Abstract

The explosive use of social media, in information dissemination and communication, has also made it a popular platform for the spread of rumors. Rumors could be easily propagated and received by a large number of users in social media, resulting in catastrophic effects in the physical world in a very short period. It is a challenging task, if not impossible, to apply classical supervised learning methods to the early detection of rumors, since the labeling process is time-consuming and labor-intensive. Motivated by the fact that abundant label information of historical rumors is publicly available, in this paper, we propose to investigate whether knowledge learned from historical data could potentially help identify newly emerging rumors. In particular, since a disputed factual claim arouses certain reactions such as curiosity, skepticism, and astonishment, we identify and utilize patterns from prior labeled data to help reveal emergent rumors. Experimental results on real-world data sets demonstrate the effectiveness. Further experiments are conducted to show how much earlier it can detect an emerging rumor than traditional approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
PublisherSociety for Industrial and Applied Mathematics Publications
Pages99-107
Number of pages9
ISBN (Electronic)9781611974874
StatePublished - 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
CountryUnited States
CityHouston
Period4/27/174/29/17

Fingerprint

Information dissemination
Supervised learning
Labeling
Labels
Personnel
Communication
Experiments

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

Cite this

Wu, L., Li, J., Hu, X., & Liu, H. (2017). Gleaning wisdom from the past: Early detection of emerging rumors in social media. In Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (pp. 99-107). Society for Industrial and Applied Mathematics Publications.

Gleaning wisdom from the past : Early detection of emerging rumors in social media. / Wu, Liang; Li, Jundong; Hu, Xia; Liu, Huan.

Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications, 2017. p. 99-107.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, L, Li, J, Hu, X & Liu, H 2017, Gleaning wisdom from the past: Early detection of emerging rumors in social media. in Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications, pp. 99-107, 17th SIAM International Conference on Data Mining, SDM 2017, Houston, United States, 4/27/17.
Wu L, Li J, Hu X, Liu H. Gleaning wisdom from the past: Early detection of emerging rumors in social media. In Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications. 2017. p. 99-107
Wu, Liang ; Li, Jundong ; Hu, Xia ; Liu, Huan. / Gleaning wisdom from the past : Early detection of emerging rumors in social media. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications, 2017. pp. 99-107
@inproceedings{a9afc4c755294b219eace61b10bd1aec,
title = "Gleaning wisdom from the past: Early detection of emerging rumors in social media",
abstract = "The explosive use of social media, in information dissemination and communication, has also made it a popular platform for the spread of rumors. Rumors could be easily propagated and received by a large number of users in social media, resulting in catastrophic effects in the physical world in a very short period. It is a challenging task, if not impossible, to apply classical supervised learning methods to the early detection of rumors, since the labeling process is time-consuming and labor-intensive. Motivated by the fact that abundant label information of historical rumors is publicly available, in this paper, we propose to investigate whether knowledge learned from historical data could potentially help identify newly emerging rumors. In particular, since a disputed factual claim arouses certain reactions such as curiosity, skepticism, and astonishment, we identify and utilize patterns from prior labeled data to help reveal emergent rumors. Experimental results on real-world data sets demonstrate the effectiveness. Further experiments are conducted to show how much earlier it can detect an emerging rumor than traditional approaches.",
author = "Liang Wu and Jundong Li and Xia Hu and Huan Liu",
year = "2017",
language = "English (US)",
pages = "99--107",
booktitle = "Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017",
publisher = "Society for Industrial and Applied Mathematics Publications",
address = "United States",

}

TY - GEN

T1 - Gleaning wisdom from the past

T2 - Early detection of emerging rumors in social media

AU - Wu, Liang

AU - Li, Jundong

AU - Hu, Xia

AU - Liu, Huan

PY - 2017

Y1 - 2017

N2 - The explosive use of social media, in information dissemination and communication, has also made it a popular platform for the spread of rumors. Rumors could be easily propagated and received by a large number of users in social media, resulting in catastrophic effects in the physical world in a very short period. It is a challenging task, if not impossible, to apply classical supervised learning methods to the early detection of rumors, since the labeling process is time-consuming and labor-intensive. Motivated by the fact that abundant label information of historical rumors is publicly available, in this paper, we propose to investigate whether knowledge learned from historical data could potentially help identify newly emerging rumors. In particular, since a disputed factual claim arouses certain reactions such as curiosity, skepticism, and astonishment, we identify and utilize patterns from prior labeled data to help reveal emergent rumors. Experimental results on real-world data sets demonstrate the effectiveness. Further experiments are conducted to show how much earlier it can detect an emerging rumor than traditional approaches.

AB - The explosive use of social media, in information dissemination and communication, has also made it a popular platform for the spread of rumors. Rumors could be easily propagated and received by a large number of users in social media, resulting in catastrophic effects in the physical world in a very short period. It is a challenging task, if not impossible, to apply classical supervised learning methods to the early detection of rumors, since the labeling process is time-consuming and labor-intensive. Motivated by the fact that abundant label information of historical rumors is publicly available, in this paper, we propose to investigate whether knowledge learned from historical data could potentially help identify newly emerging rumors. In particular, since a disputed factual claim arouses certain reactions such as curiosity, skepticism, and astonishment, we identify and utilize patterns from prior labeled data to help reveal emergent rumors. Experimental results on real-world data sets demonstrate the effectiveness. Further experiments are conducted to show how much earlier it can detect an emerging rumor than traditional approaches.

UR - http://www.scopus.com/inward/record.url?scp=85027847537&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027847537&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85027847537

SP - 99

EP - 107

BT - Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017

PB - Society for Industrial and Applied Mathematics Publications

ER -