A comparative study of Bot Detection techniques methods with an application related to Covid-19 discourse on Twitter
This work addresses the problem of misinformation from social bots during crises like Covid-19 for social media users and researchers, but it is incremental as it applies existing methods to new data.
The study compared different bot detection methods on Twitter, focusing on the Covid-19 pandemic, and found that using features like tweets metadata and digital fingerprint helped identify bots, with analysis revealing differences in discourse aspects such as sentiment and hashtag usage.
Bot Detection is an essential asset in a period where Online Social Networks(OSN) is a part of our lives. This task becomes more relevant in crises, as the Covid-19 pandemic, where there is an incipient risk of proliferation of social bots, producing a possible source of misinformation. In order to address this issue, it has been compared different methods to detect automatically social bots on Twitter using Data Selection. The techniques utilized to elaborate the bot detection models include the utilization of features as the tweets metadata or the Digital Fingerprint of the Twitter accounts. In addition, it was analyzed the presence of bots in tweets from different periods of the first months of the Covid-19 pandemic, using the bot detection technique which best fits the scope of the task. Moreover, this work includes also analysis over aspects regarding the discourse of bots and humans, such as sentiment or hashtag utilization.