With the development of social media networks, rumor detection models have attracted more and more attention. Whereas, these models primarily focus on classifying contexts as rumors or not, lacking the capability to locate and mark specific rumor content. To address this limitation, this paper proposes a novel rumor detection model named Insight Rumors to locate and mark rumor content within textual data. Specifically, we propose the Bidirectional Mamba2 Network with Dot-Product Attention (Att_BiMamba2), a network that constructs a bidirectional Mamba2 model and applies dot-product attention to weight and combine the outputs from both directions, thereby enhancing the representation of high-dimensional rumor features. Simultaneously, a Rumor Locating and Marking module is designed to locate and mark rumors. The module constructs a skip-connection network to project high-dimensional rumor features onto low-dimensional label features. Moreover, Conditional Random Fields (CRF) is employed to impose strong constraints on the output label features, ensuring accurate rumor content location. Additionally, a labeled dataset for rumor locating and marking is constructed, with the effectiveness of the proposed model is evaluated through comprehensive experiments. Extensive experiments indicate that the proposed scheme not only detects rumors accurately but also locates and marks them in context precisely, outperforming state-of-the-art schemes that can only discriminate rumors roughly.

翻译：随着社交媒体网络的发展，谣言检测模型受到越来越多的关注。然而，这些模型主要侧重于将文本内容分类为谣言或非谣言，缺乏定位和标注具体谣言内容的能力。为应对这一局限，本文提出了一种名为“洞察谣言”的新型谣言检测模型，用于在文本数据中定位和标注谣言内容。具体而言，我们提出了带点积注意力的双向Mamba2网络（Att_BiMamba2），该网络构建了一个双向Mamba2模型，并应用点积注意力对双向输出进行加权与融合，从而增强高维谣言特征的表征能力。同时，设计了一个谣言定位与标注模块来定位和标注谣言。该模块构建了一个跳跃连接网络，将高维谣言特征映射到低维标签特征。此外，采用条件随机场（CRF）对输出标签特征施加强约束，以确保谣言内容的准确定位。另外，构建了一个用于谣言定位与标注的标注数据集，并通过综合实验评估了所提模型的有效性。大量实验表明，所提方案不仅能准确检测谣言，还能在上下文中精确定位与标注谣言，其性能优于仅能粗略判别谣言的现有先进方案。