Augmenting Social Bot Detection with Crowd-Generated Labels
研究利用社交媒体用户对社交机器人活动的识别能力，通过众包标签增强传统检测系统，并设计基于言语行为理论的方法评估标签可信度。
Social media platforms are facing increasing numbers of cyber-adversaries seeking to manipulate online discourse by using social bots to help automate and scale their attacks. Likewise, some social media users have developed capabilities to identify social bot activity at varying degrees of confidence. We exploit this user intelligence to augment traditional bot detection systems. Furthermore, not all crowd-generated labels are of equal value or credibility. Some individuals are quite adept at identifying social bot activity, whereas others may become merely suspicious but remain uncertain. We design a system inspired by speech act theory to evaluate which crowd-generated labels are most credible for augmenting bot detection system efficacy.