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.
翻译：在网上社会网络(OSN)是我们生活的一部分的时期,检测机器人是一种必不可少的资产。在危机中,这一任务变得更加重要,因为Covid-19大流行是Covid-19大流行,那里开始有社会机器人扩散的风险,从而产生可能的错误信息来源。为了解决这一问题,比较了利用数据选择在Twitter上自动检测社会机器人的不同方法。用于开发机器人检测模型的技术包括利用Twitter元数据或Twitter账户的数字指纹等功能。此外,还利用最符合任务范围的机器检测技术分析了Covid-19大流行头几个月不同时期的推文中存在机器人。此外,这项工作还包括分析有关机器人和人类言论的各个方面,例如情绪或使用标签。
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