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

TL;DR

This study investigates how bots may artificially amplify scientific articles on Twitter, developing machine learning tools to detect such activity and revealing that health-related research is more susceptible to bot influence.

Contribution

The paper introduces a machine learning approach to identify bot activity in the dissemination of scientific articles on Twitter, providing a baseline for future research.

Findings

Machine learning models achieved 70% accuracy in detecting bot activity.

Articles on health and human sciences are more prone to bot amplification.

The study offers a tool to monitor and analyze social media influence on scientific communication.

Abstract

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.