What Makes Disinformation Ads Engaging? A Case Study of Facebook Ads from the Russian Active Measures Campaign
What Makes Disinformation Ads Engaging? A Case Study of Facebook Ads from the Russian Active Measures Campaign
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DOI：
10.1080/15252019.2023.2173991
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发表时间：
2023-02
影响因子：
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通讯作者：
Mirela Silva;Luiz H. F. Giovanini;Juliana Fernandes;Daniela Oliveira;Catia S. Silva
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作者：
Mirela Silva;Luiz H. F. Giovanini;Juliana Fernandes;Daniela Oliveira;Catia S. Silva
本文研究了俄罗斯互联网研究机构（伊拉）在2015年6月至2017年8月期间针对2016年美国总统大选的“积极措施”虚假信息活动中创建的3，517个Facebook广告。我们的目标是挖掘广告参与度（广告点击）和40个与广告元数据、心理意义和情感相关的特征之间的关系。我们分析的目的是（1）理解参与度和特征之间的关系，（2）通过特征选择找到最相关的特征子集来预测参与度，以及（3）通过主题建模找到最能表征数据集的语义主题。我们发现，投资特征（例如，广告花费、广告寿命）、标题长度和情感是预测用户对广告的参与度的主要特征。此外，积极情绪广告比消极广告更吸引人，心理语言学特征（例如，使用宗教相关的词语）被认为在制作一个吸引人的虚假信息广告中非常重要。线性支持向量机（SVM）和逻辑回归分类器实现了最高的平均F分数（93.6%），揭示了最佳特征子集分别包含12个和6个特征。最后，我们证实了以前的研究结果，即伊拉专门针对美国人的分裂广告主题（例如，LGBT权利），并提出虚假信息广告的定义。
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.