Public interest in science or bots? Selective amplification of scientific articles on Twitter

What is it about?

With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. In this paper, we analyzed how Twitter bots interacted with scholarly articles on the platform and determined whether bots are disseminating a given scholarly article by analyzing the relationship between Twitter bots and several research factors. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world, this topic warrants critical study and attention. We developed and tested several supervised machine-learning classification models to tackle this problem. Our analysis also identified that scholarly articles in health and human science are more prone to bot activity than other research areas.

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