-based features, we utilize the social network of Twitter and different aspects of messages such as the temporal properties and the links included in the text. We evaluate our approach on a labeled dataset of lists and with the help of these novel features, we achieve accuracies between 85% and 95%. Finally, we present the online prototype of the system.