使用BERT的跨双平台新闻与社交媒体对比关联方法：面向文章与推文
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms
AI总结：
针对推文与新闻关联的难题，本文提出基于预训练BERT的对比学习方法CATBERT，在俄乌战争相关多语言数据集上表现优于传统方法和OpenAI嵌入方法，还验证了其在推文级联主题查找任务中的性能。
摘要：
X（原Twitter）已发展为当代公共讨论空间，为人们提供了表达对时事看法与观点的平台。Twitter上讨论的绝大多数话题都与正在发生的事件直接相关，使其成为监测公众舆论的重要来源。然而，由于推文简洁且非正式的特性，将推文与特定新闻关联起来是一项重大挑战。以往的方法包括主题模型、基于图的模型以及监督分类器，都未能有效捕捉推文和新闻文章的独特特征。\n受计算机视觉领域CLIP模型（该模型采用对比学习来建模图像与字幕之间的相似性）成功的启发，本文提出了一种对比学习方法，用于训练一个表征空间，使存在关联的文章和推文在该空间中距离相近。我们提出了对比学习方法CATBERT（Contrastive Articles Tweets BERT，对比式文章-推文BERT），该方法利用预训练BERT模型实现。我们在一个包含人工标注的、与俄乌战争相关的英文和波兰文推文及文章的数据集上对模型进行了训练和测试。我们将CATBERT的性能与LDA等传统方法，以及此前未被应用于该任务的、基于OpenAI嵌入的新型方法进行了对比评估。研究结果表明，CATBERT在将推文与相关新闻文章关联方面表现出更优的性能。此外，我们还验证了模型在查找推文级联整体的核心主题（以一篇文章作为代表）任务上的性能。在这项新任务中，我们报告了不同模型的性能随级联规模变化的情况。
英文摘要：
X (formerly Twitter) has evolved into a contemporary agora, offering a platform for individuals to express opinions and viewpoints on current events. The majority of the topics discussed on Twitter are directly related to ongoing events, making it an important source for monitoring public discourse. However, linking tweets to specific news presents a significant challenge due to their concise and informal nature. Previous approaches, including topic models, graph-based models, and supervised classifiers, have fallen short in effectively capturing the unique characteristics of tweets and articles. Inspired by the success of the CLIP model in computer vision, which employs contrastive learning to model similarities between images and captions, this paper introduces a contrastive learning approach for training a representation space where linked articles and tweets exhibit proximity. We present our contrastive learning approach, CATBERT (Contrastive Articles Tweets BERT), leveraging pre-trained BERT models. The model is trained and tested on a dataset containing manually labeled English and Polish tweets and articles related to the Russian-Ukrainian war. We evaluate CATBERT's performance against traditional approaches like LDA, and the novel method based on OpenAI embeddings, which has not been previously applied to this task. Our findings indicate that CATBERT demonstrates superior performance in associating tweets with relevant news articles. Furthermore, we demonstrate the performance of the models when applied to finding the main topic -- represented by an article -- of the whole cascade of tweets. In this new task, we report the performance of the different models in dependence on the cascade size.
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。
