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The reports of Russian interference in the 2016 United States electionsbrought into the center of public attention concerns related to the ability offoreign actors to increase social discord and take advantage of personal userdata for political purposes. It has raised questions regarding the ways and theextent to which data can be used to create psychographical profiles todetermine what kind of advertisement would be most effective to persuade aparticular person in a particular location for some political event. In thiswork, we study the political ads dataset collected by ProPublica, an Americannonprofit newsroom, using a network of volunteers in the period before the 2018US midterm elections. We first describe the main characteristics of the dataand explore the user attributes including age, region, activity, and more, witha series of interactive illustrations. Furthermore, an important first steptowards understating of political manipulation via user targeting is toidentify politically related ads, yet manually checking ads is not feasible dueto the scale of social media advertising. Consequently, we address thechallenge of automatically classifying between political and non-political ads,demonstrating a significant improvement compared to the current text-basedclassifier used by ProPublica, and study whether the user targeting attributesare beneficial for this task. Our evaluation sheds light on questions, such ashow user attributes are being used for political ads targeting and which usersare more prone to be targeted with political ads. Overall, our contribution ofdata exploration, political ad classification and initial analysis of thetargeting attributes, is designed to support future work with the ProPublicadataset, and specifically with regard to the understanding of politicalmanipulation via user targeting.

Automatically Identifying Political Ads on Facebook: Towards Understanding of Manipulation via User Targeting