With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. 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 used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an extensive dataset with academic articles, several features from the article and a label indicating whether the article had excessive bot activity on Twitter or not. We analyzed the data to see the possibility of bot activity based on different characteristics of the article. We also trained machine-learning models using this dataset to identify possible bot activity in any given article.
Our machine-learning models were capable of identifying possible bot activity in any academic article with an accuracy of 0.70. We also found that articles related to “Health and Human Science” are more prone to bot activity compared to other research areas. Without arguing the maliciousness of the bot activity, our work presents a tool to identify the presence of bot activity in the dissemination of an academic article and creates a baseline for future research in this direction.
We considered the features available from the Altmetric dataset. It can be exciting research to extract additional features about the authors of the article, the location of the publication, international collaboration and other demographic features of the authors to see the relation of these features with bot activity.
Since public interest in scientific findings can shape the decisions of policymakers, it is essential to identify the possibility of bot activity in the dissemination of any given scholarly article. Without arguing whether the social bots are good or bad and without arguing about the validity of a scholarly article, our work proposes a tool to interpret the public interest in an article by identifying the possibility of bot activity toward an article. This work publishes the models and data generated through the study and provides a benchmark and guideline for future works in this direction.
While the majority of the existing research focuses on identifying and preventing bot activity on social media, our work is novel in predicting the possibility of bot activity in the dissemination of an academic article using Altmetric metadata for the article. Little work has been performed in this specific area, and the models developed from our research give policymakers and the public a tool to interpret and understand the public interest in a scientific publication with appropriate caution.
