AI 中文总结
本研究利用Botometer查询日志分析公众对Twitter机器人的怀疑行为，发现怀疑在平台危机时激增，且被怀疑账户具有更高机器人分数和更易被暂停，表明公众怀疑构成分布式平台审计。
AI 中文摘要
我们从需求侧研究私下的机器人检查行为：人们何时怀疑某个账户是自动化的，他们怀疑谁，以及随后会发生什么。利用最广泛使用的机器人检测服务Botometer的服务器端查询日志，我们将每次查询视为一条怀疑的行为痕迹。我们分析了2020年至2023年间对Twitter账户进行的超过100万次公开检查，并辅以同期1%公开流中的32亿条推文。集体怀疑在平台危机期间激增，最显著的是在2022年马斯克与Twitter的机器人争议期间。被检查的账户更老、更活跃，拥有更多关注者，并发布推广、政治和加密货币内容。引起集体怀疑的账户具有更高的机器人分数，并且更有可能被暂停。与机器人相关的公众关注和Botometer活动在同一广泛时期处于高位，尽管它们的短期波动在很大程度上是不相关的。公众反馈侧重于对人类的以第一人称身份声明，以及对机器人和半机器人基于证据的论证，这些论证引用发帖频率、政治内容和跨账户协调。因此，机器人怀疑构成了一种大规模、分布式的平台审计形式，它追踪自动化的有意义的信号，将审计工具的查询日志确立为观察公众对平台操纵反应的新视角。
英文摘要
We study private bot-checking behavior from the demand side: when people suspect an account is automated, whom they suspect, and what follows. Using Botometer's server-side query logs, the most widely used bot-detection service, we treat each query as a behavioural trace of suspicion. We analyze over 1 million public checks of Twitter accounts from 2020 to 2023, enriched with 3.2 billion tweets from the contemporaneous 1% public stream. Collective suspicion spikes with platform crises, most sharply around the 2022 Musk-Twitter bot dispute. Checked accounts are older and more prolific, have more followers, and post promotional, political, and crypto content. Accounts that draw collective suspicion have higher bot scores and are more likely to be suspended. Bot-related public attention and Botometer activity are elevated during the same broad periods, although their short-run fluctuations are largely uncoupled. Public feedback focuses on first-person identity claims for humans, and evidence-based arguments citing posting rate, political content, and cross-account coordination for bots and cyborgs. Bot suspicion thus constitutes a mass, distributed form of platform auditing that tracks meaningful signals of automation, establishing audit-tool query logs as a novel lens on public responses to platform manipulation.
