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.

Image description 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.

Image description 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.

Image description 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.

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

Really exicted to finally see our paper in print: “Web scraping for research: Legal, ethical, institutional, and scientific considerations”
A great interdisciplinary effort with @m-dot-brown.bsky.social, @orangechair.org, Gabe Maldoff, @solmg.bsky.social, @zevesanderson.com
doi.org/10.1177/2053...

Grateful for the collaboration with excellent researchers for this paper @orangechair.org, Gabe Maldoff, @solmg.bsky.social, @zevesanderson.com, & @michaelzimmer.bsky.social

Thrilled to have a new article published in @bigdatasoc.bsky.social! 🥳🎉📊

With scraping becoming a more common data collection strategy for internet researchers, we cover the legal, ethical, institutional, and scientific ramifications researchers should consider. doi.org/10.1177/2053...

they do be posting

Candidates Be Posting: Multi-Platform Strategies and Partisan Preferences in the 2022 U.S. Midterm Elections - Josephine Lukito, Maggie Macdonald, Bin Chen, Megan A. Brown, Stephen Prochaska, Yunkang ... In this multi-platform, comparative study, we analyze social media messages from political candidates (N = 1,517) running for Congress during the 2022 U.S. Midt...

🚨New publication in Social Media + Society🚨

Candidates Be Posting: Multi-Platform Strategies and Partisan Preferences in the 2022 U.S. Midterm Elections

And it's open access!

journals.sagepub.com/doi/full/10....

Evaluating how LLM annotations represent diverse views on contentious topics Researchers have proposed the use of generative large language models (LLMs) to label data for research and applied settings. This literature emphasizes the improved performance of these models relati...

7/ 📄 You can find our full paper here: arxiv.org/abs/2503.23243

w/ Shubham Atreja, @libbyh.bsky.social, & @patrickwu.bsky.social

6/ This has implications for both research and industry:
⚠️ Fairness evaluations should be context-specific
🤔 Model choice alone will not solve bias
🔍 Human disagreements are part of the complexity—not a flaw

5/ We also find that the difficulty of the labeling task is most predictive of LLM agreement with human annotators.

4/ Key finding: While LLMs disagree with human annotators on the basis of demographics, it tends to be in the same directions on the same demographic categories within the same dataset. In other words, the direction of bias is not LLM-specific, but dataset-specific.

3/ This study evaluates LLM annotations across 4 datasets and tasks, analyzing whether these models disproportionately reflect majority group opinions.

2/ Prior research praises LLMs for their high accuracy, precision, recall, and F1 scores in labeling tasks—but also raises concerns about bias, especially around sensitive or polarizing content (e.g., toxicity).

Can large language models (LLMs) fairly annotate data on contentious topics?

Our new paper dives into this question—looking at whether LLM-generated labels reflect diverse viewpoints or skew toward majority perspectives. The results are surprisingly nuanced. 🧵

Quantifying Narrative Similarity Across Languages - Hannah Waight, Solomon Messing, Anton Shirikov, Margaret E. Roberts, Jonathan Nagler, Jason Greenfield, Megan A. Brown, Kevin Aslett, Joshua A. Tuck... How can one understand the spread of ideas across text data? This is a key measurement problem in sociological inquiry, from the study of how interest groups sh...

I am thrilled to share a new article in Sociological Methods & Research, “Quantifying Narrative Similarity Across Languages”. My co-first author Sol Messing and our collaborators developed a new approach to measuring “narrative similarity” between texts: journals.sagepub.com/doi/10.1177/...

[New WP] With the closure of major social media APIs and the new data access mandates under DSA, we enter what we call the "post-post-API" era. But have researchers obtained the data they need? Our recent survey (180) + interview (19) study suggests a stark reality.

🔗 arxiv.org/abs/2505.09877

1/3

So thrilled to have worked on this important piece with @yang3kc.bsky.social @m-dot-brown.bsky.social and Kayo Mimizuka. Data access for independent researchers is at such a critical juncture

Special thanks to Mango Brown and Taylor Swift's 'Mr. Perfectly Fine' for their help in getting this paper over the finish line

So excited to finally see this out! It was the first paper I started during my postdoc at @csmapnyu.org

It's well known that politicians take more extreme positions during primaries. In @electoralstudies.bsky.social, we find this shift is much more likely when incumbents in safe seats face a well-funded primary challenger.

🧵👇
authors.elsevier.com/a/1kn5KxRaZn...

Scatterplot showing various U.S. government agencies plotted with the total staff (on a log scale) on y-axis versus likelihood of being perceived as a knowledge institution on the x-axis. Red dots indicate agencies that have experienced DOGE layoffs, while gray dots indicate agencies without layoffs. Agencies like NIH, NSF, CDC, and NOAA appear on the right side (more likely to be perceived as knowledge institutions), while agencies like ICE, DEA, and Secret Service appear on the left side (less likely to be perceived as knowledge institutions).

@adambonica.bsky.social showed ideology predicts which agencies experience DOGE layoffs. But what other factors could be driving this?

Using a generative LLM-derived measure, I find agencies perceived as knowledge institutions are more likely to experience layoffs, even controlling for ideology. 🧵

Cover for Making academia suck less: Supporting early career researchers studying harmful content online through a feminist ethics of care

🚨New Publication in New Media & Society🚨
Co-First-Authored w/ @m-dot-brown.bsky.social @meredithpruden.bsky.social & @markriedl.bsky.social

Making academia suck less: Supporting early career researchers studying harmful content online through a
feminist ethics of care.
jlukito.com/s/brown-et-a...

They don’t need an excuse. They’ll claim we gave them an excuse no matter what we do. “Be careful what you say” is precisely how authoritarians achieve compliance w/o lifting a finger. And yet, even w/ compliance, they will still attack.

Instead, may I suggest taking a look at researchersupport.org

Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerations Scientists across disciplines often use data from the internet to conduct research, generating valuable insights about human behavior. However, as generative AI relying on massive text corpora becomes...

Excited to share a pre-print about web scraping for research! We're happy to receive feedback on how we frame this issue and try to build some paths forward for researchers. w/ @orangechair.org, Gabe Maldoff, @solmg.bsky.social, Zeve Sanderson, & @michaelzimmer.bsky.social arxiv.org/abs/2410.23432

I’ll keep working with academics, civil society researchers, and journalists—including via the Coalition for Independent Technology Research—to continue these important accountability efforts. 12/ (independenttechresearch.org/introducing-...)

🚨Our new paper in Political Analysis presents a novel, cross-platform method for estimating the ideology of YouTube videos. 

What we found: it is possible to do this at scale with an efficient, automated method!

🧵1/

Elon Musk's X restructuring curtails disinformation research, spurs legal fears | Reuters Social media researchers have canceled, suspended or changed more than 100 studies about X, formerly Twitter, as a result of actions taken by Elon Musk that limit access to the social media platform, ...

Great coverage of the ongoing challenge of researcher data access on Twitter! Thanks @sheiladang.bsky.social for the great piece!

www.reuters.com/technology/e...