Summary: Social media bots are continuously evolving and becoming more ‘human-like’ in the way they talk and interact on online platforms. Previous research has focused on bot detection, but little attention has been devoted to the characterization and measurement of the behavior and activity of bots, compared to humans. In this study, researchers have revealed distinct behavioral differences between human and bot activity on social media which could be leveraged to improve bot detection strategies.
Bots are social media accounts which are controlled by artificial software rather than by humans and serve a variety of purposes from news aggregation to automated customer assistance for online retailers. However, bots have recently been under the spotlight as they are regularly employed as part of large-scale efforts on social media to manipulate public opinion, such as during electoral campaigns.
A new study in Frontiers in Physics has revealed the presence of short-term behavioral trends in humans that are absent in social media bots, providing an example of a ‘human signature’ on social media which could be leveraged to develop more sophisticated bot detection strategies. The research is the first study of its kind to apply user behavior over a social media session to the problem of bot detection.
“Remarkably, bots continuously improve to mimic more and more of the behavior humans typically exhibit on social media. Every time we identify a characteristic we think is prerogative of human behavior, such as sentiment of topics of interest, we soon discover that newly-developed open-source bots can now capture those aspects,” says co-author Emilio Ferrara, Assistant Professor of Computer Science and Research Team Leader at the University of Southern California Information Sciences Institute.
In this work, the researchers studied how the behavior of humans and bots changed over the course of an activity session using a large Twitter dataset associated with recent political events. Over the course of these sessions, the researchers measured various factors to capture user behavior, including the propensity to engage in social interactions and the amount of produced content, and then compared these results between bots and humans.
To study the behavior of bot and human users over an activity session, the researchers focused on indicators of the quantity and quality of social interactions a user engaged in, including the number of retweets, replies and mentions, as well as the length of the tweet itself. They then leveraged these behavioral results to inform a classification system for bot detection to observe whether the inclusion of features describing the session dynamics could improve the performance of the detector. a range of machine learning techniques were used to train two different sets of classifiers: one including the features describing the session dynamics and one with-out those features, as a baseline.
The researchers found, among hu
