  This paper 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. general election. We aimed to
unearth the relationship between ad engagement (as measured by ad clicks) and
41 features related to ads' metadata, sociolinguistic structures, and
sentiment. Our analysis was three-fold: (i) understand the relationship between
engagement and features via correlation analysis; (ii) find the most relevant
feature subsets to predict engagement via feature selection; and (iii) find the
semantic topics that best characterize the dataset via topic modeling. We found
that ad expenditure, text size, ad lifetime, and sentiment were the top
features predicting users' engagement to the ads. Additionally, positive
sentiment ads were more engaging than negative ads, and sociolinguistic
features (e.g., use of religion-relevant words) were identified as highly
important in the makeup of an engaging ad. Linear SVM and Logistic Regression
classifiers 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 the
IRA specifically targeted Americans on divisive ad topics (e.g., LGBT rights,
African American reparations).
