Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its reception. So far, such studies have been narrow, in that they use a fixed set of pre-defined frames and focus only on the text, ignoring the visual contexts in which those texts appear. Especially for framing in the news, this leaves out valuable information about editorial choices, which include not just the written article but also accompanying photographs. To overcome such limitations, we present a method for conducting multi-modal, multi-label framing analysis at scale using large (vision-)language models. Grounding our work in framing theory, we extract latent meaning embedded in images used to convey a certain point and contrast that to the text by comparing the respective frames used. We also identify highly partisan framing of topics with issue-specific frame analysis found in prior qualitative work. We demonstrate a method for doing scalable integrative framing analysis of both text and image in news, providing a more complete picture for understanding media bias.

The proliferation of LLM-based conversational agents has resulted in excessive disclosure of identifiable or sensitive information. However, existing technologies fail to offer perceptible control or account for users' personal preferences about privacy-utility tradeoffs due to the lack of user involvement. To bridge this gap, we designed, built, and evaluated Rescriber, a browser extension that supports user-led data minimization in LLM-based conversational agents by helping users detect and sanitize personal information in their prompts. Our studies (N=12) showed that Rescriber helped users reduce unnecessary disclosure and addressed their privacy concerns. Users' subjective perceptions of the system powered by Llama3-8B were on par with that by GPT-4o. The comprehensiveness and consistency of the detection and sanitization emerge as essential factors that affect users' trust and perceived protection. Our findings confirm the viability of smaller-LLM-powered, user-facing, on-device privacy controls, presenting a promising approach to address the privacy and trust challenges of AI.

MAINE MASSACRE - Police identify gunman who allegedly shot and killed four people, wounded three others. 'HALLUCINATIONS' - Musk warns left-wing experts are'training the AI to lie' about history, politics. PRESIDENTIAL PAY - What Biden and first lady are reporting as their federal adjusted gross income. LIFE-SAVING FACTOR - Florida swim instructor's viral TikTok warns parents to avoid buying blue bathing suits for kids: Here's why. REINING IN? - GOP senators open to AI regulation talks after Schumer pushes for guardrails.

It has not been a good week for MoviePass. And it's been a similarly tough one for its customers. The cinema ticket subscription service – which allows film fans to see a movie each day for a low subscription fee – has been hit by a whole raft of problems. Some have worried that MoviePass might soon not be around at all. And whatever happens to the company, the issues have started to hit. Users have had to struggle through the service going offline, and recent reports suggest that many films may soon be excluded from MoviePass in the future.