A few bad votes too many?: towards robust ranking in social media

Online social media draws heavily on active reader participation, such as voting or rating of news stories, articles, or responses to a question. This user feedback is invaluable for ranking, filtering, and retrieving high quality content - tasks that are crucial with the explosive amount of social content on the web. Unfortunately, as social media moves into the mainstream and gains in popularity, the quality of the user feedback degrades. Some of this is due to noise, but, increasingly, a small fraction of malicious users are trying to "game the system" by selectively promoting or demoting content for profit, or fun. Hence, an effective ranking of social media content must be robust to noise in the user interactions, and in particular to vote spam. We describe a machine learningbased ranking framework for social media that integrates user interactions and content relevance, and demonstrate its effectiveness for answer retrieval in a popular community question answering por...

• Type: Conference
• Year: 2008
• Where: AIRWEB
• Authors: Jiang Bian, Yandong Liu, Eugene Agichtein, Hongyuan Zha
• Keywords: AIRWEB 2008, Internet Technology, Social Media, Vote Spam, Vote Spam Attacks