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Mainstream news organizations shape public perception not only directly through the articles they publish but also through the choices they make about which topics to cover (or ignore) and how to frame the issues they do decide to cover. However, measuring these subtle forms of media bias at scale remains a challenge. Here, we introduce a large, ongoing (from January 1, 2024 to present), near real-time dataset and computational framework developed to enable systematic study of selection and framing bias in news coverage. Our pipeline integrates large language models (LLMs) with scalable, near-real-time news scraping to extract structured annotations -- including political lean, tone, topics, article type, and major events -- across hundreds of articles per day. We quantify these dimensions of coverage at multiple levels -- the sentence level, the article level, and the publisher level -- expanding the ways in which researchers can analyze media bias in the modern news landscape. In addition to a curated dataset, we also release an interactive web platform for convenient exploration of these data. Together, these contributions establish a reusable methodology for studying media bias at scale, providing empirical resources for future research. Leveraging the breadth of the corpus over time and across publishers, we also present some examples (focused on the 150,000+ articles examined in 2024) that illustrate how this novel data set can reveal insightful patterns in news coverage and bias, supporting academic research and real-world efforts to improve media accountability.

Elon Musk and Donald Trump are no longer friends. Tension between the two exploded into public view in the middle of last week, with each leveling sharp barbs at the other. Four days into the public feud between the world's most powerful person and the world's richest person, though, I declare Musk the loser. An unstoppable force has lost its battle with an immovable object. From my colleagues Hugo Lowell and Andrew Roth: On Thursday, Elon Musk called for Donald Trump's impeachment and mocked his connections to the convicted sex offender Jeffrey Epstein, as the US president threatened to cancel federal contracts and tax subsidies for Musk's companies, in an extraordinary social media feud that erupted between the former allies.

All over the world, political parties, politicians, and campaigns explore how Artificial Intelligence (AI) can help them win elections. However, the effects of these activities are unknown. We propose a framework for assessing AI's impact on elections by considering its application in various campaigning tasks. The electoral uses of AI vary widely, carrying different levels of concern and need for regulatory oversight. To account for this diversity, we group AI-enabled campaigning uses into three categories - campaign operations, voter outreach, and deception. Using this framework, we provide the first systematic evidence from a preregistered representative survey and two preregistered experiments (n=7,635) on how Americans think about AI in elections and the effects of specific campaigning choices. We provide three significant findings. There is a misalignment of incentives for deceptive practices and their externalities. We cannot count on public opinion to provide strong enough incentives for parties to forgo tactical advantages from AI-enabled deception. There is a need for regulatory oversight and systematic outside monitoring of electoral uses of AI. Still, regulators should account for the diversity of AI uses and not completely disincentivize their electoral use. Elections are times of high public attention on campaigns and their tools of communication. A representative survey of Americans shows that people dislike all kinds of AI uses in campaigns but are more critical of deceptive uses than those improving campaign operations or voter outreach (Study 1, n = 1,199). A survey experiment shows that when learning about specific AI uses in campaigns, American respondents reacted much more negatively to deceptive uses (Study 2, n = 1,985). Our study identifies a misalignment of incentives for deceptive practices and their externalities. We cannot count on public opinion to provide strong enough incentives for parties to forgo tactical advantages from AI-enabled deception.

As Republican presidential candidates flocked to Southern California for a debate and the state GOP convention this week, President Biden was busy in the San Francisco Bay Area collecting campaign checks and painting the election as a choice between MAGA chaos and functioning government. During three fundraising events in some of the swankiest neighborhoods of Silicon Valley and San Francisco on Tuesday and Wednesday, Biden touted his administration's accomplishments on climate and infrastructure, the United States' support for Ukraine and opposition to Russian President Vladimir Putin and his appointment of the first Black woman to the Supreme Court. As Wednesday's GOP presidential debate at the Reagan library made clear, the Republican Party has moved a long way from Reaganism. Despite that progress for the Democratic Party, Biden said he was running for reelection because "democracy is still at stake" in next year's election, a likely rematch with former President Trump, who has surged ahead in Republican primary polls. "Donald Trump and the MAGA Republicans are determined to destroy this democracy," Biden said during a private fundraising event Tuesday evening at the Atherton mansion of Democratic donors and philanthropists Liz Simons and Mark Heising in San Francisco.

The answer to this question may surprise you: AI is more Republican than Democrat. The study found that, when it comes to political values, AI is more closely aligned with the Republican Party than the Democratic Party. Specifically, the study found that AI is more likely to value individual liberty and free markets over government intervention and regulation. Additionally, AI is more likely to support traditional values such as patriotism and religion. Of course, it's important to remember that AI is not a person and cannot vote.

In 2011, a friend of mine in college asked me if I'd read The Happiness Hypothesis: Finding Modern Truth in Ancient Wisdom, by Jonathan Haidt. Haidt's aim was to probe and distill--and "savor"--the moral precepts of antiquity in the light of modern science. The 2006 book was an answer to an overabundance of too-little-appreciated advice. "We might have already encountered the Greatest Idea, the insight that would have transformed us had we savored it, taken it to heart, and worked it into our lives," Haidt wrote." My friend was happy to encounter it: Haidt helped him through a difficult breakup. I hadn't heard of the book, but I had heard of its author. A paper of Haidt's, "The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment," had been assigned in my moral psychology course, and I was in the middle of writing an essay that argued against its conclusion. Haidt wrote that reason, compared to emotion, typically matters little to what we believe is ...

Steven Pinker is a cognitive psychologist, linguist, and author of Bill Gates' two favorite books. However, his latest – Enlightenment Now – has some serious shortcomings centering on Pinker's misperceptions about climate change polarization. Pinker falls into the trap of'Both Siderism,' acknowledging the Republican Party's science denial, but also wrongly blaming liberals for the policy stalemate, telling Ezra Klein: There are organizations like Greenpeace and NRDC who are just dead set opposed to nuclear. There are also people on the left like Naomi Klein who are dead set against carbon pricing because it doesn't punish the polluters enough ... the people that you identify who believe in a) carbon pricing and b) expansion of nuclear power, I suspect they're a tiny minority of the people concerned with climate … What we need are polling data on how many people really would support carbon pricing and an expansion of nuclear and other low carbon energy sources. Here Pinker has created a strange straw man that bears no resemblance to the real population of American liberals and environmentalists.