Telegram, initially a messaging app, has evolved into a platform where users can interact with various services through programmable applications, bots. Bots provide a wide range of uses, from moderating groups, helping with online shopping, to even executing trades in financial markets. However, Telegram has been increasingly associated with various illicit activities -- financial scams, stolen data, non-consensual image sharing, among others, raising concerns bots may be facilitating these operations. This paper is the first to characterize Telegram bots at scale, through the following contributions. First, we offer the largest general-purpose message dataset and the first bot dataset. Through snowball sampling from two published datasets, we uncover over 67,000 additional channels, 492 million messages, and 32,000 bots. Second, we develop a system to automatically interact with bots in order to extract their functionality. Third, based on their description, chat responses, and the associated channels, we classify bots into several domains. Fourth, we investigate the communities each bot serves, by analyzing supported languages, usage patterns (e.g., duration, reuse), and network topology. While our analysis discovers useful applications such as crowdsourcing, we also identify malicious bots (e.g., used for financial scams, illicit underground services) serving as payment gateways, referral systems, and malicious AI endpoints. By exhorting the research community to look at bots as software infrastructure, this work hopes to foster further research useful to content moderators, and to help interventions against illicit activities.

翻译：Telegram最初作为即时通讯应用，现已发展为用户可通过可编程应用（即机器人）与各类服务交互的平台。机器人提供从群组管理、网购协助，到金融市场交易执行等广泛功能。然而，Telegram日益与各类非法活动相关联——金融诈骗、数据窃取、未经同意的图像分享等，引发人们对机器人可能助长这些操作的担忧。本文首次从规模化角度刻画Telegram机器人特征，具体贡献如下：第一，我们提供规模最大的通用消息数据集及首个机器人数据集。通过从两个公开数据集进行滚雪球采样，发现超过67,000个新增频道、4.92亿条消息及32,000个机器人。第二，我们开发了自动与机器人交互以提取其功能的系统。第三，基于机器人描述、对话响应及关联频道，将其归类至多个领域。第四，通过分析支持语言、使用模式（如时长、复用）及网络拓扑，探究各机器人服务的社区。尽管分析发现了众包等实用应用，我们也识别出恶意机器人（如用于金融诈骗、非法地下服务），这些机器人充当支付网关、推荐系统及恶意AI端点。本文呼吁研究界将机器人视为软件基础设施，期望推动对内容审核者有益的研究，并助力针对非法活动的干预措施。