Exploiting the Right: Inferring Ideological Alignment in Online Influence Campaigns Using Shared Images (original) (raw)
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Examining Similar and Ideologically Correlated Imagery in Online Political Communication
arXiv (Cornell University), 2021
This paper investigates visual media shared by US national politicians on Twitter, how a politician's variety of image types shared reflects their political position, and identifies a hazard in using standard methods for image characterization in this context. While past work has yielded valuable results on politicians' use of imagery in social media, that work has focused primarily on photographic media, which may not be sufficient given the variety of visual media shared in such spaces (e.g., infographics, illustrations, or memes). Leveraging three popular deep learning models to characterize politicians' visuals, this work uses clustering to identify eight types of visual media shared on Twitter, several of which are not photographic in nature. Results also show individual politicians share a variety of these types, and the distributions of their imagery across these clusters is correlated with their overall ideological position-e.g., liberal politicians appear to share a larger proportion of infographic-style images, and conservative politicians appear to share more patriotic imagery. At the same time, manual assessment reveals that these image characterization models group images with vastly different semantic meaning into the same clusters, as confirmed in a post-hoc analysis of hateful memetic imagery. These results suggest that, while image-characterization techniques do identify general types of imagery that correlate with political ideology, these methods miss critical semantic-and therefore politically relevant-differences among images. Consequently, care should be taken when dealing with the varieties of imagery shared in online spaces, especially in political contexts.
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Journal of Communication, 2019
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Earlier studies on ideological congruence mostly rely on public opinion surveys to measure voter ideology , while politicians' ideology is measured by instruments such as roll call votes, expert surveys, and legislative texts. One crucial problem with such approaches is that the tools used to measure the elites' ideology are not identical to those used to measure the voters' ideology. The rapid growth of social media use offers a unique opportunity to directly examine the ideological overlap of elites and the electorate on a common platform using a common technique. This study examines over four million Twitter posts by legislative candidates from four major Turkish parties and their supporters between 2012 and 2016. After applying machine-learning algorithms to clean non-political content from the data, we employ Wordfish text scaling technique to extract the policy positions and compare the party positions to those of other parties and to those of their supporters.
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#bias: Measuring the Tweeting Behavior of Propagandists
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Twitter is an efficient conduit of information for millions of usersaround the world. Its ability to quickly spread information to a largenumber of people makes it an efficient way to shape information and,hence, shape public opinion. We study the tweeting behavior of Twitter propagandists, users who consistently expressthe same opinion or ideology, focusing on two online communities: the2010 Nevada senate race and the 2011 debt-ceiling debate. We identify several extreme tweeting patterns thatcould characterize users who spread propaganda: (1) sending high volumesof tweets over short periods of time, (2) retweeting whilepublishing little original content, (3) quickly retweeting, and (4) colluding with other, seeminglyunrelated, users to send duplicate or near-duplicate messages on thesame topic simultaneously. These four features appear to distinguishtweeters who spread propaganda from other more neutral users and could serve asstarting point for developing behavioral-based propaga...
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This study integrates network and content analyses to examine exposure to cross-ideological political views on Twitter. We mapped the Twitter networks of 10 controversial political topics, discovered clusters -subgroups of highly self-connected users -and coded messages and links in them for political orientation. We found that Twitter users are unlikely to be exposed to cross-ideological content from the clusters of users they followed, as these were usually politically homogeneous. Links pointed at grassroots web pages (e.g.: blogs) more frequently than traditional media websites. Liberal messages, however, were more likely to link to traditional media. Last, we found that more specific topics of controversy had both conservative and liberal clusters, while in broader topics, dominant clusters reflected conservative sentiment.
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