Abstract
Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 |
| Pages | 32-39 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2012 Dec 1 |
| Event | 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China Duration: 2012 Dec 4 → 2012 Dec 7 |
Publication series
| Name | Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 |
|---|
Other
| Other | 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 |
|---|---|
| Country | China |
| City | Macau |
| Period | 12-12-04 → 12-12-07 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
Cite this
}
The retrieval of important news stories by influence propagation among communities and categories. / Lin, Yu Fan; Kao, Hung-Yu.
Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012. 2012. p. 32-39 6511862 (Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - The retrieval of important news stories by influence propagation among communities and categories
AU - Lin, Yu Fan
AU - Kao, Hung-Yu
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.
AB - Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.
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UR - http://www.scopus.com/inward/citedby.url?scp=84878441996&partnerID=8YFLogxK
U2 - 10.1109/WI-IAT.2012.236
DO - 10.1109/WI-IAT.2012.236
M3 - Conference contribution
AN - SCOPUS:84878441996
SN - 9780769548807
T3 - Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
SP - 32
EP - 39
BT - Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
ER -