Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Twitter accounts, we propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image; then, we leverage the strength of Convolutional Neural Networks to proceed with image classification. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature. The results confirm the effectiveness of the proposal, because the detection capability is on par with the state of the art, if not better in some cases.

翻译：在线社交网络彻底改变了我们消费和分享信息的方式，但也导致内容不一定总是可靠和准确的泛滥。一类特定的社交账户以推广不可信内容、极端党派和宣传性信息而闻名，它们是自动化账户，通常被称为机器人。聚焦于推特账户，我们提出了一种新的机器人检测方法：首先，我们提出了一种新算法，将账户执行的序列行为转换为图像；然后，我们利用卷积神经网络的优势进行图像分类。我们将性能与文献中已知的真实账户/机器人账户数据集上的最新机器人检测结果进行比较。结果证实了该方法的有效性，因为检测能力与现有技术相当，在某些情况下甚至更优。