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Fairness in Online Social Network Timelines: Measurements, Models and Mechanism Design

机译:在线社交网络时间表中的公平性:度量,模型和机制设计

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摘要

Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.
机译:Facebook News Feed个性化算法每天都会对数百万互联网用户的生活方式,情绪和意见产生重大影响。但是,这种算法的行为缺乏透明度,无法进行测量,建模和分析,从而无法理解和改善其性能。在本文中,我们提出了一种可重现的方法,包括测量,分析模型和基于公平的新闻提要设计。该模型利用生存时间(TTL)计数器的多功能性和分析可处理性来捕获新闻提要中发布者的可见性和占用率。度量用于参数化和验证所提出模型的表达能力。然后,我们进行假设分析,以评估用户根据该模型得出的基准引起的可见性和占用率偏差。我们的结果表明存在明显的偏见,并且在新闻Feed的顶部位置更为明显。此外,我们发现,即使对于故意将其政治观点定为中立的用户,这种偏见也是不可忽略的,这促使人们提出了一种新颖且更加透明的基于公平的新闻提要设计。

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