  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.
