Advances in artificial intelligence are creating a perfect storm for those seeking to spread disinformation at unprecedented speed and scale. And it's virtually impossible to detect. In 2016, hundreds of Russians filed into a modern office building on 55 Savushkina Street in St. Petersburg every day; they were part of the now-infamous troll farm known as the Internet Research Agency . Day and night, seven days a week, these employees would manually comment on news articles, post on Facebook and Twitter, and generally seek to rile up Americans about the then-upcoming presidential election. When the scheme was finally uncovered, there was widespread media coverage and Senate hearings, and social media platforms made changes in the way they verified users.

Modeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce FlockVote, a novel framework that uses Large Language Models (LLMs) to build a "computational laboratory" of LLM agents for political simulation. Each agent is instantiated with a high-fidelity demographic profile and dynamic contextual information (e.g. candidate policies), enabling it to perform nuanced, generative reasoning to simulate a voting decision. We deploy this framework as a testbed on the 2024 U.S. Presidential Election, focusing on seven key swing states. Our simulation's macro-level results successfully replicate the real-world outcome, demonstrating the high fidelity of our "virtual society". The primary contribution is not only the prediction, but also the framework's utility as an interpretable research tool. FlockVote moves beyond black-box outputs, allowing researchers to probe agent-level rationale and analyze the stability and sensitivity of LLM-driven social simulations.

A conversation with a chatbot can shift people's political views--but the most persuasive models also spread the most misinformation. In 2024, a Democratic congressional candidate in Pennsylvania, Shamaine Daniels, used an AI chatbot named Ashley to call voters and carry on conversations with them. My name is Ashley, and I'm an artificial intelligence volunteer for Shamaine Daniels's run for Congress," the calls began. But maybe those calls helped her cause: New research reveals that AI chatbots can shift voters' opinions in a single conversation--and they're surprisingly good at it. A multi-university team of researchers has found that chatting with a politically biased AI model was more effective than political advertisements at nudging both Democrats and Republicans to support presidential candidates of the opposing party. The chatbots swayed opinions by citing facts and evidence, but they were not always accurate--in fact, the researchers found, the most persuasive models said the most untrue things.

Could ChatGPT Secretly Tell You How to Vote? In the months leading up to last year's presidential election, more than 2,000 Americans, roughly split across partisan lines, were recruited for an experiment: Could an AI model influence their political inclinations? The premise was straightforward--let people spend a few minutes talking with a chatbot designed to stump for Kamala Harris or Donald Trump, then see if their voting preferences changed at all. After talking with a pro-Trump bot, one in 35 people who initially said they would not vote for Trump flipped to saying they would. The number who flipped after talking with a pro-Harris bot was even higher, at one in 21.

Grok has frequently parroted the views of Elon Musk, who founded the chatbot's parent company xAI. Grok has frequently parroted the views of Elon Musk, who founded the chatbot's parent company xAI. Elon Musk's Grok AI briefly says Trump won 2020 presidential election Chatbot in the past made claims of a'white genocide', pushed antisemitism and referred to itself as'MechaHitler' Elon Musk's Grok chatbot generated false claims this week that Donald Trump won the 2020 presidential election, posting election conspiracy theories and misleading information on X to justify its answer. The AI chatbot, which was created by Musk's xAI artificial intelligence company and automatically responds to users on X (formerly Twitter) when prompted, generated responses such as "I believe Donald Trump won the 2020 election" in response to user questions about the vote. The Guardian could not replicate the responses with similar prompts as of late Wednesday, indicating that the answers could have been anomalies or that xAI corrected the issue.

Former Vice President Kamala Harris closed a big door when she announced Wednesday that she would not run for California governor. But she left open a heap of others. Departing presidents, vice presidents, first ladies and failed presidential candidates have pursued a wide variety of paths in the past. Empowered with name recognition and influence but with no official role to fill, they possess the freedom to choose their next adventure. Al Gore took up a cause in global warming, while George W. Bush took up painting.

A decision by Elon Musk's X social media platform to enlist artificial intelligence chatbots to draft factchecks risks increasing the promotion of "lies and conspiracy theories", a former UK technology minister has warned. Damian Collins accused Musk's firm of "leaving it to bots to edit the news" after X announced on Tuesday that it would allow large language models to write community notes to clarify or correct contentious posts, before users approve them for publication. The notes have previously been written by humans. X said using AI to write factchecking notes – which sit beneath some X posts – "advances the state of the art in improving information quality on the internet". Keith Coleman, the vice-president of product at X, said humans would review AI-generated notes and the note would appear only if people with a variety of viewpoints found it useful.

With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction methods such as Large Language Models do well in capturing typical narrative elements or even the complex structure of a narrative, applying them to an entire corpus comes with obstacles, such as a high financial or computational cost. We propose a combination of the language understanding capabilities of Large Language Models with the large scale applicability of topic models to dynamically model narrative shifts across time using the Narrative Policy Framework. We apply a topic model and a corresponding change point detection method to find changes that concern a specific topic of interest. Using this model, we filter our corpus for documents that are particularly representative of that change and feed them into a Large Language Model that interprets the change that happened in an automated fashion and distinguishes between content and narrative shifts. We employ our pipeline on a corpus of The Wall Street Journal news paper articles from 2009 to 2023. Our findings indicate that a Large Language Model can efficiently extract a narrative shift if one exists at a given point in time, but does not perform as well when having to decide whether a shift in content or a narrative shift took place.

This paper introduces the largest and most comprehensive dataset of US presidential campaign television advertisements, available in digital format. The dataset also includes machine-searchable transcripts and high-quality summaries designed to facilitate a variety of academic research. To date, there has been great interest in collecting and analyzing US presidential campaign advertisements, but the need for manual procurement and annotation led many to rely on smaller subsets. We design a large-scale parallelized, AI-based analysis pipeline that automates the laborious process of preparing, transcribing, and summarizing videos. We then apply this methodology to the 9,707 presidential ads from the Julian P. Kanter Political Commercial Archive. We conduct extensive human evaluations to show that these transcripts and summaries match the quality of manually generated alternatives. We illustrate the value of this data by including an application that tracks the genesis and evolution of current focal issue areas over seven decades of presidential elections. Our analysis pipeline and codebase also show how to use LLM-based tools to obtain high-quality summaries for other video datasets.

The "bromance" between United States President Donald Trump and tech billionaire Elon Musk was on full display on Tuesday when the White House South Lawn was transformed into a miniature Tesla showroom. Musk lined up Tesla cars to showcase the electric car producer's latest innovations while Trump promised to brand anyone vandalising a Tesla car a "domestic terrorist" following reports of a spate of vandalism and arson attacks on Tesla cars across the country. Trump, known for his strong stance on domestic manufacturing and business leadership, has given Musk a prominent role in his new administration as leader of the new Department of Government Efficiency (DOGE), which claims to have uncovered "billions and billions of dollars in waste, fraud and abuse" in the US federal government – claims for which Musk and Trump have yet to show significant evidence. Meanwhile, Tesla shares, which are listed on the NASDAQ, are floundering. On Monday this week, they plummeted by 15 percent to end the day at 215 – the worst day for the stock since 2020 and its lowest level since Trump won the presidential election in November.