đŸ€– Must-Read Articles đŸ€–

Experimental Feature

Even just looking backwards a week, there are a lot more articles published than most of us could hope to read. We can always skim the titles and abstracts ourselves, but I wanted to test out some automation. The articles below, all of which can be found elsewhere on this site among other new publications, were chosen by Google Gemini Flash Thinking as "must-read" articles. The proper criteria are in the eyes of the beholder and Gemini doesn't apply my criteria without error. I may continue to refine the prompting based on experience and feedback. Like the rest of the site, this will update daily!

The field of communication is becoming less disruptive: analyses of citation data over four decades
Annals of the International Communication Association
Luling Huang, Kun Xu
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Innovation is fundamental to knowledge advancement in all realms of scholarship. The current study focuses on 2 forms of innovation in communication, as manifested via citation practices: consolidation and disruption. This study examines whether the field of communication has become consolidative or disruptive over time and projects the near-future trajectory of consolidation and disruption. Based on 92 communication journals and their citation data spanning from 1976 to 2022, our citation analyses of 52,294 focal publications suggested that communication research is overall disruptive. However, the level of disruption has been declining over time. Our linguistic analyses of article titles and abstracts further confirm the decreasing disruption in the field. Overall, this study suggests a deceleration in innovation within the field of communication, which serves as a sign that communication has been solidifying its status as an increasingly independent field, grounded in a growing body of shared intellectual legacy. We further interpreted the change in innovation from the perspectives of theory building, knowledge burden, journal scopes, academic platforms, and recent advances in artificial intelligence and large language models.
Why and when do media trust and political trust go hand in hand? A review of four explanations for the media–politics trust nexus
Annals of the International Communication Association
Nayla Fawzi, Nina Steindl
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There is robust evidence that media trust and political trust are weakly to moderately correlated with each other—but why is this so and under which conditions? By integrating current research on media trust and political trust, this paper reviews four explanations for the media–politics trust nexus. The first explanation sees media trust as a foundation for building political trust. The second explanation focuses on negative media coverage of politics that decreases political trust, which spills over to media trust. The third explanation relates to the similar origins of institutional trust, and the fourth explanation argues that media trust and political trust are mutually dependent. The paper then discusses the strength of the trust nexus and the different implications that result from low to high trust in both institutions.
Felt Time Scale (FTS): Measuring how people experience screen time
Communication Methods and Measures
Jana Dombrowski, Sabine Trepte
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Algorithmic News Content Personalization and Readers’ Attitudes
Digital Journalism
Yixue Wang, Aaron Shaw, Stephanie Edgerly, Darren Gergle, Nicholas Diakopoulos
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Unmasking the Mechanism: How News Media Consumption Drives Conspiracy Beliefs
Digital Journalism
Laura Jacobs, Peter Van Aelst
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Public Affairs Outlet Viewership, Partisanship, and Racial Resentment: An Analysis of Panel Data
Howard Journal of Communications
Patrick C. Meirick
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Developing and testing an AI-based risk information-seeking model (ARISM): A structural equation modeling approach
Information, Communication & Society
Soo Jung Hong
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Does trending across platforms popularize political topics? A cross-platform spillover framework of public attention
Information, Communication & Society
Yufan Guo, Cong Lin, Yuhan Li
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AI as a creator or a helper: how disclosure and source credibility shape public perception of political advertising messaging
International Journal of Advertising
Shahariar Khan Nobel, Rachel Esther Lim, Sujin Kim
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Familiarity, Similarity, Representativeness, and Naturalness: Cues Used to Assess Customized AI News Presenters
Journal of Broadcasting & Electronic Media
Elia Powers
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“Is This Fake News?” Examining the Antecedents of Generative AI Chatbot Use for Fact-Checking of News Online
Journal of Broadcasting & Electronic Media
Michael Chan
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Threat Level Midnight: Examining the Relationship Between Threatening Language and Engagement with Non-Partisan, Partisan, and Hyper-Partisan Media on Facebook
Mass Communication and Society
Eliana DuBosar, Jieun Shin
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Does Variety Matter? Young Adults’ News Platform Repertoires and Their Citizen Competence
Mass Communication and Society
Zhieh Lor, Jihyang Choi
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Personalized Persuasion Through Conversational AI: Can DeepSeek Change Perceptions of Genetically Modified Foods in China?
Media and Communication
Qi Xi, Jing Zeng, Zhanghao Li, Mike S. SchÀfer
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Conversational AI has become an influential intermediary in public communication. Emerging research on conversational AI highlights its potential to correct misconceptions and influence attitudes across domains. This study investigates the persuasive effects of personalized conversational AI, focusing on genetically modified foods in China. Employing a between-subjects factorial design, 813 participants engaged in dialogues with a DeepSeek-based chatbot. Participants were randomly assigned to one of four conditions, ranging from a non-personalized generic control to increasingly tailored approaches based on demographic information, risk perceptions, or a combination of both. Results indicate that while AI interactions significantly improve attitudes and willingness to consume genetically modified foods across all conditions, the additional persuasive effect of personalization was conditional. Only a personalization strategy combining demographics and risk perception yielded greater persuasive effects than the control, primarily among participants with positive risk perceptions. Furthermore, moderation analyses revealed a divergence in individual differences: Among participants with negative risk perceptions, while greater prior experience with AI and higher trust in science decreased the persuasive effects, higher AI knowledge facilitated greater attitude gains.
The Dynamic and Reciprocal Relationship Between Problematic Internet Use and Loneliness: A Longitudinal Study
Media Psychology
Eetu Marttila, Aki Koivula, Iina Savolainen, Anu Sirola, Atte Oksanen
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Negativity and Misinformation
Political Communication
Stuart Soroka, Christopher Wlezien
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Long‐run confidence: Estimating uncertainty when using long‐run multipliers
American Journal of Political Science
Mark David Nieman, David A. M. Peterson
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Researchers are often interested in the long‐run relationship (LRR) between variables where the dependent variable has dynamic properties. Though determining the long‐run multiplier (LRM) for an independent variable is straightforward, correctly estimating the significance of the LRM is often difficult, especially when time series are short and tests for series’ stationarity are uncertain. We propose a Bayesian framework for estimating the LRM by using a bounded prior on the lagged dependent variable to constrain estimates for dynamic processes to the plausible range of values arising from either stationary or integrated series, and then taking draws of the posterior distribution to summarize the credible region. Doing so provides direct estimates of the LRM and its uncertainty, even for short time series. We highlight the advantages of this approach via Monte Carlo experiments and replicate several studies to show that our method clarifies LRRs that were inconclusive using existing techniques.
Initiate and Elevate! How Political Parties Can Set an Agenda
American Political Science Review
DANIEL SANDVEJ ERIKSEN
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The study of political agenda setting is a cornerstone in political science. Within this literature, political parties are implicitly portrayed as being capable of proactively initiating discussions. However, this fundamental notion of party agency deserves further theoretical and empirical attention. In response, this article crafts a new model (the Issue Initiation Model) that opens the window into parties’ efforts to set an agenda and traces how they initiate and elevate their agenda. The model is tested on an original dataset covering more than 5.5-million tweets by political parties and MPs, coupled with over 750,000 news articles and 419,000 parliamentary questions in the United Kingdom and Denmark from 2015 to 2022. Results show how parties and their MPs can proactively redirect the attention of other actors through strategic planning and orchestrated actions. By theorizing and empirically testing the implicit notion that parties can proactively initiate discussions, this article has important implications for political agenda setting.
Partisan Expressive Responding: Lessons from Two Decades of Research
American Political Science Review
MATTHEW H. GRAHAM
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Research on partisan expressive responding suggests that the beliefs people express in surveys are more partisan than their underlying perceptions. This article examines the scope and importance of expressive responding through a meta-reanalysis of 44 studies from 25 articles. On average, treatments designed to reduce expressive responding shrink measured partisan bias by about 25%. Across the 242 survey questions in the data, treatments increase the correlation between the average Democrat’s and Republican’s beliefs from 0.81 to 0.86. Contrary to expectations derived from the two leading theories of expressive responding, misreporting (“cheerleading”) and congenial inference, there is no evidence that expressive responding increases in partisan identity strength or educational attainment. As research on expressive responding enters its third decade, greater emphasis on design-based tests of mechanisms may help build a firmer understanding of the nature and substantive importance of expressive responding—namely, whether the forces that produce expressive responding in surveys also shape real-world political judgments.
Examining Partisan Asymmetries in Fake News Sharing and the Efficacy of Accuracy Prompt Interventions
The Journal of Politics
Brian Guay, Adam Berinsky, Gordon Pennycook, David Rand
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How to Measure Public Support for Political Violence
Public Opinion Quarterly
Nathan P Kalmoe, Lilliana Mason
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With low but rising levels of violent political threats and violent acts by civilians in the United States, researchers increasingly want to measure violent public views—fearing the erosion of collective norms inhibiting violence while also regarding individual attitudes as a risk for rare violent action and more common forms of aggressive political behavior. But how should violent views be measured and interpreted? Drawing on our decade-plus researching these views with several dozen questions and a catalogue of related work by others, we provide a practical measurement guide for assessing violent political views with extensive new observational and experimental illustrations that also make important methodological and substantive contributions to the field. We provide considerations for choosing good measure(s): empirically informed measurement principles, general and specific question evaluations, a deep dive into question design, reliability, and validity, and more. The Supplementary Material also catalogues a century of violence questions and more published works.
Ideological Cues, Partisanship, and Prejudice Against LGBTQ Judges
Public Opinion Quarterly
Andrew R Stone, Tony Zirui Yang
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How does the gender and sexual identity of a prospective judge shape public support for their nomination? We build upon recent scholarship on instrumental inclusivity and argue that, after accounting for nominee ideology, Americans of all partisan stripes will penalize LGBTQ nominees. Using a conjoint experiment, we randomly vary a prospective Biden US Supreme Court nominee’s gender and sexual identity. Crucially, we also randomize the nominee’s ideology, enabling us to disentangle LGBTQ identity from the ideological signal it sends and differentiate between genuine and instrumental support for LGBTQ nominees. Contrary to recent findings suggesting that Democrats reward minority judges, we find that respondents from both parties penalize LGBTQ nominees. The magnitude of these effects—roughly 14 percentage points for transgender nominees and 8 percentage points for gay or lesbian nominees—is considerable and second only to shared partisanship. Our study underscores that ideological alignment does not necessarily foster genuine inclusivity for LGBTQ individuals and highlights the persistent challenges of representation for marginalized groups in an era of polarized judicial nominations.
Reassessing Support for Political Aggression and Violence in the United States
Public Opinion Quarterly
Scott Clifford, Lucia Lopez, Lucas Lothamer
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Recent events have driven a surge in scholarly attention to public support for political violence in the United States. Yet, research paints a conflicting picture about the levels and correlates of support for violence. We argue that these disagreements are partly due to researchers’ measurement choices. After reviewing common practices and identifying measurement challenges, we introduce a measure designed to overcome these problems that allows respondents to choose their target of aggression. Across multiple studies, we compare our measure to two common alternatives. While we find similarities, our measure uncovers substantially more support for aggression and violence, particularly among weak partisans, holding implications for the levels and correlates of support for aggression. Further, by design, our measure provides information about the type of aggression that is endorsed and the most common targets. We conclude with recommendations for researchers studying support for political aggression.
Can AI reflect public opinion? Evidence from replicating Hainmueller and Hopkins’ immigration experiment with LLMs
Computers in Human Behavior
Yajing Chen, Ming Lei, Zhanyu Liu, Man Tang, Jie She
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The effects of political advertising on Facebook and Instagram before the 2020 US election
Nature Human Behaviour
Hunt Allcott, Matthew Gentzkow, Ro’ee Levy, Adriana Crespo-Tenorio, Natasha Dumas, Winter Mason, Devra Moehler, Pablo Barberá, Taylor Brown, Juan Carlos Cisneros, Drew Dimmery, Deen Freelon, Sandra González-Bailón, Andrew M. Guess, Young Mie Kim, David Lazer, Neil Malhotra, Sameer Nair-Desai, Brendan Nyhan, Ana Carolina Paixao de Queiroz, Jennifer Pan, Jaime Settle, Emily Thorson, Rebekah Tromble, Carlos Velasco Rivera, Benjamin Wittenbrink, Magdalena Wojcieszak, Shiqi Yang, Saam Zahedian, Annie Franco, Chad Kiewiet de Jonge, Natalie Jomini Stroud, Joshua A. Tucker
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We study the effects of social media political advertising by randomizing subsets of 36,906 Facebook users and 25,925 Instagram users to have political ads removed from their news feeds for 6 weeks before the 2020 US presidential election. We show that most presidential ads were targeted towards parties’ own supporters and that fundraising ads were the most common. On both Facebook and Instagram, we found no detectable effects of removing political ads on political knowledge, polarization, perceived legitimacy of the election, political participation (including campaign contributions), candidate favourability and turnout. This was true overall and for both Democrats and Republicans separately.