Name
Abstract | ||
|---|---|---|
The popularity of social bookmarking sites has made them prime targets for spammers. Many of these systems require an adminis- trator's time and energy to manually filter or remove spam. Here we discuss the motivations of social spam, and present a study of automatic detection of spammers in a social tagging system. We identify and analyze six distinct features that address various properties of social spam, finding that each of these features pro- vides for a helpful signal to discriminate spammers from legitimate users. These features are then used in various machine learning algorithms for classification, achieving over 98% accuracy in de- tecting social spammers with 2% false positives. These promising results provide a new baseline for future efforts on social spam. We make our dataset publicly available to the research community. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1531914.1531924 | Adversarial Information Retrieval on the Web |
Keywords | Field | DocType |
automatic detection,post,social spam,tag,resource,distinct feature,social tagging system,social spam detection,web 2.0,tag similarity,discriminate spammers,various property,social bookmarking site,various machine,false positive,social spammers,annotations,web 2 0,machine learning | Social spam,Information retrieval,Computer science,Popularity,Spambot,Web 2.0,Forum spam,Bookmarking,Tag system,False positive paradox | Conference |
Citations | PageRank | References |
41 | 1.87 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Markines | 1 | 502 | 22.08 |
| Ciro Cattuto | 2 | 1740 | 97.27 |
| Filippo Menczer | 3 | 3874 | 268.67 |