基于智能体框架的社交媒体阴谋论检测研究
Agentic Detection of Online Conspiracies
以色列团队用四年份希伯来语推文数据做阴谋论检测，让智能体按需调用工具查社交上下文，比纯文本分类准不少。
arXiv 论文提出一种智能体框架，用于识别社交媒体上的阴谋论言论，核心难点在于推断发言者意图而非仅识别字面内容。
研究基于覆盖 2018 年底至 2023 年初约 80%–90% 公开希伯来语推文的数据集，涵盖多轮选举和新冠疫苗接种期。在人工标注的对抗性数据集上，带上下文感知的工作流持续优于纯文本分类，智能体框架又显著优于接触相同上下文的非智能体模型。
作者还分析了错误类型与 token 消耗的效率权衡。
Agentic Detection of Online Conspiracies
Conspiratorial discourse on social media is not always expressed through explicit claims or stable lexical markers. The same surface content may express endorsement, legitimate concerns, criticism, satire, or mockery. The main challenge is therefore not only recognizing conspiracy-related claims, but inferring the speaker's intent -- the utterance's illocutionary force. We argue that this can be achieved through the use of relevant social contexts and propose an agentic framework, equipped with a set of tools supporting social queries. We demonstrate the benefits of our approach on a unique dataset of Hebrew tweets, covering 80\%--90\% of the public Hebrew tweets published over a four-year span (late 2018-- early 2023), encompassing several election cycles as well as the COVID pandemic years and related vaccination campaigns. This extensive coverage can be used in recovering different social contexts. Evaluating our framework on a manually-annotated adversarial dataset, we find that context-aware workflows consistently outperform text-only classification and that the agentic framework performs significantly better than other frameworks and settings, including a non-agentic model exposed to the same contexts available to the agent. We further provide an analysis of the results, the errors and efficiency (token economy) tradeoffs. These findings support viewing the task of conspiracy detection as a socially embedded interpretation task, in which effective classification depends not only on access to contexts, but also on adaptive reasoning in which the agent uses tools on a per-case basis, asking only for evidence relevant to its current reasoning step.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：智能体、阴谋论检测、社交媒体、arXiv，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Lior Biton, Oren Tsur），经 AITOP 多维评分管线结构化提炼。
