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This work advances investigations into the visual media shared by agents in disinformation campaigns by characterizing the images shared by accounts identified by Twitter as being part of such campaigns. Using images shared by US politicians' Twitter accounts as a baseline and training set, we build models for inferring the ideological presentation of accounts using the images they share. Results show that, while our models recover the expected bimodal ideological distribution of US politicians, we find that, on average, four separate influence campaigns -- attributed to Iran, Russia, China, and Venezuela -- all present conservative ideological presentations in the images they share. Given that prior work has shown Twitter accounts used by Russian disinformation agents are ideologically diverse in the text and news they share, these image-oriented findings provide new insights into potential axes of coordination and suggest these accounts may not present consistent ideological positions across modalities.

Coordinated political influence operations leave a distinct signature in content that machine learning can detect., We study how easy it is to distinguish influence operations from organic social media activity by assessing the performance of a platform-agnostic machine learning approach. Our method uses public activity to detect content that is part of coordinated influence operations based on human-interpretable features derived solely from content. We test this method on publicly available Twitter data on Chinese, Russian, and Venezuelan troll activity targeting the United States, as well as the Reddit dataset of Russian influence efforts. To assess how well content-based features distinguish these influence operations from random samples of general and political American users, we train and test classifiers on a monthly basis for each campaign across five prediction tasks. Content-based features perform well across period, country, platform, and prediction task. Industrialized production of influence campaign content leaves a distinctive signal in user-generated content that allows tracking of campaigns from month to month and across different accounts.

This paper introduces LABurst, a general technique for identifying key moments, or moments of high impact, in social media streams without the need for domain-specific information or seed keywords. We leverage machine learning to model temporal patterns around bursts in Twitter's unfiltered public sample stream and build a classifier to identify tokens experiencing these bursts. We show LABurst performs competitively with existing burst detection techniques while simultaneously providing insight into and detection of unanticipated moments. To demonstrate our approach's potential, we compare two baseline event-detection algorithms with our language-agnostic algorithm to detect key moments across three major sporting competitions: 2013 World Series, 2014 Super Bowl, and 2014 World Cup. Our results show LABurst outperforms a time series analysis baseline and is competitive with a domain-specific baseline even though we operate without any domain knowledge. We then go further by transferring LABurst's models learned in the sports domain to the task of identifying earthquakes in Japan and show our method detects large spikes in earthquake-related tokens within two minutes of the actual event.