测量机器人和人类行为动力学
Measuring bot and human behavioral dynamics
摘要：
机器人，由软件而不是人类控制的社交媒体账户，最近因其与各种形式的在线操纵的联系而受到关注。迄今为止，许多工作都集中在社交机器人检测上，但很少关注机器人行为和活动的表征和测量，而不是人类的。多年来，机器人变得越来越复杂，能够反映一些短期行为，模仿人类用户的行为。本文的目标是研究机器人在一个活动会话过程中表现出的行为动力学，并强调这些行为是否以及如何与人类活动特征不同。通过使用与最近政治事件相关的大型Twitter数据集，我们首先将机器人和人类分开，然后隔离他们的活动会话。我们编制了一个要测量的数量列表，例如用户参与社交互动或制作内容的倾向。我们的分析强调了人类短期行为趋势的存在，这可能与认知起源有关，而机器人由于其自动化活动而直观地不存在。这些研究结果最终被编码以创建和评估一个机器学习算法来检测机器人和人类产生的活动会话，以允许更细致入微的机器人检测策略。
英文摘要：
Bots, social media accounts controlled by software rather than by humans, have recently been under the spotlight for their association with various forms of online manipulation. To date, much work has focused on social bot detection, but little attention has been devoted to the characterization and measurement of the behavior and activity of bots, as opposed to humans'. Over the course of the years, bots have become more sophisticated, and capable to reflect some short-term behavior, emulating that of human users. The goal of this paper is to study the behavioral dynamics that bots exhibit over the course of one activity session, and highlight if and how these differ from human activity signatures. By using a large Twitter dataset associated with recent political events, we first separate bots and humans, then isolate their activity sessions. We compile a list of quantities to be measured, like the propensity of users to engage in social interactions or to produce content. Our analysis highlights the presence of short-term behavioral trends in humans, which can be associated with a cognitive origin, that are absent in bots, intuitively due to their automated activity. These findings are finally codified to create and evaluate a machine learning algorithm to detect activity sessions produced by bots and humans, to allow for more nuanced bot detection strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
