iiinova — where your digital self keeps evolving

Ross Dahlke @rossdahlke.bsky.social · 03/09/2026

My MA student, Huang, and I find 16% of US adults report using Polymarket. Use is higher among those with lower incomes and less education, but similar across political parties. In both parties, use is correlated with lower civic knowledge and certain anti-democratic attitudes:

Key findings from a survey of U.S. adults about Polymarket. Any use was reported by 15.7%; 11.2% used it a moderate amount or more. Use was higher among Democrats (16.8%) and Republicans (18.0%) than Independents (10.8%); adults under 50 (about 23%) than older adults (6.5–7.0%); and Black (30.4%) and Hispanic respondents (20.1%) than White respondents (11.4%). Use declined as income and education rose and was somewhat higher among men than women. Users had varied demographic profiles. Compared with nonusers, users scored lower on a basic civics question and were more accepting of some forms of political rule-breaking and limits on debate or speech. These patterns generally appeared within each party.

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Six grouped bar charts show self-reported Polymarket use by age, income, gender, education, party, and race and ethnicity. “Any use” was reported by 23.0% of ages 18–29, 23.5% of ages 30–49, 6.5% of ages 50–64, and 7.0% of ages 65-plus. Use declined from 19.9% among households earning under $50,000 to 9.4% among those earning $150,000 or more, and from 19.6% among adults with a high-school education or less to 12.8% among those with a four-year or postgraduate degree. Rates were 17.7% for men and 14.2% for women; 16.8% for Democrats, 10.8% for Independents, and 18.0% for Republicans; and 11.4% for White, 30.4% for Black, and 20.1% for Hispanic respondents.

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Ross Dahlke @rossdahlke.bsky.social · 24/08/2026

Excited to be speaking at Predict 2026: The Prediction Market Conference in NYC in December! Would love to connect with other academics and practitioners attending predict2026.org

Predict 2026 homepage https://predict2026.org/

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Prof. Ross Dahlke, Professor, University of Wisconsin–Madison. Ross Dahlke studies social behavior and political communication through prediction markets.

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Ross Dahlke @rossdahlke.bsky.social · 01/06/2026

Me working on a paper with my grad students

USMNT meme with Poch coaching the team with a computer

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Ross Dahlke @rossdahlke.bsky.social · 27/05/2026

New work with @ryanmoore.bsky.social in @digitaljournalism.bsky.social examines Americans' exposure to Pink Slime websites--partisan websites that masquerade as local news--and compares consumption levels to untrustworthy and genuine local news websites doi.org/10.1080/2167...

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Ross Dahlke @rossdahlke.bsky.social · 03/04/2026

Most web browsing studies analyzing news and misinformation operate at the domain level. Work by me, @fangjingtu.bsky.social et al., scrapes the content from web visits to go beyond the source to the content level, finding significant topical and linguistic variation doi.org/10.1145/3757571

Screenshot of a paper titled “Contextualizing Misinformation: A User-Centric Approach to Linguistic and Topical Patterns in News Consumption,” authored by Ross Dahlke and colleagues. The abstract says the study uses web-browsing data from 1,240 U.S. adults during the 2020 election to compare misinformation and hard news. It finds that misinformation people consumed was generally easier to read, more negative in tone, and more morally framed, with substantial variation across topics and across groups such as older adults and Republicans.

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Two side-by-side horizontal bar charts compare topic distributions for hard news and misinformation. Hard news is led by general news at 35.9%, followed by U.S. electoral politics at 27.0%, social issues at 17.1%, COVID-19 at 14.2%, and health at 5.7%. Misinformation is much more concentrated in U.S. electoral politics at 53.0%, followed by social issues at 23.7%, COVID-19 at 11.6%, general news at 6.8%, and health at 4.9%.

Two stacked line charts show how topic shares changed over time from late August to early December 2020, with a vertical marker at Election Day 2020. In misinformation, U.S. electoral politics rises sharply in October and November and becomes the dominant topic around the election. In hard news, general news remains largest throughout, while U.S. electoral politics also spikes around Election Day before declining afterward.

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Two stacked line charts show how topic shares changed over time from late August to early December 2020, with a vertical marker at Election Day 2020. In misinformation, U.S. electoral politics rises sharply in October and November and becomes the dominant topic around the election. In hard news, general news remains largest throughout, while U.S. electoral politics also spikes around Election Day before declining afterward.

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Becca Lewis @beccalew.bsky.social · 26/03/2026

Cannot believe I got to work with such an incredible group of scholars on a topic that I care so deeply about! Check out our new article on the political economy of Alex Jones, wherein we find that his presentation and topic choices predicted his merch sales. www.tandfonline.com/doi/full/10....

tandfonline.com

Style and substance on The Alex Jones Show predict InfoWars sales: a multi-modal analysis of a media empire

Alex Jones, a prominent conspiracy theorist, has garnered substantial influence and wealth through his InfoWars media empire, which includes The Alex Jones Show and InfoWars.com. This study leverag...

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Ross Dahlke @rossdahlke.bsky.social · 30/03/2026

New from me, @yunkangyang.bsky.social @jolukito.bsky.social @jasong.bsky.social @m-dot-brown.bsky.social @beccalew.bsky.social: analyzing sales data released from InfoWar's court case, we find that certain styles (linguistic and auditory) used by Alex Jones on his radio show predict next-day sales

Screenshot of the title page of a journal article in Information, Communication & Society by Ross Dahlke and coauthors. The article is titled “Style and substance on The Alex Jones Show predict InfoWars sales: a multi-modal analysis of a media empire.” The abstract explains that the study combines daily InfoWars sales data from 2016 to 2018 with linguistic, auditory, and topical features from Alex Jones’s radio show and online articles, finding that some styles and topics predict next-day sales.

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Line chart showing daily InfoWars sales in dollars from January 2016 through December 2018. Sales are highly volatile, with frequent spikes, but generally rise from relatively low levels in early 2016 to a higher and more sustained range through 2017 and 2018, often around $100,000 to $300,000 per day, with occasional peaks approaching $1 million.

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Multi-panel figure showing daily trends in selected themes and styles in Alex Jones radio shows and InfoWars news articles from 2016 to 2018. The left column tracks radio show content including Power, Bio, Achieve, Focus Future, and Money; the right column tracks article content including Power, Achieve, Money, Anger, and Focus Future. Gray daily values are overlaid with smoothed trend lines, showing that some themes shift gradually over time while others remain fairly stable.

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Multi-panel figure showing daily trends in major topics in Alex Jones radio shows and InfoWars news articles from 2016 to 2018. Radio show panels include Nationalism, Politicians, Show Slogans, Promotions, and Fake News; article panels include Trump, Scientific Discoveries and Controversies, Media and Politics, Attacking Democrats, and Global Conflicts. Smoothed trend lines show modest but noticeable changes over time, including persistent attention to Trump and politics in articles and nationalism and political messaging in radio content.

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Ross Dahlke @rossdahlke.bsky.social · 18/03/2026

If you're around Madison, come to Ryan's talk this Friday!

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Ross Dahlke @rossdahlke.bsky.social · 14/03/2026

The first ever graduate seminar I ever took was basically a whole semester on Habermas, and it’s shaped my thinking about communication, media, and the public sphere since. RIP to a legend

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Ross Dahlke @rossdahlke.bsky.social · 05/03/2026

Thanks to University of Iowa School of Journalism and Mass Communication for hosting me for their Communication and Media Colloquium series and to Sang Jung Kim, @bingbingzhang.bsky.social and Jamil Marques for inviting me to your classes!

Ross Dahlke presents in a lecture room, gesturing toward a slide titled “Ideological Information Environments” with bullet points comparing the open web and personal messaging.

Ross Dahlke points at a projected slide with two bar charts comparing “Web Browsing” vs. “Personal Messaging,” showing congruent vs. cross-cutting content for Democrats and Republicans.

Ross Dahlke speaks at the front of a conference room while a slide shows scatterplots of “Percentage of Messages by Type,” with colored dots representing different messaging platforms.

Ross Dahlke leads a small-group meeting in the Moeller Lab “Media Research” room, seated at a conference table with laptops while a wall screen displays a simple diagram about “Untrustworthy Websites.”

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Ross Dahlke @rossdahlke.bsky.social · 28/02/2026

Thanks to UW IT for inviting me to present my Polymarket data collection pipeline at Google Cloud Platform Research Day. Pipeline enabled by cloud computing: $40B in volume, 500M trades, and 1.25M comments with continuous collection. Preprints and public datasets coming soon!

Via this pipeline we have collected $37.8B in trading volume

Electoral prediction markets: a quantitative description

Two scatter plots showing the relationship between trades and comments. R = 0.28

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Ross Dahlke @rossdahlke.bsky.social · 24/12/2025

Happy holidays from the Dahlke-McComas family! @lydiamccomas.bsky.social

Happy holidays from Lydia, Ross, and Boomer

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Ross Dahlke @rossdahlke.bsky.social · 08/11/2025

Who did it better? Mamdani or Soglin?

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Ross Dahlke @rossdahlke.bsky.social · 24/09/2025

I am excited to present as part of the #TSRConf 2025 Conference Proceedings of the Journal of Online Trust and Safety at @stanfordcyber.bsky.social. Happy to have this paper published doi.org/10.54501/jot...

Ross Dahlke. Speaker. Join me at the Trust & Safety Research Conference. September 25-26. Stanford University Alumni Center.

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Journal of Online Trust & Safety. Volume 3, Issue 1. September 2025. ISSN: 2770-3142.

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Vol. 3 No. 1 (2025)
Untrustworthy Website Exposure and Election Beliefs: Selective Exposure and Ideological Asymmetry
Peer-reviewed Articles
https://doi.org/10.54501/jots.v3i1.250
Published 2025-09-12
Authors
Ross Dahlke
University of Wisconsin-Madison
https://orcid.org/0000-0002-5179-2525
Jeffrey Hancock
https://orcid.org/0000-0001-5367-2677

Keywords
Digital trace data
double machine learning
data science
false beliefs
causal inference
Categories
Conference Proceedings
How to Cite
Dahlke, R., & Hancock, J. (2025). Untrustworthy Website Exposure and Election Beliefs: Selective Exposure and Ideological Asymmetry. Journal of Online Trust and Safety, 3(1). https://doi.org/10.54501/jots.v3i1.250

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Figure 1. Timeline of data collection.

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Ross Dahlke @rossdahlke.bsky.social · 31/07/2025

In an experiment with ~4% of the electorate of Cyprus, personalized affinity information increased electoral participation and encouraged party consideration but did not shift voting intentions, finds Ioannidis doi.org/10.1080/1933...

Screenshot of a journal article titled “The power of alignment: how personalized information shapes voter decisions” by Nikandros Ioannidis in the Journal of Information Technology & Politics. The abstract summarizes a field experiment using a Voting Advice Application (VAA) during Cyprus’s 2021 elections, finding that personalized political information boosted participation by up to 10 percentage points, but did not meaningfully shift vote intention toward more ideologically congruent parties.

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Flowchart of the experimental design. Participants (N ≈ 17,000) were randomly assigned to one of five groups: control, Party Rankings, Map Eco-Social, Map Eco-CyProb, or Map CyProb-Social. All groups were asked about demographics, policy preferences, and past vote. Treatments received personalized VAA output and were later surveyed on vote intentions.

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Top panel shows number of VAA users per day from May 21–30, 2021, peaking on May 22. Bottom panel shows cumulative number of users, rising steadily across the same period. Below, a horizontal bar chart shows sample VAA output: seven parties with positive affinity scores (yellow bars) and two parties with negative affinity (red bars). Scores range from −37 to +40.

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Coefficient plot from probit models predicting election participation across four treatment arms: Party Rankings, Map Eco/Social, Map Eco/CyProb, and Map Cyprob/Social. All treatments show positive effects on turnout likelihood compared to control, with Party Rankings showing the largest and most precise effect.

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Ross Dahlke @rossdahlke.bsky.social · 30/07/2025

While an urban-rural divide persists in policy priorities, partisan affiliation is a stronger predictor of priorities than geographic location, finds Yildirim & Solvig in @psrm.bsky.social doi.org/10.1017/psrm...

Screenshot of a journal article titled “The urban–rural divide in policy priorities across time and space” by Yildirim and Solvig in Political Science Research and Methods. The abstract summarizes an analysis of 850 U.S. surveys (1939–2020) showing persistent but modest urban–rural differences in top policy concerns, with partisan identity more predictive than geography. Keywords include partisanship, geography, and public opinion.

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Six line graphs showing urban–rural gaps in prioritization of budget deficit, agriculture, moral values, immigration, economy, and tax issues from 1960 to 2020. Rural residents more often prioritize budget, agriculture, values, and immigration. Urban and rural trends on economy and tax converge more closely. Rural lines are generally above urban, indicating stronger issue salience.

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Six-panel plot showing how partisanship and residence jointly shape issue priorities over time (1960–2020). Rural Republicans rank budget, values, and immigration highest. Urban and rural Democrats differ little from each other and show lower prioritization of conservative-coded issues. Economy and tax are prioritized similarly across groups, with convergence over time.

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Six-panel plot showing partisanship × geography effects on civil rights, crime, education, foreign policy, health, and drugs (1960–2020). Urban/rural gaps are small relative to partisan gaps. Republicans (urban and rural) consistently prioritize crime and drugs more than Democrats. Democrats emphasize civil rights, education, and health more. Foreign policy converges by 2020.

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Ross Dahlke @rossdahlke.bsky.social · 29/07/2025

Experimental manipulation of threat exposure has a null effect on ideological conservatism, finds @abbycassario.bsky.social et al. osf.io/preprints/ps...

Screenshot of study abstract titled “Does threat increase conservatism?” summarizing three large U.S. experiments (Ns = 1000, 889, 843) testing threat effects on ideology. Despite successful threat manipulations, no effects are found on conservatism or personality × threat interactions. Concludes field should move beyond threat-based explanations.

Bar plots from three studies testing effects of threat on ideology. Each plot shows coefficient estimates for two threat conditions vs. control across ideological outcomes. Study 1 shows null effects on global ideology, healthcare, and economic policy. Study 2 adds measures like Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO), also showing no threat effects. Study 3 adds race threat and race policy; again, no consistent significant effects.

Two panels of coefficient plots from Studies 2 and 3 showing threat effects on specific policy attitudes (e.g., abortion, immigration, guns). Each dot represents a treatment effect estimate vs. control. Across dozens of items, no consistent ideological shifts emerge in response to unemployment, healthcare, or race threats.

Interaction plots from all three studies testing if personality (openness, conscientiousness) moderates threat effects on ideology. Across economic, global, and healthcare ideology—as well as RWA and SDO—no consistent threat × personality interactions emerge. One weak effect in Study 2 flagged for negative bias, but generally null.

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Ross Dahlke @rossdahlke.bsky.social · 26/06/2025

Excited to be on this panel discussing surveillance capitalism today!

World Salon Surveillance Capitalism: Who owns your data? www.world-salon.com

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Center for Communication & Civic Renewal @cccr.bsky.social · 17/06/2025

🚨 CCCR has a new survey report out today! 🚨 "100 Days Under Trump: Public Reactions to Attacks on American Governance & Institutions" The report draws on our Apr/May YouGov panel survey of US adults, following our Oct 2024 survey w/ recontacts + a sample refresh. 1/ cccr.wisc.edu/wp-content/u...

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Computational Methods Division of ICA @icacm.bsky.social · 11/06/2025

So many great Computational Methods sessions coming up at #ICA25!! Check them out, and we look forward to seeing you there! 👇👇👇

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Ross Dahlke @rossdahlke.bsky.social · 10/06/2025

People negatively evaluate AI moderators that use emotional arguments rather than rational arguments, finds Silver, Williams-Ceci, & @informor.bsky.social doi.org/10.1145/3706...

AI is Perceived as Less Trustworthy and Less Effective when Using Emotional Arguments to Moderate Misinformation

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Figure 1. Perceived quality by moderator identity and argument type
Three bar plots show perceived credibility, informativeness, and transparency as a function of moderator identity (human vs. AI) and argument type (rational vs. emotional). Rational arguments are rated significantly higher than emotional ones across all dimensions (p < .001), and human moderators are rated higher than AI within each argument type. Error bars reflect 95% confidence intervals.

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Two bar plots display mean perceived trustworthiness. The left panel shows human moderators are rated more trustworthy than AI (p = .004). The right panel shows rational arguments are rated as more trustworthy than emotional ones (p = .005). Error bars represent 95% confidence intervals.

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Figure 3. Efficacy across misinformation contexts by moderator and argument type
Five panels show perceived efficacy of moderation across different misinformation scenarios (GMO, general, aligned beliefs), comparing human and AI moderators using rational vs. emotional arguments. Human moderators using rational arguments are rated as significantly more effective in several contexts (p < .05), with AI-emotional combinations rated lowest overall.

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Ross Dahlke @rossdahlke.bsky.social · 07/06/2025

When Community Notes inform users about falsehoods on X posts, the replies to the post have more negativity, anger, distrust, and moral outrage, finds Chuai ‪et al. dl.acm.org/doi/10.1145/...

Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media

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Figure 2. Summary statistics for misleading posts and replies before and after fact-check display
Panel (a) shows a time series line chart of the rolling average number of misleading source posts with community notes from January to April 2023, trending upward over time. Panel (b) displays two horizontal bars comparing the proportion of positive vs. negative sentiment in source posts, with negative sentiment dominating. Panel (c) contains horizontal bars comparing six emotions (anger, disgust, fear, joy, sadness, surprise) in source posts, with anger and disgust most prevalent. Panels (d–f) show CCDFs: (d) total reply count per post, (e) post age at the time of community note display, and (f) the proportion of replies that occurred before vs. after the display. Panels (g–i) present line charts tracking hourly averages of reply sentiment/emotion (negative, anger, surprise) from 16 hours before to 16 hours after note display, showing modest increases after the note appears.

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Figure 4. Predicted effects of note display on sentiments and emotions in replies
Panels (a–h) are separate line charts with scatter overlays, each representing one sentiment or emotion. The x-axis spans from -16 to +16 hours relative to the display of a community note. Each chart includes a blue line for pre-display averages, a yellow line for post-display averages, and a shaded 95% confidence interval around the predicted effect. Predicted increases are most visible in anger (c), disgust (d), and negative sentiment (b), with flat or minimal changes for joy (f), sadness (g), and positive sentiment (a).

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Figure 6. Regression estimates for reply sentiment and emotion after community note display
Eight coefficient plots (panels a–h) showing the estimated effects of key predictors—including whether a note was displayed, post age, and source sentiment—on each type of sentiment or emotion in replies. The estimates are split by whether the source post was political or not, with red and blue error bars representing separate groups. Vertical lines represent the 95% confidence intervals around each coefficient. Displaying a community note is positively associated with anger, disgust, and negative sentiment, especially for political posts.

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Ross Dahlke @rossdahlke.bsky.social · 05/06/2025

Higher problematic social media use is correlated with engaging false information online, finds Meshi & Molina t.co/NokaLxCGVn

Problematic social media use is associated with believing in and engaging with fake news

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Figure: Interaction between problematic social media use and engagement with false vs. real content.
Five panels (A–E) show line graphs with problematic social media use on the x-axis and outcome variables on the y-axis: credibility (A), intention to click (B), like (C), comment (D), and share (E). Each graph shows separate lines for false (blue dashed) and real (red solid) content. In panels A, B, and E, false content shows stronger increases in credibility and engagement as problematic use rises, with significant or marginal interaction effects. In panels C and D, both content types rise similarly, with only main effects significant. Results suggest that problematic social media use is associated with higher belief in and engagement with false news, more so than real news in some cases.

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Ross Dahlke @rossdahlke.bsky.social · 05/06/2025

Those high in need for cognition and cognitive reflection ability are more receptive to fact checks, finds Lee & Chung doi.org/10.1080/2167...

Thinking Hard, Thinking Smart: How News Users’ Cognitive Traits Guide Their Responses to Fact-Checks

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A flowchart diagram illustrating a randomized controlled experiment with three arms: (1) a treatment group split into two subconditions—Forewarning and No-Forewarning—and (2) a Control group.
• Participants in the treatment subgroups receive either a forewarning or a filler before viewing four news articles, each accompanied by a fact-check.
• Control group participants receive filler content, followed by the same four news articles without fact-checks.
• All participants complete a post-test questionnaire at the end.

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A bar chart with the y-axis labeled “Perceived Truthfulness” (range 0 to 6) and four x-axis groups representing combinations of two traits: Need for Cognition (NFC: high or low) and Cognitive Reflection (CR: high or low).
• Each group contains two bars—dark gray for “Fact-check True” and light gray for “Fact-check False.”
• In all groups, True-rated items are perceived as more truthful than False-rated ones, but the difference is largest for participants high in both NFC and CR.
• Differences in perceived truthfulness between fact-check conditions diminish among participants low in NFC or CR.

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Four path models labeled (a) through (d), each showing a mediation analysis by trait group:
(a) High NFC × High CR
(b) High NFC × Low CR
(c) Low NFC × High CR
(d) Low NFC × Low CR
• Each diagram includes three variables—Fact-check (0=False, 1=True), Perceived Truthfulness, and Sharing Intention—connected by arrows with regression coefficients.
• In all four panels, Fact-check strongly predicts Perceived Truthfulness (significant in all groups).
• Perceived Truthfulness significantly predicts Sharing Intention in all models.
• Direct effects of Fact-check on Sharing Intention are only significant in panel (b), where a negative coefficient suggests that High NFC but Low CR participants reduce sharing when content is marked false.

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Ross Dahlke @rossdahlke.bsky.social · 03/06/2025

In a political era of Super PACs, congressional candidates seek to maintain control over their visual image through visual "b-roll", effectively subsidizing outside organizations, finds ‪@gfoysutherland.bsky.social‬ doi.org/10.1177/1532...

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Table 1: Visual Resource Provision by Chamber as a Proportion of Total Races, 2018-2022 Cycles.

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Table 4, Breakdown of Advertising Employing Candidate/Party-Provided Visual Resources 2018-2020.

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Table 2, Division of Red Boxing/Visual Resource Permission by Candidate in Cycle 2018-2022.

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Ross Dahlke @rossdahlke.bsky.social · 02/06/2025

Fascinating look at decentralized, multi-directional propaganda efforts in China by Lu et al. doi.org/10.1111/ajps...

Decentralized propaganda in the era of digital media: The massive presence of the Chinese state on Douyin
Yingdan Lu, Jennifer Pan, Xu Xu, Yiqing Xu
First published: 23 May 2025 https://doi.org/10.1111/ajps.12990

Horizontal stacked bar chart with three rows comparing the share of six content categories in Douyin videos. For “Trending videos (non-regime accounts)” the bar is ≈95 % blue, showing that non-regime trending content is almost entirely entertainment/sensational. The two rows for regime accounts (“Trending videos” and “All videos”) display a much more diverse palette: roughly 35–40 % moral-society (salmon), 20–25 % pink announcements, 20 % blue entertainment, and smaller slices of orange nationalism, dark-red party-line propaganda, and grey other content. The x-axis spans 0–100 % share of videos.

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Horizontal stacked bar chart with four rows—central, provincial, city, and county government Douyin accounts—each broken into the same six categories. Across all levels, moral-society posts (salmon) form the largest block (≈40–50 %), followed by pink announcements. Entertainment (blue) and “other” (grey) grow slightly from central to county level, while nationalism (orange) and party-line propaganda (dark red) occupy small (<15 %) but visible segments throughout.

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Two proportional rectangles connected by arrows illustrate bidirectional reposting.
• Top rectangle labelled “Central videos with local matches”: 59 514 central-origin videos, split into 32 930 grey (central videos that appear in local feeds) and 26 584 blue (central originals that stay central only). A thick blue arrow points downward, indicating central content flowing to local accounts.
• Bottom rectangle labelled “Local videos with central matches”: 59 514 local-origin videos that re-appear on central accounts, broken into 12 458 yellow (province), 13 076 orange (city), 7 396 brown (county), and 26 584 grey (central re-uploads). Three upward arrows, color-matched to the provincial, city, and county segments, represent local content travelling upward to the central level.

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Ross Dahlke @rossdahlke.bsky.social · 01/06/2025

Access to high-speed internet increases addictive internet usage, reduces time allotted to sleep, homework, and social interactions, and leads to increases in mental health diagnoses and suicides, among adolescents in Spain, finds @estherarenasarroyo.bsky.social et al. doi.org/10.1016/j.jh...

High Speed Internet and the Widening Gender Gap in Adolescent Mental Health: Evidence from Spanish Hospital Records*

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Map of Spanish provinces shaded by quintiles of fiber-optic broadband penetration. Darker shades indicate higher fiber penetration (first quintile), while lighter shades indicate lower penetration (fifth quintile). Provinces like León, Málaga, and Castellón fall in the highest quintile of fiber penetration, while provinces such as Madrid, Cuenca, and the Balearic Islands are in the lowest.

Two-panel figure showing the relationship between fiber penetration and adolescent mental health issues. Panel a displays a scatterplot of province-year residuals for mental health against residuals for fiber penetration, showing a slightly positive trend. Panel b presents a fixed effects regression showing a positive association between higher fiber penetration (in five categories) and behavioral and mental health problems, with error bars indicating statistical uncertainty.

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Eight-panel event study graphs showing estimated effects of fiber rollout on various outcomes from 2009 to 2019. Panel a shows increasing mental health problems after 2014. Panel b shows a decreasing trend in population density. Other panels include population, GDP, GDP per capita, an interaction term (GDP per capita × population density), divorce rate, and employment rate—none showing consistent significant trends, reinforcing the specificity of the mental health effects.

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Ross Dahlke @rossdahlke.bsky.social · 30/05/2025

A majority of people follow "costly" rules, even in settings in which they are anonymous, alone, and violations are harmless because of respect for rules and social expectations, even though rule violation is moderately contagious, finds Gächter et al., www.nature.com/articles/s41...

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Figure 1: People tend to conform to an arbitrary rule against their self-interest, even a stylized, asocial, and unforced rule stated by the experimenter.

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Figure 2 rules generate social expectations and conditional conformity with them, even in a minimalist setup.

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Figure 3: Rule violations are contagious, but rule-following remains high.

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Ross Dahlke @rossdahlke.bsky.social · 29/05/2025

Some really fascinating articles in this new issue edited by @lindsaypalmer.bsky.social

Editor’s Note
Lindsay Palmer
University of Wisconsin-Madison
School of Journalism and Mass Communication

Volume 27 Issue 2, June 2025

Editor’s Note
Editor’s Note
Lindsay Palmer
Free accessEditorialFirst published May 15, 2025pp. 88–90

Editor’s Note
Commentaries
A Critical Time for Critical Race Theory Research
Meredith D. Clark
Free accessArticle commentaryFirst published May 15, 2025pp. 91–102

A Critical Time for Critical Race Theory Research
A Seat at the Table: Struggling for Access as an Asian American Race and Media Scholar
David C. Oh
Free accessArticle commentaryFirst published May 15, 2025pp. 103–110

A Seat at the Table: Struggling for Access as an Asian American Race and Media Scholar
Research Beyond the “Two Class Theory”
Melita M. Garza
Free accessArticle commentaryFirst published May 15, 2025pp. 111–118

Research Beyond the “Two Class Theory”
Academic Writing and Strategic Activist Multiplicity
Lori Kido Lopez
Free accessArticle commentaryFirst published May 15, 2025pp. 119–126

Academic Writing and Strategic Activist Multiplicity
Complicated Utopias: Latinx in Mainstream Media
Angharad N. Valdivia
Free accessArticle commentaryFirst published May 15, 2025pp. 127–137

Complicated Utopias: Latinx in Mainstream Media
Researching Resistance: The Challenges and Responsibilities of Documenting Race, Media, and Justice
Allissa V. Richardson
Free accessArticle commentaryFirst published May 15, 2025pp. 138–145

Researching Resistance: The Challenges and Responsibilities of Documenting Race, Media, and Justice
Indigenizing Mainstream News Coverage of Native Americans
Patty Loew
Free accessArticle commentaryFirst published May 15, 2025pp. 146–155

Indigenizing Mainstream News Coverage of Native Americans
On Resisting in Media Studies
Cristina Mislán
Free accessArticle commentaryFirst published May 15, 2025pp. 156–165

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Ross Dahlke @rossdahlke.bsky.social · 01/04/2025

Across four experiments in two countries, broken campaign promises decrease domain-specific evaluations but not overall performance, have limited effects on those with strong priors, and are downplayed by ingroup members, finds @alonzoizner.bsky.social & Amsalem doi.org/10.1177/1940...

When Do Broken Campaign Promises Matter? Evidence From Four Experiments. Campaign promises are a central mechanism for voters to hold politicians accountable, and information about their breakage or fulfillment features prominently in the media during election campaigns. Despite the importance of campaign promises, previous research yields conflicting expectations regarding their influence on citizens. Some theories suggest citizens vote based on policy performance and, therefore, consistently penalize actors who break their promises. Other theoretical accounts, however, argue that exposure to information during election campaigns often has minimal effects on citizens due to strongly held prior beliefs and partisan motivations. The goal of the current study is to address these competing claims by systematically testing the conditions under which citizens penalize politicians for breaking promises. We conducted four experiments (total N = 7,030), three of them preregistered.

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Table 1. Table showing an overview of four experiments. Studies 1 and 3 involved U.S. participants; Study 2 used Israeli participants; Study 4 used U.S. participants with a fictitious leader. Studies varied by promise type (real or fictitious), leader (e.g., Biden, Trump, Netanyahu, fictitious Paul Miller), and issue type (partisan or bipartisan). Fielding dates ranged from March 2021 to February 2022, with sample sizes between 1,354 and 1,942. Note indicates U.S. participants were recruited via Lucid and Israeli participants via iPanel.

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Figure 1. Two side-by-side dot-and-whisker plots show estimated treatment effects from four studies on three outcomes—policy approval, general approval, and warmth—separately for ingroup (left panel) and outgroup (right panel) targets. Each estimate is color-coded by study (Study 1–4) and includes a 95% confidence interval. In both panels, ingroup and outgroup effects from Studies 1–3 cluster near zero or positive, while Study 4 consistently shows negative effects across all outcomes, especially for warmth and policy approval. The x-axis shows effect sizes from -0.6 to 0.6. A vertical dashed line marks zero.

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Figure 2. Two side-by-side dot-and-whisker plots show effects of four studies on two cognitive mechanisms—Decoupling and Rationalization—for ingroup (left) and outgroup (right) evaluations. Each study (1–4) is color-coded and includes 95% confidence intervals. In the ingroup panel, all studies show small positive effects for both mechanisms, especially Study 1. In the outgroup panel, effects are generally near zero, with confidence intervals overlapping the null. X-axis represents effect size from -0.2 to 0.2; a vertical dashed line indicates zero.

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Ross Dahlke @rossdahlke.bsky.social · 31/03/2025

Both "cheap" and "deep" fakes suggesting a sex, corruption, or prejudice scandal caused reputational damage for an innocent politician, but a journalistic fact-check reduced the effect, finds @vioreladan.bsky.social doi.org/10.1177/1940...

Deepfakes as a Democratic Threat: Experimental Evidence Shows Noxious Effects That Are Reducible Through Journalistic Fact Checks. Concerns have been raised over AI-generated deepfakes and their impact on democracy. Unlike earlier forms of disinformation relying on text or traditional video-editing techniques (cheapfakes), deepfakes employ artificial intelligence, provoking speculations that they may be even more persuasive and harder to debunk. Using an experiment with a multiple-message design (N = 2,085), we found that fake videos suggesting a sex, corruption, or prejudice scandal—but not text-only fakes—elicited substantial reputational damage for an innocent politician, regardless of whether the underlying technique was “cheap” or “deep.” This was visible in altered attitudes, emotions, and voting intentions. However, exposure to a journalistic fact-check substantially reduced and even eliminated the detrimental effects. These findings have important implications for our theoretic

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Figure 1. Three bar plots (Panels A, B, and C) display the effects of different message formats on attitudes, voting intentions, and negative emotions. Each panel shows mean values with error bars across seven conditions: Control, Text, Cheapfake, Deepfake Basic, Deepfake Moderate, Deepfake Advanced, and Authentic.

Panel A (Attitudes): The highest attitudes are reported in the Control and Text conditions (around 4.5), followed by lower ratings in the Cheapfake condition and all Deepfake and Authentic conditions (around 3.0–3.2), indicating a drop in attitudes as the message becomes more visually manipulated.

Panel B (Voting Intentions): Voting intentions are highest in the Control and Text conditions (around 4.0), while the Deepfake and Authentic formats show lower intentions (around 2.8–3.0), with the lowest in Deepfake Basic and Deepfake Moderate.

Panel C (Negative Emotions): The Control group reports the lowest negative emotions (around 2.7), while all other formats, particularly

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Figure 2. Figure 2. Three line graphs (Panels A, B, and C) show the effects of format and fact-checking on attitudes, voting intentions, and negative emotions. The x-axis in all panels includes six message formats: Text, Cheapfake, Deepfake Basic, Deepfake Moderate, Deepfake Advanced. Two groups are shown: No Fact-Checking (black circles with solid lines) and Fact-Checking (black squares with solid lines).

Panel A (Attitudes): Attitudes are highest in the Text format for both groups. Without fact-checking, attitudes decline substantially across deepfake conditions, remaining lowest in Deepfake Basic and Moderate. With fact-checking, attitudes remain relatively stable across formats and higher than the No Fact-Checking group, especially in Deepfake Advanced.

Panel B (Voting Intentions): Voting intentions follow a similar pattern. The No Fact-Checking group sees sharp declines across formats, with the lowest voting intentions in Deepfake Advanced. The Fact-Checking group maintains high

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Ross Dahlke @rossdahlke.bsky.social · 31/03/2025

News authentication--proactive verification of news--is more prevalent in the U.S. and Hong Kong than in the Netherlands, with political efficacy and institutional trust being individual-level predictors, finds @qfzhu.bsky.social Peng & Zhang doi.org/10.1177/1940...

How Do Individual and Societal Factors Shape News Authentication? Comparing Misinformation Resilience Across Hong Kong, the Netherlands, and the United States

Table summarizing standardized coefficients from structural equation models predicting interpersonal and institutional authentication across Hong Kong (HK), Netherlands (NL), and United States (US). Key predictors include news consumption, political interest, political efficacy, and institutional trust—each positively associated with both forms of authentication. Age is negatively associated. In HK, conservative ideology predicts lower authentication; this effect is not significant in NL or US. Goodness-of-fit indices (CFI ≈ 0.83–0.86, RMSEA = 0.07–0.08) suggest acceptable model fit. Sample sizes range from 1,562 (HK) to 2,180 (US).

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Ross Dahlke @rossdahlke.bsky.social · 30/03/2025

Fascinting new study in 14 countries across Asia examining the media trust gap by Guo & @yuzhelei.bsky.social doi.org/10.1177/1940...

Despite the increasing reliance on online media for news consumption, people generally exhibit lower levels of trust in online news relative to traditional media. To explain the preference disparities in media trust and their potential cross-national variations, this article examines individuals’ trust gap between newspapers and Internet news across 14 countries and regions in East, South, and Southeast Asia. Drawing on nationally representative data and other country-level data (2018–2021), we test two underlying mechanisms, political trust transfer and alternative information orientation, that account for the media trust gap, as well as their boundary conditions. Multilevel analysis reveals that political trust positively correlates with people’s relative trust in newspapers, which is pronounced in societies with lower levels of polarization and limited press freedom. Besides, using the Internet and social media as the main channels of political information seeking may increase people’s relative trust in Internet news, especially in societies with higher levels of press freedom and political polarization. Our findings offer systematic explanations for news trust preferences by combining political characteristics and their contextual conditions, which have implications for understanding today’s media trust crisis.

Figure 1 is a bar and point plot comparing trust in newspaper and Internet news across 14 Asian societies. Trust levels are shown for each country with two markers: a black circle for newspapers and a black triangle for Internet news. Countries include Myanmar, Mainland China, India, Japan, Malaysia, Singapore, Mongolia, Indonesia, Thailand, Philippines, Vietnam, South Korea, Taiwan, and Hong Kong. The gap between the two points for each country represents the “trust gap,” with newspaper trust generally higher than Internet news trust, especially in Myanmar and China. The smallest or reversed gaps are seen in Taiwan and Hong Kong.

Table 2 presents results from three multilevel regression models predicting the media trust gap (defined as newspaper trust minus Internet news trust). Model 1 includes only fixed effects, Model 2 adds controls, and Model 3 includes interaction terms. Key predictors include political trust (positive, significant in all models), alternative information orientation (negative and significant in Models 1 and 2), and several significant interaction terms with press freedom and political polarization in Model 3. Model fit improves across models, as shown by decreasing AIC and BIC values. Marginal and conditional R² increase from Model 1 (0.035/0.112) to Model 3 (0.059/0.222), indicating better explanatory power. The note clarifies that coefficients are unstandardized and controls include various demographic and political variables. Asterisks denote significance at ***p < .001.

Two marginal effects plots show how the effect of political trust on the media trust gap varies by (a) press freedom and (b) political polarization. The y-axis in both panels is labeled “Marginal Effect of Political Trust.”

In Panel (a), the x-axis represents press freedom, ranging from 0.2 to 0.8. The marginal effect of political trust on the media trust gap decreases as press freedom increases. The slope is negative and crosses the zero line, with a shaded gray area indicating 95% confidence intervals. A histogram at the bottom shows the distribution of press freedom across countries.

In Panel (b), the x-axis represents political polarization, ranging from approximately -1.5 to 3.5. The marginal effect of political trust decreases as polarization increases, with a consistently positive but declining slope. Again, shaded areas indicate 95% confidence intervals, and a histogram at the bottom displays the distribution of polarization scores across countries.

Two marginal effects plots illustrate how the effect of alternative information orientation on the media trust gap changes depending on (a) press freedom and (b) political polarization. The y-axis in both panels is labeled “Marginal Effect of Alternative Information Orientation,” with values decreasing from 0 at the top to more negative values at the bottom.

In Panel (a), the x-axis shows levels of press freedom from 0.2 to 0.8. As press freedom increases, the marginal effect of alternative information orientation becomes more negative, suggesting that in countries with greater press freedom, the negative association between alternative information orientation and trust in mainstream media is stronger. A shaded area around the line represents the 95% confidence interval. A histogram below the x-axis displays the distribution of press freedom across countries.

In Panel (b), the x-axis represents political polarization ranging from approximately -1.5 to 3.5. The marginal effect of alternative information orientation becomes more negative as political polarization increases, again indicating a stronger negative effect in more polarized contexts. A histogram at the bottom shows the distribution of polarization values. The shaded area reflects 95% confidence intervals around the trend line.

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Ross Dahlke @rossdahlke.bsky.social · 29/03/2025

Sudden collective economic shocks can increase far-right vote share, with preexisting public service deprivation moderating the effects, finds @simonecremaschi.bsky.social Bariletto @catherinedevries.bsky.social in the case of a plant disease epidemic in Italy doi.org/10.1017/S000...

Without Roots: The Political Consequences of Collective Economic Shocks. While an abundance of scholarly work investigates how economic shocks influence the political behavior of affected individuals, we know much less about their collective effects. Exploiting the sudden onset of a plant disease epidemic in Puglia, Italy—where the plant pathogen Xylella fastidiosa devastated centuries-old olive groves—we explore the collective effects of economic shocks. By combining quantitative difference-in-differences analysis of municipal data with a novel case selection strategy for qualitative fieldwork, we document the hardship caused by the outbreak, and estimate a 2.2-percentage-point increase in far-right vote share. We show that preexisting public service deprivation moderates the shock’s political consequences through a community narrative of state neglect. These findings highlight that preexisting community conditions shape the political consequences of economic shocks, and that plant...

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Figure 1. Four-panel figure titled “The Xylella Outbreak” depicting spatial and temporal aspects of infection in the Puglia region of Italy.
(a) Map of Italy highlighting Puglia, with treatment and control areas marked in red and yellow, respectively.
(b) Map of olive cultivation in 2010 across Puglia, with municipalities shaded by the percentage of land used for olive cultivation, from 10% (yellow) to 40% (dark blue).
(c) Map showing timing of Xylella infection by municipality, with colors indicating the date of infection from April 2014 (dark blue) to May 2019 (light yellow), and gray for not infected.
(d) Sequence plot of infection timing by municipality ordered by latitude (Gallipoli to Bari), showing declared infection dates over time from 2014 to 2022. Vertical lines mark the outbreak declaration and an election.
Note: Panel (a) uses 2022 provincial and municipal borders; panels (b), (c), and (d) use 2022 municipal borders.

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Two-panel figure titled “TWFE Event Study of Far-Right Vote Share, 2001–22.”
Panel (a) shows the average treatment effect on the treated (ATT) and control mean for far-right vote share over the total number of valid votes by election year (2001–2022). The top graph displays ATT with point estimates and confidence intervals. The bottom graph shows the control group’s mean vote share, which rises steeply after 2013.
Panel (b) shows the same information but for far-right vote share over the number of votes cast for the right-wing bloc. The ATT plot shows increasing effects after 2013, while the control mean plot indicates a sharp rise in far-right share from 2013 to 2022. The 2013 election year is marked with an open circle. Gray shading highlights the post-2013 period.

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Two-panel figure titled “Staggered Event-Study Plots for Two Indicators of Economic and Sociocultural Hardship.”
Panel (a) shows average treatment effects (ATT) on pre-tax income per capita (2008–2020) in euros on an arcsinh scale, plotted by years relative to infection (from -5 to +5). Estimates are near zero before infection, with negative effects appearing after infection, especially from year 1 onward.
Panel (b) shows ATT on the share of Italian residents aged 20 to 35 (2002–2019), also on an arcsinh scale. Estimates are near zero before infection and become increasingly negative in the years following infection. Each dot represents an ATT estimate with vertical lines for confidence intervals. Year -1 is marked with an open circle. The post-infection period is shaded in gray.

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Ross Dahlke @rossdahlke.bsky.social · 27/03/2025

During the 2019 Canadian Election, partisan differences in online news consumption were small, with news consumption characteristics being more predictive of news consumption, finds @ericmerkley.bsky.social doi.org/10.31219/osf...

Bar chart showing the share of respondents who used various online news outlets during a four-week tracking period. The chart is divided into two panels: domestic and international sources (left panel) and American sources (right panel).

In the left panel, top-used domestic sources include CBC, Global, and CTV, with over 25% of respondents using CBC. Some outlets are labeled with ideological leanings (e.g., National Observer – Left, Rebel – Right).

In the right panel, American outlets with the highest usage include CNN, Washington Post (WaPo), and New York Times (NYT), all used by less than 10% of respondents. Numerous lower-use outlets are labeled with political leanings, such as Breitbart (Right), Vox (Left), and Daily Caller (Right).

A note below the chart explains that CBC includes Radio-Canada and that local newspapers encompass a broad range of regional publications in Canada. TV5 and TVA are Quebec-based French-language broadcasters.

Table titled “Partisan differences in online partisan news exposure,” listing 24 online news outlets along with their ideological slant (Left or Right), average exposure by left- and right-leaning individuals, and the absolute difference between those values.

Outlets with the largest partisan exposure gaps include Fox News (Right; Left: 3.9, Right: 6.7, Diff.: 2.8), Raw Story (Left; Diff.: 2.3), and Slate (Left; Diff.: 1.6). Most right-leaning outlets show greater usage by right-leaning respondents, while most left-leaning outlets show greater usage by left-leaning respondents.

Some rows are italicized (e.g., Info Wars, MSNBC, Washington Examiner, Breitbart, Salon), indicating “wrong-signed” partisan differences—i.e., higher exposure among the ideological out-group. A note explains that differences are expressed as absolute values and italicization denotes these unexpected exposure patterns.

Figure 3 presents six panels displaying predicted partisan media use based on different political and psychological traits. The y-axis in all panels represents the predicted probability of partisan news use, with values ranging from approximately -0.1 to 0.4. Each panel includes point estimates with 95% confidence intervals.

In the top-left panel, partisan strength is grouped into three categories—none, weak/fairly strong, and strong. There is no clear pattern across these categories, as the predicted probability of partisan media use appears relatively flat with overlapping confidence intervals. The top-center panel shows ideological extremity on a scale from 0 to 5 and reveals a positive relationship: individuals with higher ideological extremity are more likely to use partisan news sources. In the top-right panel, political ideology is plotted on a 0 to 10 scale, and the relationship is slightly negative, suggesting that as individuals move along this ideological scale, their predicted use of partisan media slightly declines.

The bottom-left panel displays standardized scores for populism and shows a negative relationship, with greater populism associated with lower predicted use of partisan news. The bottom-center panel, focused on conspiratorial thinking (also standardized), similarly shows a negative trend: as conspiratorial thinking increases, the predicted use of partisan media decreases. In contrast, the bottom-right panel, which depicts media distrust (standardized), shows a slight positive relationship, indicating that individuals with higher media distrust are somewhat more likely to consume partisan news.

Across all panels, the plotted trends are accompanied by vertical error bars indicating the 95% confidence intervals for each estimate.

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Ross Dahlke @rossdahlke.bsky.social · 27/03/2025

Interesting look at news values and perceived misinformation across 24 countries by Nenno & @cbpuschmann.bsky.social in IJPP doi.org/10.1177/1940...

All The (Fake) News That’s Fit to Share? News Values in Perceived Misinformation across Twenty-Four Countries. Abstract
While there is a strong scholarly interest surrounding the content of political misinformation online, much of this research concerns misinformation in Western, Educated, Industrialized, Rich and Democratic (WEIRD) countries. Although such research has investigated the topical and stylistic characteristics of misinformation, its findings are frequently not interpreted systematically in relation to properties that journalists rely on to capture the attention of audiences, that is, in relation to news values. We close the gap on comparative studies of news values in misinformation with a perspective that emphasizes non-WEIRD countries. Relying on a dataset of URLs that were shared on Facebook in twenty-four countries and reported by users as containing false news...

Figure 1. Stacked bar chart showing participant counts by country, categorized by four groups: WEIRD-flagged (dark blue), WEIRD-non-flagged (light blue), non-WEIRD-flagged (yellow), and non-WEIRD-non-flagged (orange). Countries are ordered by total count from highest (Poland, PL) to lowest (Vietnam, VN). Most countries have a majority of non-flagged participants, with variation in WEIRD and non-WEIRD classification. For example, Poland shows 77% WEIRD-non-flagged and 23% WEIRD-flagged; Brazil shows 64% non-WEIRD-non-flagged and 36% non-WEIRD-flagged; Portugal is the only country with a notable portion of WEIRD-flagged responses (35%). Percentages are labeled on each segment of the bars.

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Figure 2. Line graph showing monthly counts of responses from March 2017 to July 2019, grouped by four categories: WEIRD-flagged (black dashed line), WEIRD-non-flagged (blue dashed line), non-WEIRD-flagged (yellow solid line), and non-WEIRD-non-flagged (orange solid line). The non-WEIRD-non-flagged group starts with the highest count, peaking around April 2017 and again around September 2018 before declining sharply. The WEIRD-non-flagged group peaks around January 2019. The flagged groups (both WEIRD and non-WEIRD) remain lower in count throughout but rise briefly in mid-2018. Overall, all groups show declining trends after early 2019.

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Figure 3. Grid of five dot plots displaying effect sizes for different journalistic dimensions—Conflict, Negativity, Proximity, Individualization, and Informativeness—across multiple countries. Each plot has countries listed along the x-axis and effect sizes on the y-axis, ranging from approximately -0.2 to 0.4. Most countries have effect sizes near zero. A few countries show higher positive effect sizes for specific dimensions; for example, Brazil (BR) for Conflict and Individualization, and South Korea (KR) and Thailand (TH) for Informativeness. Each dot represents a country's effect size for that dimension.

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Ross Dahlke @rossdahlke.bsky.social · 26/03/2025

Abstaining from social media does not significantly affect positive affect, negative affect, or life satisfaction, finds @lauralemahieu.bsky.social et al., in a meta analysis doi.org/10.1038/s415...

The effects of social media abstinence on affective well-being and life satisfaction: a systematic review and meta-analysis. Abstaining from social media has become a popular digital disconnection strategy of individuals to enhance their well-being. To date, it is unclear whether social media abstinences are truly effective in improving well-being, however, as studies produce inconsistent outcomes. This preregistered systematic review and meta-analysis therefore aims to provide a more precise answer regarding the impact of social media abstinence on well-being. The databases of PubMed, Scopus, Web of Science, Communication Source, Cochrane Library, and Google Scholar were searched for studies examining the effect of social media abstinence on three outcomes, namely positive affect, negative affect, and/or life satisfaction. In total, ten studies (N = 4674) were included, allowing an examination of 38 effect sizes across these three outcomes. The analyses revealed no significant effects

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Figure 1. Forest plot showing results from a meta-analysis of studies on digital interventions and their effect on positive affect. Each row lists a study with its estimated effect size (Hedges' g), 95% confidence interval (CI), and weight in the meta-analysis. Most individual study estimates are close to zero, with some negative and some positive. The overall effect size is 0.03 [95% CI: -0.11, 0.16], suggesting a small, non-significant positive effect. The prediction interval spans from -0.42 to 0.47. The plot indicates moderate heterogeneity (I² = 61%, p < .01). A diamond represents the overall effect at the bottom of the plot.

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Figure 2. Funnel plot displaying the relationship between standard error (y-axis) and effect size (Hedges' g, x-axis) for individual studies in a meta-analysis. Each dot represents a study. The plot includes a vertical dashed line at zero, a solid triangle indicating the estimated overall effect, and shaded areas representing regions of increasing statistical significance. The distribution of studies appears slightly asymmetrical, with more studies showing positive effect sizes. A dashed diagonal line and funnel-shaped contours illustrate expected dispersion under no publication bias.

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Ross Dahlke @rossdahlke.bsky.social · 25/03/2025

Despite trust in personal doctors becoming a partisan issue, experimental evidence suggests that sharing a political background with one's medical provider increases willingness to seek care, finds @obrian.bsky.social & Bradley Kent in @bjpols.bsky.social doi.org/10.1017/S000...

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Partisanship and Trust in Personal Doctors: Causes and Consequences

Abstract
In the first decades of the twentieth century, the gap in age-adjusted mortality rates between people living in Republican and Democratic counties expanded; people in Democratic counties started living longer. This paper argues that political partisanship poses a direct problem for ameliorating these trends: trust and adherence in one’s personal doctor (including on non-COVID-19 related care) – once a non-partisan issue – now divides Democrats (more trustful) and Republicans (less trustful). We argue that this divide is largely a consequence of partisan conflict surrounding COVID-19 that spilled over and created a partisan cleavage in people’s trust in their own personal doctor. We then present experimental evidence that sharing a political background with your medical provider increases willingness to seek care. The doctor-patient relationship is essential for combating some of society’s most pressing proble

Figure 1. Line graph with three panels showing trends in attitudes toward Education, Medicine, and the Scientific Community from 1988 to 2020. Each panel displays two lines: one for Democrats (light blue) and one for Republicans (red), with vertical error bars. Y-axis values range from 1.75 to 2.5. In all panels, Republican ratings decline more steeply over time, especially after 2010. Democrat ratings remain relatively stable or increase slightly, particularly in the Scientific Community panel after 2010.

Figure 2. Dot-and-whisker plot showing treatment effects (Treatment – Control) on three outcomes: “Trust Own Doctor,” “Adhere Doc Advice,” and “Conf. in Medicine.” Three groups are plotted: Vote Biden (light blue), Vote Trump (red), and Biden–Trump difference (gray). The y-axis ranges from -0.5 to 1.0. For “Trust Own Doctor” and “Conf. in Medicine,” the Biden group shows positive treatment effects, while the Trump group shows negative effects. The Biden–Trump difference is positive for all outcomes, with error bars indicating uncertainty. A horizontal dashed line at 0.0 marks no treatment effect.

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Figure 3. Dot-and-whisker plot with four panels showing Average Marginal Component Effects (AMCE) for Democratic (light blue) and Republican (red) respondents across different attributes: Male, Ivy League, Far Away, Democrat, High Rating, Medium Rating, Black, and Hispanic. Panels display results for All Respondents, Female Respondents, Black Respondents, and Latinx Respondents. The x-axis ranges from -0.25 to 0.50 with a vertical dashed line at 0.0 indicating no effect. Each dot represents the AMCE estimate with horizontal error bars indicating uncertainty. Some estimates differ by respondent group, and not all attributes have data points for both political affiliations in all panels.

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Ross Dahlke @rossdahlke.bsky.social · 24/03/2025

Observers of Black Lives Matter protests are more likely to describe protesters as violent if the protest is met with heavy police presence, finds English, @arielrwhite.bsky.social & @eckhouse.bsky.social in @poppublicsphere.bsky.social doi.org/10.1017/S153...

How Police Behavior Shapes Perceptions of Protests: Evidence from Black Lives Matter
Abstract
As Black Lives Matter protests swept across the United States in recent years, protesters encountered a mix of police reactions: Some news reports described police in military gear and widespread arrests, whereas others reported minimal police involvement. We developed an original dataset of BLM protests that shows that police reactions varied widely, even when comparing protests with similar messages and tactics. We then investigated this variation with a survey experiment and found that observers are more likely to describe protesters as violent when a protest is met with a heavy police presence. These findings highlight the role of the police in shaping public perceptions of violence and social movements and extend a growing body of empirical research on BLM by shifting the focus from protest activity to the impact of protest policing.

Map of the United States showing the locations and sizes of Black Lives Matter protests from July 2014 to March 2017. Each protest is represented by a circle, with larger circles indicating larger protest sizes. Protests occurred across the country, with high concentrations along the East Coast, Midwest, and California. The legend categorizes protest size into four groups: 0–50, 50–100, 100–1000, and 1000+ participants.

Table showing regression results on factors predicting police response to Black Lives Matter protests (2014–2017). Three dependent variables are analyzed: any police presence, any arrests made, and use of crowd-control measures. Key predictors include protest disruptions (e.g., highway blockage), protest size, timing (after dark), and protest demographics. Significant positive associations are found for disruptive actions and nighttime protests across all outcomes. Smaller protests are linked to less police presence and crowd control. Majority-Black protests are not significantly associated with police responses.

Dot-and-whisker plot showing treatment effects on three outcomes: “Cause Trouble” (control mean = 1.79), “Intentions Violent” (control mean = 1.63), and “Event Violent” (control mean = 1.83), all measured on a 5-point Likert scale. The x-axis represents the treatment effect (Treated – Control), ranging from 0.0 to 0.3. All three outcomes show positive treatment effects with confidence intervals spanning different ranges. A vertical dashed line at 0.0 indicates no effect.

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Ross Dahlke @rossdahlke.bsky.social · 22/03/2025

Across 26 countries and using a database of 32M tweets, radical-right populist elites are the most likely to spread misinformation, finds @pettertornberg.bsky.social & @julianachueri.bsky.social doi.org/10.1177/1940...

When Do Parties Lie? Misinformation and Radical-Right Populism Across 26 Countries

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Ross Dahlke @rossdahlke.bsky.social · 21/03/2025

Larger LLMs produce more persuasive political messages, but marginal persuasiveness diminishes significantly with model size, finds @kobihackenburg.bsky.social @benmtappin.bsky.social @paul-rottger.bsky.social Bright @computermacgyver.bsky.social @helenmargetts.bsky.social doi.org/10.1073/pnas...

Scaling language model size yields diminishing returns for single-message political persuasion

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Ross Dahlke @rossdahlke.bsky.social · 19/03/2025

Great in-depth examination of news media, bias, and information spread, by @hanshanley.bsky.social et al. doi.org/10.48550/arX...

Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites

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Ross Dahlke @rossdahlke.bsky.social · 18/03/2025

Fascinating study of politicians' misinformation sharing behavior on social media, comparing across the U.S., UK, Italy, and Germany and levels of governance by @jingyuanyu.bsky.social @emesedomahidi.bsky.social @zollofab.bsky.social doi.org/10.48550/arX...

- Figure 1 misinformation sharing behavior by country and levels of political hierarchy

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- Figure 2 public engagement with misinformation per country

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Ross Dahlke @rossdahlke.bsky.social · 17/03/2025

Fact-checking posts on Facebook have become more positive, with editorial fact-checkers using more emotionality than independent fact-checkers, with more emotionality associated with more engagement, finds @hnxue.bsky.social et al. doi.org/10.1177/2056...

Facts or Feelings? Leveraging Emotionality as a Fact-Checking Strategy on Social Media in the United States

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Ross Dahlke @rossdahlke.bsky.social · 17/03/2025

Attitudinal sorting (symbolic ideology, policy attitudes, group sentiments), not demographic sorting, is the primary contributor to affective polarization, finds Konicki using data from 1952-2020 doi.org/10.31219/osf...

The Rise of Affective Polarization: Is It What We Think, or Who We Are?

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Figure 2: Partisan Sorting Over Time 1952-2020 ANES, and Table 2 Are over time, increases in Partisan Sorting are significant?

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Figure 4: Social Group Importance Over Time, ANES

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Figure 5, Change Over Time, Attitudinal Sorting, and Affective Polarization.

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