基於LLM的操控性政治敘事檢測

摘要

我們提出了一個新的計算框架，用於偵測並結構化操縱性政治敘事。由於政治討論轉向社群媒體，此任務變得更加重要。其中一個主要挑戰在於區分操縱性政治敘事與合法批評。某些貼文也可能在操縱性脈絡下重新詮釋實際事件。為達到良好的分群結果，我們事先使用一份詳細的少量樣本提示來過濾操縱性貼文，該提示結合了有記錄的競選敘事與合法批評，以區分兩者。此提示使推理模型能夠標註類別，僅保留操縱性敘事貼文供後續處理。其餘貼文隨後進行嵌入，並使用UMAP進行降維，接著應用HDBSCAN以揭示敘事群組。此無監督方法的一項關鍵優勢在於：它不依賴預先定義的目標類別清單，因此能夠發現新的敘事叢集。最後，運用推理模型來揭露每個叢集背後的敘事。此方法應用於超過120萬則社群媒體貼文，透過整合基於提示的過濾與無監督聚類，成功識別出41個截然不同的操縱性敘事叢集。

English

We present a new computational framework for detecting and structuring manipulative political narratives. A task that became more important due to the shift of political discussions to social media. One of the primary challenges thereby is differentiating between manipulative political narratives and legitimate critiques. Some posts may also reframe actual events within a manipulative context. To achieve good clustering results, we filter manipulative posts beforehand using a detailed few-shot prompt that combines documented campaign narratives with legitimate criticisms to differentiate them. This prompt enables a reasoning model to assign labels, retaining only manipulative narrative posts for further processing. The remaining posts are subsequently embedded and dimensionality-reduced using UMAP, before HDBSCAN is applied to uncover narrative groups. A key advantage of this unsupervised approach is its independence from a predefined list of target categories, enabling it to uncover new narrative clusters. Finally, a reasoning model is employed to uncover the narrative behind each cluster. This approach, applied to over 1.2 million social media posts, effectively identified 41 distinct manipulative narrative clusters by integrating prompt-based filtering with unsupervised clustering.