Increased usage of bots through the Internet in general, and social networks in particular, has many implications related to influencing public opinion. Mechanisms to distinguish humans from machines span a broad spectrum of applications and hence vary in their nature and complexity. Here we use several public Twitter datasets to build a model that can predict whether or not an account is a bot account based on features extracted at the tweet or the account level. We then apply the model to Twitter's Russian Troll Tweets dataset. At the account level, we evaluate features related to how often Twitter accounts are tweeting, as previous research has shown that bots are very active at some account levels and very low at others. At the tweet level, we noticed that bot accounts tend to sound more formal or structured, whereas real user accounts tend to be more informal in that they contain more slang, slurs, cursing, and the like. We also noted that bots can be created for a range of different goals (e.g., marketing and politics) and that their behaviors vary based on those distinct goals. Ultimately, for high bot-prediction accuracy, models should consider and distinguish among the different goals for which bots are created.

通常，通过互联网（尤其是通过社交网络）对机器人的使用不断增加，对影响公众舆论具有许多影响。区分人与机器的机制涉及广泛的应用，因此其性质和复杂性各不相同。在这里，我们使用几个公开的Twitter数据集来构建一个模型，该模型可以根据在tweet或帐户级别提取的功能来预测帐户是否为机器人帐户。然后，我们将该模型应用于Twitter的Russian Troll Tweets数据集。在帐户级别，我们评估与Twitter帐户发推频率有关的功能，因为以前的研究表明，机器人在某些帐户级别上非常活跃，而在其他帐户级别上非常低。在推文级别，我们注意到机器人帐户听起来听起来更正式或更结构化，而真实的用户帐户往往更非正式，因为它们包含更多的语，诽谤，诅咒等。我们还注意到，可以为一系列不同的目标（例如，营销和政治）创建机器人，并且其行为会根据这些不同的目标而有所不同。最终，为了获得高的机器人预测准确性，模型应该考虑并区分为机器人创建的不同目标。