Topic-focused Dynamic Information Filtering in Social Media.

With the quick development of online social media such as twitter or sina weibo in china, many users usually track hot topics to satisfy their desired information need. For a hot topic, new opinions or ideas will be continuously produced in the form of online data stream. In this scenario, how to effectively filter and display information for a certain topic dynamically, will be a critical problem. We call the problem as Topic-focused Dynamic Information Filtering (denoted as TDIF for short) in social media. In this paper, we start open discussions on such application problems. We first analyze the properties of the TDIF problem, which usually contains several typical requirements: relevance, diversity, recency and confidence. Recency means that users want to follow the recent opinions or news. Additionally, the confidence of information must be taken into consideration. How to balance these factors properly in online data stream is very important and challenging. We propose a dynamic preservation strategy on the basis of an existing feature-based utility function, to solve the TDIF problem. Additionally, we propose new dynamic diversity measures, to get a more reasonable evaluation for such application problems. Extensive exploratory experiments have been conducted on TREC public twitter dataset, and the experimental results validate the effectiveness of our approach.