Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, e.g., Web pages or scientific papers, with descriptive labels called tags. The social annotations contributed by thousands of users, can potentially be used to infer categorical knowledge, classify documents or recommend new relevant information. Traditional text inference methods do not make best use of social annotation, since they do not take into account variations in individual users' perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes interests of individual annotators into account in order to find hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be specified a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the synthetic and real-world data by comparing its performance to Latent Dirichlet Allocation on the topic extraction task. For the latter evaluation, we apply the model to infer topics of Web resources from social annotations obtained from Delicious in order to discover new resources similar to a specified one. Our empirical results demonstrate that the proposed model is a promising method for exploiting social knowledge contained in user-generated annotations.
Prompt Selection Matters: Enhancing Text Annotations for Social Sciences with Large Language Models
Université Paris 1 Panthéon-Sorbonne（巴黎第一大学先贤祠-索邦大学）；Université Paris-Saclay（巴黎萨克雷大学）；Institut Jean Nicod（让·尼科德研究所）；Ecole Normale Supérieure（巴黎高等师范学院）；PSL-EHESS-CNRS（巴黎文理研究大学-法国高等社会科学研究院-法国国家科学研究中心）
Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of Helsinki（赫尔辛基大学）
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
University of Oxford（牛津大学）
无向图模型在统计学、物理学和机器视觉等领域有着广泛的应用。然而，无向模型的贝叶斯参数估计是极具挑战性的，因为后验的评估通常涉及到一个棘手的归一化常数的计算。这个问题已经得到了很大的关注，但很少集中在重要的实际情况下，数据包括噪音或不完整的观察潜在的隐藏结构。本文专门针对这个问题，比较两种替代方法。在这些方法的第一种中，粒子马尔可夫链蒙特卡罗（Andrieu等人，2010）用于有效地探索参数空间，结合交换算法（Murray et al.，2006），以避免计算棘手的归一化常数（证明这种组合的目标是正确的分布，可以在在线补充附录中找到）。该方法与近似贝叶斯计算（Pritchard等人，1999年）。应用估计伊辛模型和指数随机图的参数从噪声数据。由于使用MCMC从潜在图形模型进行模拟，本文中使用的每个算法都以接近真实后验为目标，而不是能够在一般情况下准确地做到这一点。补充附录还说明了由此得出的近似值的性质。
