标题： 一种用于争议性话题中用户和关键词分类的轻量级方法 显示英文标题

标题： A Lightweight Approach for User and Keyword Classification in Controversial Topics

摘要： 对有争议话题的立场进行分类并揭示个体的关注点对于社会科学家和政策制定者至关重要。 来自在线社交网络（OSNs）的数据，作为社会代表性样本的代理，提供了一个机会来对这些立场进行分类，发现社会在有争议话题上的关注点，并跟踪这些关注点随时间的变化。 因此，OSNs中的立场分类引起了研究人员的广泛关注。 然而，大多数现有的方法通常依赖于标记数据，并利用用户帖子的内容或用户之间的互动，这需要大量数据、大量的处理时间和对不易获取的信息（例如：用户的关注者/被关注者）的访问。 本文提出了一种轻量级的方法，用于OSNs中用户和关键词的立场分类，旨在理解个体的集体意见及其关注点。 我们的方法采用了一个定制的随机游走模型，只需每个立场的一个关键词，仅使用社交媒体帖子中的关键词。 实验结果表明，与基线方法相比，我们的方法表现出色，在用户和关键词的立场分类方面表现优异，虽然运行时间不是最快的，但仍具有竞争力。 显示英文摘要

摘要： Classifying the stance of individuals on controversial topics and uncovering their concerns is crucial for social scientists and policymakers. Data from Online Social Networks (OSNs), which serve as a proxy to a representative sample of society, offers an opportunity to classify these stances, discover society's concerns regarding controversial topics, and track the evolution of these concerns over time. Consequently, stance classification in OSNs has garnered significant attention from researchers. However, most existing methods for this task often rely on labelled data and utilise the text of users' posts or the interactions between users, necessitating large volumes of data, considerable processing time, and access to information that is not readily available (e.g. users' followers/followees). This paper proposes a lightweight approach for the stance classification of users and keywords in OSNs, aiming at understanding the collective opinion of individuals and their concerns. Our approach employs a tailored random walk model, requiring just one keyword representing each stance, using solely the keywords in social media posts. Experimental results demonstrate the superior performance of our method compared to the baselines, excelling in stance classification of users and keywords, with a running time that, while not the fastest, remains competitive.