论文信息 - Social spam, campaigns, misinformation and crowdturfing

Social spam, campaigns, misinformation and crowdturfing

This tutorial will introduce peer-reviewed research work on information quality on social systems. Specifically, we will address new threats such as social spam, campaigns, misinformation and crowdturfing, and overview modern techniques to improve information quality by revealing and detecting malicious participants (e.g., social spammers, content polluters and crowdturfers) and low quality contents.

Calton Pu | Kyumin Lee | James Caverlee

[1] Chao Wu,et al. Information Credibility on Twitter in Emergency Situation , 2012, PAISI.

[2] Arjun Mukherjee,et al. Spotting fake reviewer groups in consumer reviews , 2012, WWW.

[3] Kyumin Lee,et al. Uncovering social spammers: social honeypots + machine learning , 2010, SIGIR.

[4] Gang Wang,et al. Social Turing Tests: Crowdsourcing Sybil Detection , 2012, NDSS.

[5] Jacob Ratkiewicz,et al. Detecting and Tracking Political Abuse in Social Media , 2011, ICWSM.

[6] Gang Wang,et al. Serf and turf: crowdturfing for fun and profit , 2011, WWW.

[7] Vern Paxson,et al. @spam: the underground on 140 characters or less , 2010, CCS '10.

[8] Kyumin Lee,et al. Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter , 2011, ICWSM.

[9] Kyumin Lee,et al. The Dark Side of Micro-Task Marketplaces: Characterizing Fiverr and Automatically Detecting Crowdturfing , 2014, ICWSM.

[10] Songqing Chen,et al. UNIK: unsupervised social network spam detection , 2013, CIKM.

[11] Jun Hu,et al. Detecting and characterizing social spam campaigns , 2010, IMC '10.

[12] Anupam Joshi,et al. Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy , 2013, WWW.

[13] Jussara M. Almeida,et al. Detection of spam tipping behaviour on foursquare , 2013, WWW.