论文标题
自动在Facebook上识别政治广告：通过用户定位了解操纵
Automatically Identifying Political Ads on Facebook: Towards Understanding of Manipulation via User Targeting
论文作者
论文摘要
俄罗斯干预在2016年美国选举中的报道使公众关注问题的中心与外国参与者增加社会不和谐和利用个人用户数据的能力有关政治目的。它提出了有关可以使用数据来创建心理概况的方式和程度的问题，以确定哪种广告最有效地说服特定位置的某人进行某些政治事件。在这项工作中，我们研究了美国非营利性新闻编辑室ProPublica收集的政治广告数据集，该数据集在2018年美国中期选举之前使用志愿者网络。我们首先描述数据的主要特征，并探索用户属性，包括年龄，区域，活动等，以及一系列交互式插图。此外，通过用户定位对政治操纵的低估迈出的重要第一步是确定与政治相关的广告，但是由于社交媒体广告的规模，手动检查广告是不可行的。因此，我们应对在政治和非政治广告之间自动分类的挑战，与ProPublica使用的当前基于文本的分类器相比，证明了显着改善，并研究用户靶向属性是否对该任务有益。我们的评估阐明了问题，例如如何将用户属性用于针对政治广告，以及哪些用户更容易针对政治广告。总体而言，我们对目标属性的数据探索，政治广告分类和初始分析的贡献旨在支持ProPublica数据集的未来工作，特别是关于通过用户定位对政治操纵的理解。
The reports of Russian interference in the 2016 United States elections brought into the center of public attention concerns related to the ability of foreign actors to increase social discord and take advantage of personal user data for political purposes. It has raised questions regarding the ways and the extent to which data can be used to create psychographical profiles to determine what kind of advertisement would be most effective to persuade a particular person in a particular location for some political event. In this work, we study the political ads dataset collected by ProPublica, an American nonprofit newsroom, using a network of volunteers in the period before the 2018 US midterm elections. We first describe the main characteristics of the data and explore the user attributes including age, region, activity, and more, with a series of interactive illustrations. Furthermore, an important first step towards understating of political manipulation via user targeting is to identify politically related ads, yet manually checking ads is not feasible due to the scale of social media advertising. Consequently, we address the challenge of automatically classifying between political and non-political ads, demonstrating a significant improvement compared to the current text-based classifier used by ProPublica, and study whether the user targeting attributes are beneficial for this task. Our evaluation sheds light on questions, such as how user attributes are being used for political ads targeting and which users are more prone to be targeted with political ads. Overall, our contribution of data exploration, political ad classification and initial analysis of the targeting attributes, is designed to support future work with the ProPublica dataset, and specifically with regard to the understanding of political manipulation via user targeting.
