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This paper examines 3,517 Facebook ads created by Russia's Internet ResearchAgency (IRA) between June 2015 and August 2017 in its active measuresdisinformation campaign targeting the 2016 U.S. general election. We aimed tounearth the relationship between ad engagement (as measured by ad clicks) and41 features related to ads' metadata, sociolinguistic structures, andsentiment. Our analysis was three-fold: (i) understand the relationship betweenengagement and features via correlation analysis; (ii) find the most relevantfeature subsets to predict engagement via feature selection; and (iii) find thesemantic topics that best characterize the dataset via topic modeling. We foundthat ad expenditure, text size, ad lifetime, and sentiment were the topfeatures predicting users' engagement to the ads. Additionally, positivesentiment ads were more engaging than negative ads, and sociolinguisticfeatures (e.g., use of religion-relevant words) were identified as highlyimportant in the makeup of an engaging ad. Linear SVM and Logistic Regressionclassifiers achieved the highest mean F-scores (93.6% for both models),determining that the optimal feature subset contains 12 and 6 features,respectively. Finally, we corroborate the findings of related works that theIRA specifically targeted Americans on divisive ad topics (e.g., LGBT rights,African American reparations).

Facebook Ad Engagement in the Russian Active Measures Campaign of 2016