倾听他们的声音：利用用户反馈更好地理解和检测在线错误信息
Listen to what they say: Better understand and detect online misinformation with user feedback
AI总结：
本研究从举报用户视角分析错误信息，提出内容分类体系，识别新型操纵技术和举报行为，并利用行为分解训练简单分类器以检测不同类型的被举报内容。
摘要：
社交媒体上举报内容的用户是管理在线错误信息的关键盟友，然而，目前尚无研究来理解他们的角色及其举报活动背后的不同趋势。我们提出了一种研究错误信息的新方法：从举报用户的角度，在内容层面以及跨地区和平台进行比较分析。我们提出了首个被举报内容片段的分类体系，该分类基于对2020年6月法国、英国和美国Facebook和Instagram上约9000个被举报条目的审查。这使得我们能够观察到不同国家和平台之间在举报内容上的显著差异，因为举报内容在数量、类型、主题和操纵技术上存在显著变化。通过考察其中六种技术，我们识别出一种新颖的技术，该技术特定于美国Instagram，且明显比其他技术更复杂，可能对算法检测和人工审核构成具体挑战。我们还识别出四种举报行为，并从中推导出四种类型的噪声，这些噪声能够解释被举报为错误信息的内容中一半的不准确性。最后，我们表明，将用户举报信号分解为多种行为，可以在一个小型数据集上，结合基本的用户举报信息，训练一个简单但具有竞争力的分类器，以对不同类型的被举报内容片段进行分类。
英文摘要：
Social media users who report content are key allies in the management of online misinformation, however, no research has been conducted yet to understand their role and the different trends underlying their reporting activity. We suggest an original approach to studying misinformation: examining it from the reporting users perspective at the content-level and comparatively across regions and platforms. We propose the first classification of reported content pieces, resulting from a review of c. 9,000 items reported on Facebook and Instagram in France, the UK, and the US in June 2020. This allows us to observe meaningful distinctions regarding reporting content between countries and platforms as it significantly varies in volume, type, topic, and manipulation technique. Examining six of these techniques, we identify a novel one that is specific to Instagram US and significantly more sophisticated than others, potentially presenting a concrete challenge for algorithmic detection and human moderation. We also identify four reporting behaviours, from which we derive four types of noise capable of explaining half of the inaccuracy found in content reported as misinformation. We finally show that breaking down the user reporting signal into a plurality of behaviours allows to train a simple, although competitive, classifier on a small dataset with a combination of basic users-reports to classify the different types of reported content pieces.
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。
