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We propose a novel automatic online algorithm for news topic ranking based on an aging theory, using both media focus and user attention
Automatic online news topic ranking using media focus and user attention based on aging theory
CIKM, pp.1033-1042, (2008)
WOS SCOPUS EI
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 ...More
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- A new problem arises: how to rank the news topics to show the top ones with high priority, which are both timely and important?.
- More factors must be taken into consideration: (1) every news story of a topic contributes to its importance, while the contribution decays along the timeline; (2) topics that attract more users’ attention should be ranked higher
- News stories are gathered from many Websites and organized into news topics by practical Web applications like Google News
- How much do users like to read news stories about the topic? This one is called user attention. Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included by the two factors
- Through investigation of characteristics of topics, we have found out that topic ranking is determined by two primary factors: media focus and user attention
- We propose a novel automatic online algorithm for news topic ranking based on an aging theory, using both media focus and user attention
- The main contributions of this paper are twofold: (1) 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
- Preliminary experiments are firstly performed on a training dataset to find proper values for parameters.
- The analysis on results of calculating inconsistency between media focus and user attention is demonstrated.
- The discussions of topic ranking results are presented .
- 5.1 Dataset and Experimental Setup
- TestingSet is used to perform the automatic online news topic ranking experiment.
- Topics are ranked in every time slot online automatically, using the method described in Section 4.3.
- Snippets of the latest news story are shown as the summaries of topics.
- In this way, the up-to-date status of a topic will be viewed.
- It is worth noting that media focus and user attention curves are provided for users to know the topic trends
- The authors propose a novel automatic online algorithm for news topic ranking based on an aging theory, using both media focus and user attention.
- Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included.
- Empirical evaluation on the topic ranking result indicates that the proposed topic ranking algorithm reflects the influence of time, the media and users
- Table1: Contingency
- Table2: Top 10 topics on search engine related companies at 8:00 a.m., Oct 26, 2007
- Topic detection and tracking (TDT) are intended to structure news stories from newswires and broadcasts into topics . Approaches in TDT were mainly variants and improvements of the single pass method and agglomerative clustering algorithms [2, 3, 7, 14, 15, 16, 19, 21, 22, 23]. Although  concluded that time information “did not help” improve the new event detection results, some recent work has utilized the aging theory or timeline analysis, and achieved good performance in TDT and hot topic extraction [4, 5]. The state-of-the-art TDT techniques are used to generate topics from news stories in our work. We also apply the aging theory both in the TDT process and the calculation of media focus and user attention. However, traditional TDT tasks  are not the main focus of our work.
- This work is supported by the Chinese National Key Foundation Research & Development Plan (2004CB318108), Natural Science Foundation (60621062, 60503064, 60736044) and National 863 High Technology Project (2006AA01Z141)
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