Abstract
In recent years, substantial research efforts have gone into investigating different approaches to the detection of events in real time from the Twitter data stream. Most of these approaches, however, suffer from a high computational cost and are not evaluated using a publicly available corpus, thus making it difficult to properly compare them. In this paper, we propose a scalable event detection system, TwitterNews+, to detect and track newsworthy events in real time. TwitterNews+ uses a novel approach to cluster event related tweets from Twitter with a significantly lower computational cost compared to the existing state-of-theart approaches. Finally, we evaluate the effectiveness of TwitterNews+ using a publicly available corpus and its associated ground truth data set of newsworthy events. The result of the evaluation shows a significant improvement, in terms of recall and precision, over the baselines we have used.
| Language | English |
|---|---|
| Title of host publication | Social Informatics - 8th International Conference, SocInfo 2016, Proceedings |
| Editors | Emma Spiro, Yong-Yeol Ahn |
| Place of Publication | Cham |
| Publisher | Springer, Springer Nature |
| Pages | 224-239 |
| Number of pages | 16 |
| Volume | 10046 LNCS |
| ISBN (Print) | 9783319478791 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 8th International Conference on Social Informatics, SocInfo 2016 - Bellevue, United States Duration: 11 Nov 2016 → 14 Nov 2016 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10046 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 8th International Conference on Social Informatics, SocInfo 2016 |
|---|---|
| Country | United States |
| City | Bellevue |
| Period | 11/11/16 → 14/11/16 |
Fingerprint
Keywords
- Event detection
- Incremental clustering
- Microblog
- Social media
Cite this
}
TwitterNews+ : A framework for real time event detection from the twitter data stream. / Hasan, Mahmud; Orgun, Mehmet A.; Schwitter, Rolf.
Social Informatics - 8th International Conference, SocInfo 2016, Proceedings. ed. / Emma Spiro; Yong-Yeol Ahn. Vol. 10046 LNCS Cham : Springer, Springer Nature, 2016. p. 224-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10046 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › Research › peer-review
TY - GEN
T1 - TwitterNews+
T2 - A framework for real time event detection from the twitter data stream
AU - Hasan, Mahmud
AU - Orgun, Mehmet A.
AU - Schwitter, Rolf
PY - 2016
Y1 - 2016
N2 - In recent years, substantial research efforts have gone into investigating different approaches to the detection of events in real time from the Twitter data stream. Most of these approaches, however, suffer from a high computational cost and are not evaluated using a publicly available corpus, thus making it difficult to properly compare them. In this paper, we propose a scalable event detection system, TwitterNews+, to detect and track newsworthy events in real time. TwitterNews+ uses a novel approach to cluster event related tweets from Twitter with a significantly lower computational cost compared to the existing state-of-theart approaches. Finally, we evaluate the effectiveness of TwitterNews+ using a publicly available corpus and its associated ground truth data set of newsworthy events. The result of the evaluation shows a significant improvement, in terms of recall and precision, over the baselines we have used.
AB - In recent years, substantial research efforts have gone into investigating different approaches to the detection of events in real time from the Twitter data stream. Most of these approaches, however, suffer from a high computational cost and are not evaluated using a publicly available corpus, thus making it difficult to properly compare them. In this paper, we propose a scalable event detection system, TwitterNews+, to detect and track newsworthy events in real time. TwitterNews+ uses a novel approach to cluster event related tweets from Twitter with a significantly lower computational cost compared to the existing state-of-theart approaches. Finally, we evaluate the effectiveness of TwitterNews+ using a publicly available corpus and its associated ground truth data set of newsworthy events. The result of the evaluation shows a significant improvement, in terms of recall and precision, over the baselines we have used.
KW - Event detection
KW - Incremental clustering
KW - Microblog
KW - Social media
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84995489793&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-47880-7_14
DO - 10.1007/978-3-319-47880-7_14
M3 - Conference proceeding contribution
SN - 9783319478791
VL - 10046 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 224
EP - 239
BT - Social Informatics - 8th International Conference, SocInfo 2016, Proceedings
A2 - Spiro, Emma
A2 - Ahn, Yong-Yeol
PB - Springer, Springer Nature
CY - Cham
ER -