社交媒体上国家赞助宣传的多模态识别
Multi-modal Identification of State-Sponsored Propaganda on Social Media
摘要：
近年来，国家在互联网上进行宣传的盛行已成为令人关切的问题。虽然已经做出了很多努力来识别国家支持的互联网宣传，但这个问题仍然远远没有解决，因为宣传的模糊定义导致不可靠的数据标签，大量的潜在预测特征导致模型无法解释。本文是第一次尝试为这项任务建立一个平衡的数据集。该数据集由三个不同组织在两个时间段的宣传组成。提出了一种仅基于视觉和文本内容检测宣传消息的多模型框架，该框架在同一时间段（对同一时间段的数据进行训练和测试）（F1=0.869）和不同时间段（对过去进行训练，对未来进行测试）（F1=0.697）检测三个组织的宣传方面都取得了令人满意的性能。为了减少假阳性预测的影响，我们改变阈值来测试假阳性率和真阳性率之间的关系，并使用可视化工具为我们的模型所做的预测提供解释，以增强我们框架的可解释性。我们的新数据集和总体框架为识别国家支持的互联网宣传的任务提供了一个强有力的基准，并为这项任务的未来工作指出了一条潜在的道路。
英文摘要：
The prevalence of state-sponsored propaganda on the Internet has become a cause for concern in the recent years. While much effort has been made to identify state-sponsored Internet propaganda, the problem remains far from being solved because the ambiguous definition of propaganda leads to unreliable data labelling, and the huge amount of potential predictive features causes the models to be inexplicable. This paper is the first attempt to build a balanced dataset for this task. The dataset is comprised of propaganda by three different organizations across two time periods. A multi-model framework for detecting propaganda messages solely based on the visual and textual content is proposed which achieves a promising performance on detecting propaganda by the three organizations both for the same time period (training and testing on data from the same time period) (F1=0.869) and for different time periods (training on past, testing on future) (F1=0.697). To reduce the influence of false positive predictions, we change the threshold to test the relationship between the false positive and true positive rates and provide explanations for the predictions made by our models with visualization tools to enhance the interpretability of our framework. Our new dataset and general framework provide a strong benchmark for the task of identifying state-sponsored Internet propaganda and point out a potential path for future work on this task.
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。
