What Makes Disinformation Ads Engaging? A Case Study of Facebook Ads from the Russian Active Measures Campaign

Abstract

Abstract This article examines 3,517 Facebook ads created by Russia’s Internet Research Agency (IRA) between June 2015 and August 2017 in its Active Measures disinformation campaign targeting the 2016 U.S. presidential election. We aimed to unearth the relationship between ad engagement (ad clicks) and 40 features related to the ads’ metadata, psychological meaning, and sentiment. The purpose of our analysis was to (1) understand the relationship between engagement and features, (2) find the most relevant feature subsets to predict engagement via feature selection, and (3) find the semantic topics that best characterize the data set via topic modeling. We found that investment features (e.g., ad spend, ad lifetime), caption length, and sentiment were the top features predicting users’ engagement with the ads. In addition, positive sentiment ads were more engaging than negative ads, and psycholinguistic features (e.g., use of religion-relevant words) were identified as highly important in the makeup of an engaging disinformation ad. Linear support vector machines (SVMs) and logistic regression classifiers achieved the highest mean F scores (93.6%), revealing that the optimal feature subset contains 12 and six features, respectively. Finally, we corroborate the findings of previous research that the IRA specifically targeted Americans on divisive ad topics (e.g., LGBT rights) and advance a definition of disinformation advertising.