  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.
