通过全球数据对比，揭示机器人与人类在社交平台上的行为差异。
What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics
- 基于2亿用户7个事件的分析，定义社交机器人本质特征。
- 机器人用可自动化语言，人类依赖理解性对话；机器人呈星型结构，人类为层级结构。
- 适合安全研究、政策制定者及算法监管者参考，推动对智能机器人的治理。
社交媒体对话中20%为机器人，80%为人类。机器人与人类在语言使用上存在系统性差异：机器人倾向于使用易于自动化的语言线索，而人类则依赖需要对话理解的线索。机器人所用词汇与其伪装身份一致，而人类发送的内容可能与身份无关。通信结构方面，抽样的机器人呈现星型互动模式，人类则表现为层级结构。这些结论基于对全球7个事件下约2亿用户的大型社交媒体推文分析。社交机器人自被网络安全研究人员发现以来便引发关注，因其能传播虚假信息并操纵舆论。多数研究依赖特定事件的定义，本文从“什么是机器人”出发，提出基于原理的定义，并系统比较跨全球事件中机器人与人类的特征差异。研究表明，软件编程的机器人是人工智能算法，随技术发展具有演化潜力。据此提出使用与监管建议，并讨论未来方向：检测——系统识别潜在进化的机器人；区分——评估机器人内容与互动质量；干扰——减轻恶意机器人的影响。
原文摘要 · Abstract (English)
Chatter on social media is 20% bots and 80% humans. Chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated while humans use cues that require dialogue understanding. Bots use words that match the identities they choose to present, while humans may send messages that are not related to the identities they present. Bots and humans differ in their communication structure: sampled bots have a star interaction structure, while sampled humans have a hierarchical structure. These conclusions are based on a large-scale analysis of social media tweets across ~200mil users across 7 events. Social media bots took the world by storm when social-cybersecurity researchers realized that social media users not only consisted of humans but also of artificial agents called bots. These bots wreck havoc online by spreading disinformation and manipulating narratives. Most research on bots are based on special-purposed definitions, mostly predicated on the event studied. This article first begins by asking, "What is a bot?", and we study the underlying principles of how bots are different from humans. We develop a first-principle definition of a social media bot. With this definition as a premise, we systematically compare characteristics between bots and humans across global events, and reflect on how the software-programmed bot is an Artificial Intelligent algorithm, and its potential for evolution as technology advances. Based on our results, we provide recommendations for the use and regulation of bots. Finally, we discuss open challenges and future directions: Detect, to systematically identify these automated and potentially evolving bots; Differentiate, to evaluate the goodness of the bot in terms of their content postings and relationship interactions; Disrupt, to moderate the impact of malicious bots.
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