At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in-vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02 on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings on average, of posts by 5.15 and of comments by 37.4 . Compared to the control group, the probability of reaching a high rating (>=2000) for posts is increased by 24.6 when posts receive the positive treatment and for comments is decreased by 46.6 when comments receive the negative treatment.
On the practical side, one obvious case of online manipulation is spam, particularly a new type of spam called . Social spam is on the rise, with Nexgate Research reporting a tripling of social spam activity every six months @cite_29 and BusinessWeek magazine reporting that only 40
@cite_29
2128509431
The popularity of social bookmarking sites has made them prime targets for spammers. Many of these systems require an administrator'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 provides 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 detecting 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.