Writing

To avoid irrelevance when they are needed most, experts and nonpartisan analysts must rethink not just their channels of communication but also their theory of influence.

Bad faith politicking about the way search algorithms work makes it harder for tech companies to solve the real problems.

The internet has allowed independent creators to thrive, finding niche audiences for everything from nudes to salad recipes. But it’s also spawned silos that incentivize propaganda.

A series by Renee DiResta on technology in politics, influence, propaganda, and such.

A review of National Academies of Sciences, Engineering, and Medicine, “Understanding and Addressing Misinformation About Science” (The National Academies Press, 2025).

Middleware, third-party software intermediaries between users and platforms, has been broached as a means to decentralize the power of social media platforms and enhance user agency. Middleware may enable a more user-centric and democratic approach to shaping digital experiences, offering a flexible architecture as an alternative to both centrally controlled, opaque platforms and an unmoderated, uncurated internet. The widespread adoption of open middleware has long hinged on the cooperation of established major platforms; however, the recent growth of federated platforms, such as Mastodon and Bluesky, has led to increased offerings and user awareness. In this report we consider the potential of middleware as a means of enabling greater user control over curation and moderation - two aspects of the social media experience that are often mired in controversy. We evaluate the trade-offs and negative externalities it might create, and discuss the technological, regulatory, and market dynamics that could either support or hinder its implementation.

American political theater isn’t new. The House Un-American Activities Committee operated for decades—until targets learned to fight back.

Google’s letter to Rep. Jim Jordan inadvertently provides evidence against the “censorship industrial complex” narrative he is using it to support.

Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this paper, we analyze the value of a new tool to address this challenge: “personhood credentials” (PHCs), digital credentials that empower users to demonstrate that they are real people -- not AIs -- to online services, without disclosing any personal information. Such credentials can be issued by a range of trusted institutions -- governments or otherwise. A PHC system, according to our definition, could be local or global, and does not need to be biometrics-based. Two trends in AI contribute to the urgency of the challenge: AI’s increasing indistinguishability from people online (i.e., lifelike content and avatars, agentic activity), and AI’s increasing scalability (i.e., cost-effectiveness, accessibility). Drawing on a long history of research into anonymous credentials and “proof-of-personhood” systems, personhood credentials give people a way to signal their trustworthiness on online platforms, and offer service providers new tools for reducing misuse by bad actors. In contrast, existing countermeasures to automated deception -- such as CAPTCHAs -- are inadequate against sophisticated AI, while stringent identity verification solutions are insufficiently private for many use-cases. After surveying the benefits of personhood credentials, we also examine deployment risks and design challenges. We conclude with actionable next steps for policymakers, technologists, and standards bodies to consider in consultation with the public.

The site formerly known as Twitter has become the center of a fantastical political culture.

Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.

A growing body of research suggests human behavior on social media is strikingly similar to collective behavior in nature.

You don’t need fake accounts to spread ampliganda online. Real people will happily do it.

Society’s well-being depends on how well public-health officials and average internet users combat misinformation.

Generative language models have improved drastically, and can now produce realistic text outputs that are difficult to distinguish from human-written content. For malicious actors, these language models bring the promise of automating the creation of convincing and misleading text for use in influence operations. This report assesses how language models might change influence operations in the future, and what steps can be taken to mitigate this threat. We lay out possible changes to the actors, behaviors, and content of online influence operations, and provide a framework for stages of the language model-to-influence operations pipeline that mitigations could target (model construction, model access, content dissemination, and belief formation). While no reasonable mitigation can be expected to fully prevent the threat of AI-enabled influence operations, a combination of multiple mitigations may make an important difference.

Upon request by the United States Senate Select Committee on Intelligence (SSCI), New Knowledge reviewed an expansive data set of social media posts and metadata provided to SSCI by Facebook, Twitter, and Alphabet, plus a set of related data from additional platforms. The data sets were provided by the three primary platforms to serve as evidence for an investigation into the Internet Research Agency (IRA) influence operations. The organic post content in this data set has never previously been seen by the public. Our report quantifies and contextualizes Internet Research Agency (IRA) influence operations targeting American citizens from 2014 through 2017, and articulates the significance of this long-running and broad influence operation. It includes an overview of Russian influence operations, a collection of summary statistics, and a set of key takeaways that are then discussed in detail later in the document. The document includes links to full data visualizations, hosted online, that permit the reader to explore facets of the IRA-created manipulation ecosystem. Finally, we share our concluding notes and recommendations. We also provide a comprehensive slide deck accommodating a wide array of selected images directly from the data set illustrating our observations, and, as an appendix, a comprehensive summary of relevant statistics related to the data set. Broadly, Russian interference in the U.S. Presidential Election of 2016 took three distinct forms, one of which is within the scope of our analysis: ... 3. A sweeping and sustained social influence operation consisting of various coordinated disinformation tactics aimed directly at US citizens, designed to exert political influence and exacerbate social divisions in US culture. This last form of interference, a multi-year coordinated disinformation effort conducted by the Russian state-supported Internet Research Agency (IRA), is the topic of this analysis.