Matt DeVerna

@matthewdeverna.com

8955 Followers 848 Following

Postdoc with Stanford's Tech Impact and Policy Center (@techimpactpolicy.bsky.social). Past: Indiana University / Observatory on Social Media. Societal impacts of AI within the information ecosystem. 🧨 matthewdeverna.com

Screenshot of a TLDR section of a substack blog post that reads as follows: - I have private datasets (ChatGPT logs, WhatsApp data, YouTube traces) that are too sensitive to share but could answer dozens of research questions my small team will never think to ask. - I want to build a public platform where anyone can submit a structured research idea in plain English. My team runs the analysis using AI coding agents against the private data, and the contributor gets full credit and all the results. - The model has real limitations: agents make mistakes, contributors cannot iterate freely with the data, and verification requires trust. I am starting with a small pilot scoped to tractable problems. - I want feedback on whether this is useful, where it will fail, and what I am missing before I build it.
Stacked horizontal bar chart showing the distribution of platform content marking for deepfake bounties classified as SFW versus NSFW. Each bar represents 100\% of bounties within that category, divided into ``Not marked'' (light purple) and ``Marked'' (dark purple) segments. For SFW deepfake bounties, 85.6\% were marked by the platform while 14.4\% remained unmarked. In contrast, NSFW deepfake bounties show a more balanced distribution, with 58.3\% marked and 41.7\% not marked. The substantially higher marking rate for SFW deepfakes (85.6\%) compared to NSFW deepfakes (58.3\%) suggests that the platform's content moderation systems more effectively detect deepfakes when they violate deepfake policies in non-adult content contexts. The near-even split for NSFW deepfakes indicates potential detection challenges when deepfake content overlaps with adult content categories, where the presence of NSFW elements may obscure or complicate deepfake identification. This differential marking effectiveness has significant implications for platform governance, suggesting that moderation strategies may need category-specific approaches to achieve consistent enforcement across different content types.
Lorenz curves illustrating the distribution of bounty requests across supporters (requesters) for three content categories: SFW (blue line, Gini = 0.395), NSFW (red line, Gini = 0.380), and Deepfake (green line, Gini = 0.452). The x-axis represents the cumulative share of supporters sorted by request volume (from highest to lowest), while the y-axis shows the cumulative share of bounties created. The diagonal dashed gray line represents perfect equality, where each supporter would contribute an equal share of requests. All three curves bow above the equality line, indicating concentration of requesting activity among smaller groups of users. Deepfake requests exhibit the highest concentration, with approximately 10\% of supporters responsible for 25\% of all deepfake bounties, compared to 20\% for SFW and 18\% for NSFW at the same percentile. The higher Gini coefficient for deepfakes (0.452) compared to SFW (0.395) and NSFW (0.380) indicates that deepfake requesting is significantly more concentrated among a small group of intensive users, suggesting potentially systematic rather than casual requesting behavior. This concentration pattern may serve as a behavioral indicator for targeted harassment, non-consensual intimate imagery production, or commercial exploitation operations.
Two-panel figure analyzing deepfake bounties on Civitai. Panel (a) shows gender distribution split into two horizontal bar charts. The top chart displays NSFW deepfake bounties with Female at 95.8\% and Male at 4.2\%. The bottom chart shows SFW deepfake bounties with Female at 89.6\%, Male at 10.1\%, and Non-binary at 0.3\%. Panel (b) presents a stacked horizontal bar chart showing the profession distribution across all deepfakes, with bars color-coded for NSFW (red) and SFW (blue) categories. Actor/actress leads at 39.3\% of deepfakes, followed by Influencer at 13.9\%, Adult worker at 13.0\%, Artist at 10.2\%, Miscellaneous at 8.4\%, Model at 7.4\%, Athlete at 5.3\%, Self/spouse at 1.2\%, Reporter at 0.9\%, and Producer at 0.3\%. The x-axis shows the number of bounties ranging from 0 to 140.
Four-panel figure showing Civitai bounty statistics. Panel (a) shows a horizontal bar chart of bounty counts by type, with LoRA having the most bounties at 2,984, followed by Image at 1,475, Model at 138, and decreasing to Video at 35. Panel (b) displays a horizontal bar chart of bounty themes by proportion, with NSFW being the largest category at approximately 0.43 (comprising 2,286 bounties split between platform-labeled and GPT-4.1 classified NSFW shown with diagonal hatching), followed by Fictional Characters at 1,463 bounties. Panel (c) presents stacked horizontal bars showing NSFW versus SFW proportions for each bounty type, with Video being 69\% NSFW and Embed being 82\% SFW. Panel (d) shows a time series from November 2023 to January 2025 displaying the proportion of SFW (decreasing trend line) and NSFW (increasing trend line) bounties over time, with 95\% confidence intervals shown as shaded regions around each trend line and vertical dashed lines marking the start of 2024 and 2025.
Two-panel figure showing example Civitai bounty pages. Panel (a) displays the ``Civitai Official Bounty - Swamp Monster Style LoRA'' bounty page featuring two unblurred fantasy creature images: a moss-covered humanoid creature and a colorful frog-like monster with large eyes. The bounty details show it's a LoRA creation type, base model SD 1.5, started October 16, 2023, with deadline October 23, 2023. A completion banner indicates prizes have been awarded to winners. The supporter section shows a user avatar with a 50,000 buzz reward. Panel (b) shows ``Dno's 2k Special'' bounty page with two blurred images of different content ratings - one marked as R with a ``Show'' button overlay, and another marked as XXX with a ``Show'' button. The bounty type is image creation, started September 22, 2025, with deadline October 23, 2025. Supporter names are redacted for privacy.
Screenshot of an email from the OpenAI Codex team informing Codex users that they have doubled API limits.
Job posting on LinkedIn for a Research Scientist position in YY Ahn's group at UVA.
Screenshot of text that reads as follows: 14 Postdoctoral Fellowships (2 years) Fellowship Period: between July 2026 – November 2028 Application Deadline: 9 January 2026, 11:00 AM (CET) The fellowships are available to researchers from all over the world and from any discipline represented at one if the three universities with a minimum of one year and a maximum of seven years of post-doctoral experience, who cannot conduct or continue their work in the USA appropriately because of actual political pressure. The aim is to provide time and space for their excellent research and open up international prospects for their future career. The programme offers a two-year research position at one of the three universities, preferred by the applicant, and local disciplinary cooperation, as well as support, resources, networks and exchange within the respective Institute for Advanced Studies with its international and interdisciplinary community of fellows.
Screenshot of a title page. The title is: Extending Truth-Default Theory to Misinformation: Lessons for Misinformation Scholarship from Deception Research. The authors are Ryan C. Moore and Jeffrey T. Hancock from Stanford University.
A screenshot of a Washington Post fact-checking article. Headline: "$50 million for condoms in Gaza? There’s no evidence for the White House claim." Subheading: "The figure would rain 1.5 billion condoms on an area only double the size of the District of Columbia." Date: Updated January 29, 2025 Byline: Analysis by Glenn Kessler Image Description: A photograph of White House Press Secretary Karoline Leavitt speaking at a press briefing. She has blonde hair, is wearing a magenta blazer, and appears to be mid-speech with a serious expression. The background is blurred, suggesting a formal press setting. The photo is credited to Jabin Botsford of The Washington Post.
A cellphone screenshot showing the functionality on X that allows users to get notified when a specific account posts. In this case it is shown for the official CNN account.
Screenshot demonstrating how to use the Plurals Python library to create a nationally representative ensemble of agents modeled by different large language models (LLMs). Includes installation instructions (pip install plurals), API key setup, and example Python code to ask agents a task question about improving bike commuting.
Flyer for the 'Awesome Speakers' virtual event hosted by the Observatory on Social Media (OSoMe) at Indiana University. The flyer features the event title, date (November 13, 2024), and time (12 PM - 1 PM ET). It describes the event as a platform for scholars to discuss social media manipulation and misinformation. The featured speaker is Kristina Lerman from the University of Southern California. Attendees are encouraged to register and view the full list of speakers at osome.iu.edu/events.