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

@inproceedings{bian_2008_votes, abstract = {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 learning based ranking framework for social media that integrates user interactions and content relevance, and demonstrate its effec- tiveness for answer retrieval in a popular community question answering portal. We consider several vote spam attacks, and introduce a method of training our ranker to increase its robustness to some common forms of vote spam attacks. The results of our large-scale experimental evaluation show that our ranker is signifcicantly more robust to vote spam compared to a state-of-the-art baseline as well as the ranker not explicitly trained to handle malicious interactions.}, address = {New York, NY, USA}, author = {Bian, Jiang and Liu, Yandong and Agichtein, Eugene and Zha, Hongyuan}, booktitle = {AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web}, citeulike-article-id = {3875263}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1451983.1451997}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1451983.1451997}, doi = {10.1145/1451983.1451997}, isbn = {978-1-60558-159-0}, keywords = {adversarial-ir, tagging}, location = {Beijing, China}, pages = {53--60}, posted-at = {2009-01-12 09:41:23}, priority = {2}, publisher = {ACM}, title = {A few bad votes too many?: towards robust ranking in social media}, url = {http://dx.doi.org/10.1145/1451983.1451997}, year = {2008} }

TY - CONF ID - bian_2008_votes L3 - citeulike-article-id:3875263 N2 - 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 learning based ranking framework for social media that integrates user interactions and content relevance, and demonstrate its effec- tiveness for answer retrieval in a popular community question answering portal. We consider several vote spam attacks, and introduce a method of training our ranker to increase its robustness to some common forms of vote spam attacks. The results of our large-scale experimental evaluation show that our ranker is signifcicantly more robust to vote spam compared to a state-of-the-art baseline as well as the ranker not explicitly trained to handle malicious interactions. CY - Beijing, China SN - 978-1-60558-159-0 AD - New York, NY, USA EP - 60 TI - A few bad votes too many?: towards robust ranking in social media PB - ACM T2 - AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web SP - 53 KW - adversarial-ir KW - tagging AU - Bian, Jiang AU - Liu, Yandong AU - Agichtein, Eugene AU - Zha, Hongyuan PY - 2008/// UR - http://dx.doi.org/10.1145/1451983.1451997 ER -