Automatic online news topic ranking using media focus and user attention based on aging theory

Abstract

News topics, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. However, it is almost impossible to view all the generated topics, because of the large amount. So it will be helpful if all topics are ranked and the top ones, which are both timely and important, can be viewed with high priority. Generally, topic ranking is determined by two primary factors. One is how frequently and recently a topic is reported by the media; the other is how much attention users pay to it. Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included. However, inconsistency exists between both factors. In this paper, an automatic online news topic ranking algorithm is proposed based on inconsistency analysis between media focus and user attention. News stories are organized into topics, which are ranked in terms of both media focus and user attention. Experiments performed on practical Web datasets show that the topic ranking result reflects the influence of time, the media and users. The main contributions of this paper are as follows. First, we present the quantitative measure of the inconsistency between media focus and user attention, which provides a basis for topic ranking and an experimental evidence to show that there is a gap between what the media provide and what users view. Second, to the best of our knowledge, it is the first attempt to synthesize the two factors into one algorithm for automatic online topic ranking.

Identifying Concept-drift in Twitter Streams

“…Wang et.al [3], automates on-line news ranking, based on aging theory and burstiness of terms. The topic detection and tracking algorithm also integrates media focus and user attention parameters while ranking.…”

Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

“…MODEST utilizes topic tracking technique (Wang et al, 2008) In summary, TDT function provides the services of detecting and tracking the latest and emergent topics, analyzing the relationships of topics on the dynamics of the company. It meets the aforementioned demand, "to accurately grasp the latest business news and trends of the company".…”

“…The advantage of rulebased methods is that they can well handle particular linguistic phenomena which are peculiar to languages and domains. For example, the TCH MT system at IWSLT 2008 (Wang et al, 2008) used dictionary and hand-crafted rules (e.g. regular expression) to process NEs.…”

Web video topics discovery and structuralization with social network

“…Mining the evolution path of a topic can give users a global view about the topic, and help them to predict its evolution trend. Most of the previous works [6,25,4] focus on the topical evolution and organization in the content space, and model their life cycles in the [19,27] propose to investigate the geographical propagation phenomenon of video topics on the web, and mine the hot topics for different regions. It is notable that topic is also propagating among the users of social network.…”