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,但也导致了不一定可靠和准确的内容大量泛滥。一种特定类型的社交账号能够宣传不可靠的内容、极端党派和宣传性的信息,即常被称为机器人的自动化账号。我们提出了一种新颖的检测机器人的方法,该方法首先提出了一个新算法,将账号执行的行动序列转换为图像,然后利用卷积神经网络的力量进行图像分类。我们将我们的表现与在文献中已知的真实账户/机器人账户数据集中的最新结果进行了比较。结果确认了这一提议的有效性,因为检测能力达到了与当前技术水平相当甚至更好的水平。