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With the quick development of online social media such as twitter or sinaweibo in china, many users usually track hot topics to satisfy their desiredinformation need. For a hot topic, new opinions or ideas will be continuouslyproduced in the form of online data stream. In this scenario, how toeffectively filter and display information for a certain topic dynamically,will be a critical problem. We call the problem as Topic-focused DynamicInformation Filtering (denoted as TDIF for short) in social media. In thispaper, we start open discussions on such application problems. We first analyzethe properties of the TDIF problem, which usually contains several typicalrequirements: relevance, diversity, recency and confidence. Recency means thatusers want to follow the recent opinions or news. Additionally, the confidenceof information must be taken into consideration. How to balance these factorsproperly in online data stream is very important and challenging. We propose adynamic preservation strategy on the basis of an existing feature-based utilityfunction, to solve the TDIF problem. Additionally, we propose new dynamicdiversity measures, to get a more reasonable evaluation for such applicationproblems. Extensive exploratory experiments have been conducted on TREC publictwitter dataset, and the experimental results validate the effectiveness of ourapproach.

Topic-focused Dynamic Information Filtering in Social Media