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1. Examining the Left‐Right Divide Through the Lens of a Global Crisis: Ideological Differences and Their Implications for Responses to the COVID‐19 Pandemic

   https://doi.org/10.1111/pops.12740  

   Ruisch, Benjamin Coe ; Moore, Courtney ; Granados Samayoa, Javier ; Boggs, Shelby ; Ladanyi, Jesse ; Fazio, Russell ( May 2021 , Political Psychology)

   The COVID‐19 disease pandemic is one of the most pressing global health issues of our time. Nevertheless, responses to the pandemic exhibit a stark ideological divide, with political conservatives (versus liberals/progressives) expressing less concern about the virus and less behavioral compliance with efforts to combat it. Drawing from decades of research on the psychological underpinnings of ideology, in four studies (totalN = 4441) we examine the factors that contribute to the ideological gap in pandemic response—across domains including personality (e.g., empathic concern), attitudes (e.g., trust in science), information (e.g., COVID‐19 knowledge), vulnerability (e.g., preexisting medical conditions), demographics (e.g., education, income) and environment (e.g., local COVID‐19 infection rates). This work provides insight into the most proximal drivers of this ideological divide and also helps fill a long‐standing theoretical and empirical gap regarding how these various ideological differences shape responses to complex real‐world sociopolitical events. Among our key findings are the central role of attitude‐ and belief‐related factors (e.g., trust in science and trust in Trump)—and the relatively weaker influence of several domain‐general personality factors (empathic concern, disgust sensitivity, conspiratorial ideation). We conclude by considering possible explanations for these findings and their broader implications for our understanding of political ideology.

   Stark ideological differences exist across a wide range of attitudinal and behavioral indices of pandemic response, with more conservative individuals reliably exhibiting less concern about the virus. These findings illustrate the extent to which the pandemic has become politicized.

   A range of factors contribute to this ideological gap in pandemic response, but some are substantially more important than others.

   Several factors that have received attention in public and academic discourse about the pandemic appear to contribute little, if at all, to the ideological divide. These include news following, scientific literacy, perceived social norms, and knowledge about the virus.

   The most critical factors appear to be trust in scientists and trust in Trump, which further highlights the politicization of COVID‐19 and, importantly, the antagonistic nature of these two beliefs. Efforts to change and, especially, disentangle these two attitudes have the potential to be effective interventions.

    

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2. Aligning Multidimensional Worldviews and Discovering Ideological Differences

   https://doi.org/10.18653/v1/2021.emnlp-main.396  

   Milbauer, Jeremiah ; Mathew, Adarsh ; Evans, James ( January 2021 , Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)

   The Internet is home to thousands of communities, each with their own unique worldview and associated ideological differences. With new communities constantly emerging and serving as ideological birthplaces, battlegrounds, and bunkers, it is critical to develop a framework for understanding worldviews and ideological distinction. Most existing work, however, takes a predetermined view based on political polarization: the “right vs. left” dichotomy of U.S. politics. In reality, both political polarization – and worldviews more broadly – transcend one-dimensional difference, and deserve a more complete analysis. Extending the ability of word embedding models to capture the semantic and cultural characteristics of their training corpora, we propose a novel method for discovering the multifaceted ideological and worldview characteristics of communities. Using over 1B comments collected from the largest communities on Reddit.com representing ~40% of Reddit activity, we demonstrate the efficacy of this approach to uncover complex ideological differences across multiple axes of polarization. 

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3. POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection

   https://doi.org/10.18653/v1/2022.findings-naacl.101  

   Liu, Y. ; Zhang X. ; Wegsman D. ; Beauchamp N. ; Wang, L. ( January 2022 , Findings of the Association for Computational Linguistics: NAACL 2022)

   Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios. 

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4. Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics

   Lee, JooYoung ; Wu, Siqi ; Ertugrul, Ali Mert ; Lin, Yu-Ru ; Xie, Lexing ( January 2022 , Proceedings of the International AAAI Conference on Weblogs and Social Media)

   The ideological asymmetries have been recently observed in contested online spaces, where conservative voices seem to be relatively more pronounced even though liberals are known to have the population advantage on digital platforms. Most prior research, however, focused on either one single platform or one single political topic. Whether an ideological group garners more attention across platforms and/or topics, and how the attention dynamics evolve over time, have not been explored. In this work, we present a quantitative study that links collective attention across two social platforms -- YouTube and Twitter, centered on online activities surrounding popular videos of three controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose several sets of video-centric metrics to characterize how online attention is accumulated for different ideological groups. We find that neither side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention time series unfold quicker for left-leaning videos, but span a longer time for right-leaning videos. Network analysis on the early adopters and tweet cascades show that the information diffusion for left-leaning videos tends to involve centralized actors; while that for right-leaning videos starts earlier in the attention lifecycle. In sum, our findings go beyond the static picture of ideological asymmetries in digital spaces and provide a set of methods to quantify attention dynamics across different social platforms. 

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5. The ‘Fairness Doctrine’ lives on? Theorizing about the Algorithmic News Curation of Google’s Top Stories

   https://doi.org/https://doi.org/10.1145/3372923.3404794  

   Kawakami, Anna ; Umarova, Khonzoda ; Huang, Dongchen ; Mustafaraj, Eni ( July 2020 , The 31st ACM Conference on HyperText and Social Media)

   When one searches for political candidates on Google, a panel composed of recent news stories, known as Top stories, is commonly shown at the top of the search results page. These stories are selected by an algorithm that chooses from hundreds of thousands of articles published by thousands of news publishers. In our previous work, we identified 56 news sources that contributed 2/3 of all Top stories for 30 political candidates running in the primaries of 2020 US Presidential Election. In this paper, we survey US voters to elicit their familiarity and trust with these 56 news outlets. We find that some of the most frequent outlets are not familiar to all voters (e.g. The Hill or Politico), or particularly trusted by voters of any political stripes (e.g. Washington Examiner or The Daily Beast). Why then, are such sources shown so frequently in Top stories? We theorize that Google is sampling news articles from sources with different political leanings to offer a balanced coverage. This is reminiscent of the so-called “fairness doctrine” (1949-1987) policy in the United States that required broadcasters (radio or TV stations) to air contrasting views about controversial matters. Because there are fewer right-leaning publications than center or left-leaning ones, in order to maintain this “fair” balance, hyper-partisan far-right news sources of low trust receive more visibility than some news sources that are more familiar to and trusted by the public. 

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