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

arXiv: https://arxiv.org/abs/2410.01842 · HTML: https://mlsanity.com/paper/2410.01842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 94 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [Arming the public with artificial intelligence to counter social bots](https://mlsanity.com/paper/1901.00912/) (2019)
- [The DARPA Twitter Bot Challenge](https://mlsanity.com/paper/1601.05140/) (2016)
- [Do altmetrics correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective](https://mlsanity.com/paper/1401.4321/) (2014)
- [Early Indicators of Scientific Impact: Predicting Citations with Altmetrics](https://mlsanity.com/paper/2012.13599/) (2020)
- [Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots](https://mlsanity.com/paper/1703.06027/) (2017)
- [Hateful People or Hateful Bots? Detection and Characterization of Bots Spreading Religious Hatred in Arabic Social Media](https://mlsanity.com/paper/1908.00153/) (2019)
- [Instagram Fake and Automated Account Detection](https://mlsanity.com/paper/1910.03090/) (2019)
- [Social Bots: Detection and Challenges](https://mlsanity.com/paper/2312.17423/) (2023)
- [Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter](https://mlsanity.com/paper/1410.4139/) (2014)
- [Differences in Personal and Professional Tweets of Scholars](https://mlsanity.com/paper/1505.01515/) (2015)
- [On Profiling Bots in Social Media](https://mlsanity.com/paper/1609.00543/) (2016)
- [Towards Automatic Bot Detection in Twitter for Health-related Tasks](https://mlsanity.com/paper/1909.13184/) (2019)
- [Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques](https://mlsanity.com/paper/2008.12471/) (2020)
- [Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS (altmetrics) and F1000Prime (paper tags)](https://mlsanity.com/paper/1409.2863/) (2014)
- ["Life never matters in the DEMOCRATS MIND": Examining Strategies of Retweeted Social Bots During a Mass Shooting Event](https://mlsanity.com/paper/1808.09325/) (2018)
- [The insoluble problems of books: What does Altmetric.com have to offer?](https://mlsanity.com/paper/1809.10128/) (2018)
- [YouTube and Science: Models for Research Impact](https://mlsanity.com/paper/2209.02380/) (2022)
- [Quantifying the Online Long-Term Interest in Research](https://mlsanity.com/paper/2209.06212/) (2022)
- [The Botization of Science? Large-scale study of the presence and impact of Twitter bots in science dissemination](https://mlsanity.com/paper/2310.12741/) (2023)
- [Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination](https://mlsanity.com/paper/2407.03190/) (2024)

## Abstract (arXiv)

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.