The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs. While detecting and profiling the sources that spread this news is important to maintain a healthy society, it is challenging for automated systems. In this paper, we propose an interactive framework for news media profiling. It combines the strengths of graph based news media profiling models, Pre-trained Large Language Models, and human insight to characterize the social context on social media. Experimental results show that with as little as 5 human interactions, our framework can rapidly detect fake and biased news media, even in the most challenging settings of emerging news events, where test data is unseen.

翻译：近年来社交媒体的兴起导致大量虚假和偏见性新闻的传播，这些内容旨在影响公众认知。虽然检测和识别传播此类新闻的来源对于维护社会健康发展至关重要，但对自动化系统而言仍具有挑战性。本文提出了一种交互式新闻媒体档案分析框架，融合了基于图的新闻媒体档案模型、预训练大语言模型以及人类洞察力，用以刻画社交媒体上的社会语境。实验结果表明，即使仅需5次人机交互，我们的框架也能在最具挑战性的新兴新闻事件场景中（测试数据完全未知）快速检测出虚假和偏见性新闻媒体。