Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2019 SIAM International Conference on Data Mining (SDM) |
| Editors | Tanya Berger-Wolf, Nitesh Chawla |
| Publisher | Society for Industrial and Applied Mathematics Publications |
| Pages | 226-234 |
| Number of pages | 9 |
| ISBN (electronic) | 9781611975673 |
| Publication status | Published - 6 May 2019 |
| Event | 19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada Duration: 2 May 2019 → 4 May 2019 |
Publication series
| Name | Proceedings of the SIAM International Conference on Data Mining |
|---|---|
| ISSN (electronic) | 2167-0099 |
Abstract
Twitter has been heavily used for users to report and share information about real-world events. However, understanding the multiple aspects of an event as it happens is a very challenging task due to the prevalent noise and redundant in tweets as well as the evolution of the event. In this paper, we present a graph-based method for summarizing evolutionary events from tweet streams. Unlike existing approaches that either require prior information, result in less readable summaries, or are not scalable, our proposed method can automatically extract sets of representative tweets as concise summaries for the events. Moreover, the method also allows the summaries to be updated efficiently using an incremental procedure, thus can scale up to large data streams. The experiments on five datasets reveal that our proposed method significantly outperforms several baselines.
ASJC Scopus subject areas
- Computer Science(all)
- Software
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Proceedings of the 2019 SIAM International Conference on Data Mining (SDM). ed. / Tanya Berger-Wolf; Nitesh Chawla. Society for Industrial and Applied Mathematics Publications, 2019. p. 226-234 (Proceedings of the SIAM International Conference on Data Mining).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient Summarizing of Evolving Events from Twitter Streams
AU - Nguyen, Thi Huyen
AU - Hoang, Tuan Anh
AU - Nejdl, Wolfgang
N1 - Funding information: This research is supported by the ERC Grant (339233) ALEXANDRIA. This research is supported by the ERC Grant (339233) ALEXANDRIA. This is also supported by the DFG Grant (NI-1760/1-1) Managed Forgetting.
PY - 2019/5/6
Y1 - 2019/5/6
N2 - Twitter has been heavily used for users to report and share information about real-world events. However, understanding the multiple aspects of an event as it happens is a very challenging task due to the prevalent noise and redundant in tweets as well as the evolution of the event. In this paper, we present a graph-based method for summarizing evolutionary events from tweet streams. Unlike existing approaches that either require prior information, result in less readable summaries, or are not scalable, our proposed method can automatically extract sets of representative tweets as concise summaries for the events. Moreover, the method also allows the summaries to be updated efficiently using an incremental procedure, thus can scale up to large data streams. The experiments on five datasets reveal that our proposed method significantly outperforms several baselines.
AB - Twitter has been heavily used for users to report and share information about real-world events. However, understanding the multiple aspects of an event as it happens is a very challenging task due to the prevalent noise and redundant in tweets as well as the evolution of the event. In this paper, we present a graph-based method for summarizing evolutionary events from tweet streams. Unlike existing approaches that either require prior information, result in less readable summaries, or are not scalable, our proposed method can automatically extract sets of representative tweets as concise summaries for the events. Moreover, the method also allows the summaries to be updated efficiently using an incremental procedure, thus can scale up to large data streams. The experiments on five datasets reveal that our proposed method significantly outperforms several baselines.
UR - http://www.scopus.com/inward/record.url?scp=85066068876&partnerID=8YFLogxK
U2 - 10.1137/1.9781611975673.26
DO - 10.1137/1.9781611975673.26
M3 - Conference contribution
AN - SCOPUS:85066068876
T3 - Proceedings of the SIAM International Conference on Data Mining
SP - 226
EP - 234
BT - Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)
A2 - Berger-Wolf, Tanya
A2 - Chawla, Nitesh
PB - Society for Industrial and Applied Mathematics Publications
T2 - 19th SIAM International Conference on Data Mining, SDM 2019
Y2 - 2 May 2019 through 4 May 2019
ER -