11. Cambridge Analytica, Facebook, and Understanding Social Media Beyond the Screen

Sujon, Zoetanya

doi:10.11647/obp.0162.11

Cited by 2 publications

(1 citation statement)

References 81 publications

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“…Bots have different names for diverse purposes, including crawlers, chatbots, social bots, sock puppets and cyborgs (Gorwa and Guilbeault, 2020). They are used by states or state-sponsored agencies for network and political reasons, as well as for financial and marketing purposes (Sujon, 2019). Social bots on Twitter are completely automated, such as automatic retweeting, commenting and tweeting, which are of particular interest here.…”

Section: Related Workmentioning

confidence: 99%

Research on the generalization of social bot detection from two dimensions: feature extraction and detection approaches

Zeng

Sun

et al. 2022

DTA

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PurposeThe proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.Design/methodology/approachThis paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.FindingsThe experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.Originality/valueBased on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.

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Section: Related Workmentioning

confidence: 99%