Tracking China’s Cross-Strait Bot Networks Against Taiwan

Bots have been in the spotlight for many social media studies, for they have been observed to be participating in the manipulation of information and opinions on social media. These studies analyzed the activity and influence of bots in a variety of contexts: elections, protests, health communication and so forth. Prior to this analyzes is the identification of bot accounts to segregate the class of social media users. In this work, we propose an ensemble method for bot detection, designing a multi-platform bot detection architecture to handle several problems along the bot detection pipeline: incomplete data input, minimal feature engineering, optimized classifiers for each data field, and also eliminate the need for a threshold value for classification determination. With these design decisions, we generalize our bot detection framework across Twitter, Reddit and Instagram. We also perform feature importance analysis, observing that the entropy of names and number of interactions (retweets/shares) are important factors in bot determination. Finally, we apply our multi-platform bot detector to the US 2020 presidential elections to identify and analyze bot activity across multiple social media platforms, showcasing the difference in online discourse of bots from different platforms.

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Integrating higher-order relations for enhanced twitter bot detection

Reiche,

Cohen,

Simonov

et al. 2024

Soc. Netw. Anal. Min.

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Bot detection is a critical task in preserving the integrity of social networks and mitigating online disinformation. Despite advances in graph-based methods for detecting bots, these models often rely on follow relations, assuming that users sharing similar characteristics are more likely to connect. However, these methods often fail to address complex behavioral patterns indicative of coordinated bot activities, allowing bot developers to avoid detection more easily. We investigate the potential of integrating behavioral and higher-order relations, focusing on Retweet, Co-Retweet (where two users retweet the same tweet) and Co-Hashtag (where two users frequently use the same hashtag), and compare these against conventional follower and following relations. We further explore the process of relation creation, particularly highlighting the Co-Hashtag relation’s robustness against data collection flaws and its ability to mitigate shortcomings in the collection of datasets. Our experiments not only contribute to the ongoing efforts to address challenges in bot detection, but also open up new avenues for exploring how complex network patterns can be employed for social network analysis.

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Tracking China’s Cross-Strait Bot Networks Against Taiwan

Cited by 3 publications

References 11 publications

Tiny-BotBuster: Identifying Automated Political Coordination in Digital Campaigns

Tiny-BotBuster: Identifying Automated Political Coordination in Digital Campaigns

Assembling a multi-platform ensemble social bot detector with applications to US 2020 elections

Integrating higher-order relations for enhanced twitter bot detection

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