将自我中心网络模型应用于跨目标立场检测
Applying the Ego Network Model to Cross-Target Stance Detection
AI总结：
本文提出自我中心网络模型ENM和带符号自我中心网络模型SENM用于跨目标立场检测，在所需数据更少且更易获取的情况下达到与图神经网络模型CT-TN相似的准确率，并发现代表弱联系的外圈对立场预测更有用。
摘要：
理解人类互动与社会结构是一项极其重要的任务，尤其是在这样一个高度互联的世界中。促进这一理解的一项任务是立场检测，它预测文本对某一目标实体的意见或态度。传统上，这通常主要借助基于文本的方法来完成，然而，近期工作提出了一个模型（CT-TN），该模型利用关于用户社交网络的信息来帮助预测其立场，并在某些跨目标文本方法上表现更优。遗憾的是，此类基于图的方法所需的数据并非总是可用。本文提出了两种用于立场检测的新工具：自我中心网络模型（Ego Network Model，ENM）和带符号自我中心网络模型（Signed Ego Network Model，SENM）。这些模型建立在人类学和心理学研究的基础上，并已在社会网络分析及相关任务（例如链接预测）的语境中得到使用。利用这些特征获得的立场检测预测达到了与CT-TN所使用的基于图的特征相似的准确率水平，同时所需数据更少且更易获取。除此之外，本文还比较了ENM中分别代表较强和较弱社会联系的内圈与外圈的表现。令人惊讶的是，包含更多但较不亲密连接的外圈对预测立场更为有用。
英文摘要：
Understanding human interactions and social structures is an incredibly important task, especially in such an interconnected world. One task that facilitates this is Stance Detection, which predicts the opinion or attitude of a text towards a target entity. Traditionally, this has often been done mainly via the use of text-based approaches, however, recent work has produced a model (CT-TN) that leverages information about a user's social network to help predict their stance, outperforming certain cross-target text-based approaches. Unfortunately, the data required for such graph-based approaches is not always available. This paper proposes two novel tools for Stance Detection: the Ego Network Model (ENM) and the Signed Ego Network Model (SENM). These models are founded in anthropological and psychological studies and have been used within the context of social network analysis and related tasks (e.g., link prediction). Stance Detection predictions obtained using these features achieve a level of accuracy similar to the graph-based features used by CT-TN while requiring less and more easily obtainable data. In addition to this, the performances of the inner and outer circles of the ENM, representing stronger and weaker social ties, respectively are compared. Surprisingly, the outer circles, which contain more numerous but less intimate connections, are more useful for predicting stance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
