用符号学框架识别阴谋论，提升对隐性叙事的检测能力
Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection
- 基于符号学与框架语义构建阴谋论语义框架
- 在Telegram数据集上实现细粒度标注，发现关键语义模式
- 为大模型提供可解释的语义线索，适合安全与舆情研究者
阴谋论是反权威叙事，易引发社会冲突，影响公众对政治信息的判断。本文提出‘阴谋论框架’（Conspiracy Frame），一种源自框架语义学与符号学的细粒度语义表征，并构建了Con.Fra.数据集——一个在跨度级别标注的Telegram消息语料库。该框架与数据集有助于更泛化的阴谋论理解与识别。我们评估了大模型在域内与跨域场景下的识别能力，发现尽管上下文注入框架未显著提升性能，但其潜力显现；将标注跨度映射至FrameNet后，识别出如`Kinship`（亲属关系）、`Ingest_substance`（摄取物质）等抽象语义模式，为更语义化、符号化地检测阴谋叙事提供可能。
原文摘要 · Abstract (English)
Conspiracy theories are anti-authoritarian narratives that lead to social conflict, impacting how people perceive political information. To help in understanding this issue, we introduce the Conspiracy Frame: a fine-grained semantic representation of conspiratorial narratives derived from frame-semantics and semiotics, which spawned the Conspiracy Frames (Con.Fra.) dataset: a corpus of Telegram messages annotated at span-level. The Conspiracy Frame and Con.Fra. dataset contribute to the implementation of a more generalizable understanding and recognition of conspiracy theories. We observe the ability of LLMs to recognize this phenomenon in-domain and out-of-domain, investigating the role that frames may have in supporting this task. Results show that, while the injection of frames in an in-context approach does not lead to clear increase of performance, it has potential; the mapping of annotated spans with FrameNet shows abstract semantic patterns (e.g., `Kinship', `Ingest\_substance') that potentially pave the way for a more semantically- and semiotically-aware detection of conspiratorial narratives.
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