Detecting Anomalous Twitter Users by Extreme Group Behaviors

• Main Authors: DAI, Hanbo, Ee-peng LIM, ZHU, Feida, Hwee Hwa PANG
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Computer Sciences; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/2909; https://ink.library.smu.edu.sg/context/sis_research/article/3909/viewcontent/C16___Detecting_Anomalous_Twitter_Users_by_Extreme_Group_Behaviors__NETSCI2012_.pdf
• Institution: Singapore Management University

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.