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AbstractWhile previous research has revealed an ideological divide in Americans' perceptions of COVID‐19, specific ideological components can additionally explain public reactions to the pandemic. With two surveys—one sample of crowdsourced workers (N = 482) and a nationally representative sample of American adults (N = 7449)—this research investigates how multiple ideological facets simultaneously predict individuals' reactions to COVID‐19. Results demonstrate that social dominance orientation and libertarianism are two important ideological sources that predict more dismissal of COVID‐19 and less support for government measures. Right‐wing authoritarianism was negatively correlated with COVID‐19 concern and support for government actions, but suppression effects could exist. The effects of ideological variables were largely consistent when trust in science was considered. This study highlights the role of specific ideological components in contributing to the political divide regarding attitudes toward the COVID‐19 pandemic beyond the liberal–conservative identification.

Previous research on the success of politicians' messages on social media has so far focused on a limited number of platforms, especially Facebook and Twitter, and predominately studied the effects of textual content. This research reported here applies computer vision analysis to a total of 59,020 image posts published by 172 Instagram accounts of U.S. politicians, both candidates and office holders, and examines how visual attributes influence audience engagement such as likes and comments. In particular, this study introduces an unsupervised approach that combines transfer learning and clustering techniques to discover hidden categories from large-scale visual data. The results reveal that different self-personalization strategies in visual media, for example, images featuring politicians in private, nonpolitical settings, showing faces, and displaying emotions, generally increase audience engagement. Yet, a significant portion of politician's Instagram posts still fell into the traditional, "politics-as-usual" type of political communication, showing professional settings and activities. The analysis explains how self-personalization is embodied in specific visual portrayals and how different self-presentation strategies affect audience engagement on a popular but less studied social media platform.

Automated image analysis has received increasing attention in social scientific research, yet existing scholarship has mostly covered the application of supervised learning to classify images into predefined categories. This study focuses on the task of unsupervised image clustering, which aims to automatically discover categories from unlabelled image data. We first review the steps to perform image clustering and then focus on one key challenge in this task—finding intermediate representations of images. We present several methods of extracting intermediate image representations, including the bag-of-visual-words model, self-supervised learning, and transfer learning (in particular, feature extraction with pretrained models). We compare these methods using various visual datasets, including images related to protests in China from Weibo, images about climate change on Instagram, and profile images of the Russian Internet Research Agency on Twitter. In addition, we propose a systematic way to interpret and validate clustering solutions. Results show that transfer learning significantly outperforms the other methods. The dataset used in the pretrained model critically determines what categories the algorithms can discover.

While partisan selective exposure could drive audience fragmentation, other individual factors might also differentiate news diets. This study applies a method that disentangles the differential contributions of the individual characteristics to audience duplication networks. By analyzing a nationally representative survey about US adults' media use in 2019 ( N = 12,043), we demonstrate that news fragmentation is driven by a myriad of individual factors, such as gender, race, and religiosity. Partisanship is still an important driver. We also distinguish between media exposure and media trust, showing that many cross-cutting ties in co-exposure networks disappear when media trust is considered. We conclude that audience fragmentation research should extend beyond ideological selectivity and additionally investigate how and why other individual-level preferences differentially contribute to fragmentation both in news exposure and in news trust.

Digital gatekeepers have greatly shaped the gatekeeping process of news consumption and news engagement, but how digital gatekeepers work is understudied. This study focuses on one example of digital gatekeepers, trending topics on social media, which aggregate the most popular search terms and present them to the public. We utilize a natural experiment on Weibo by analyzing user engagement data of 36,239 posts in three consecutive weeks, during which trending topics were removed for 1 week. We show that trending topics implemented two layers of gatekeeping: trending topics increased user engagement with top news posts within each news outlet and widened the engagement gap between popular posts and less popular ones (intra-outlet gatekeeping), and the increases in engagement with top news items were most salient among the least popular news outlets, thus reducing the inequality among outlets (inter-outlet gatekeeping).

To amplify their audience reach, far-right outlets need a calculated and coordinated array of acts to set the stage for audience attention and to build a communication network that spreads their messages. We examined the Facebook newsfeed history of The Epoch Times ( N = 117,274 posts from 2013 to 2020), which transitioned from a niche anti-China publication to an influential player in US far-right politics. We found that US partisan issues helped the outlet attract immediate audience engagement, but such content did not invite audience growth and engagement with future content. In contrast, viral entertaining videos, while seemingly benign and mundane, demonstrated superb effects in getting audience responses, accumulating followers, and boosting engagement with subsequent posts. We argue that entertainment media is a crucial component of the extreme politics landscape that directs the flows of political attention, which could exert a "ripple effect" on assembling audience networks.