Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
7-2012
Abstract
Twitter has enjoyed tremendous popularity in the recent years. To help categorizing and search tweets, Twitter users assign hashtags to their tweets. Given that hashtag assignment is the primary way to semantically categorizing and search tweets, it is highly susceptible to abuse by spammers and other anomalous users [1]. Popular hashtags such as #Obama and #ladygaga could be hijacked by having them added to unrelated tweets with the intent of misleading many other users or promoting specific agenda to the users. The users performing this act are known as the hashtag hijackers. As the hijackers usually abuse common sets of hashtags, they demonstrate common extreme group behaviors which can be used for detection.
Discipline
Computer Sciences | Databases and Information Systems | Social Media
Publication
Proceedings of the ACM International Conference on Net Science (NetSci)
First Page
1
Last Page
2
City or Country
Chicago, Illinois
Citation
DAI, Hanbo; Ee-peng LIM; ZHU, Feida; and Hwee Hwa PANG.
Detecting Anomalous Twitter Users by Extreme Group Behaviors. (2012). Proceedings of the ACM International Conference on Net Science (NetSci). 1-2.
Available at: https://ink.library.smu.edu.sg/sis_research/2909
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.