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最初作为一款即时通讯应用，已演变为用户可通过可编程应用（即机器人）与多种服务交互的平台。机器人功能涵盖群组管理、在线购物协助，乃至金融市场交易执行。然而，Telegram日益与金融诈骗、数据泄露、非自愿图像传播等违法行为关联，引发机器人可能助长这些活动的担忧。本文首次通过以下贡献实现Telegram机器人的规模化特征分析：其一，提供迄今最大规模的通用消息数据集及首个机器人数据集。通过对两个已发布数据集进行滚雪球采样，我们发现了超过6.7万个新增频道、4.92亿条消息及3.2万个机器人。其二，构建自动与机器人交互的系统以提取其功能。其三，基于描述文本、聊天响应及关联频道，将机器人划分为多个领域。其四，通过分析支持语言、使用模式（如持续时间、复用性）及网络拓扑，探究各机器人服务的社群特征。分析中虽发现众包等实用应用，亦识别出充当支付网关、推荐系统及恶意AI端点的恶意机器人（如用于金融诈骗、地下非法服务）。本工作呼吁学术界将机器人视为软件基础设施，以期促进对内容审核的进一步研究，并助力打击违法行为的干预措施。