Reddit's human content wins amid the AI flood For Ines Tan there's one particular site she turns to again and again for advice - and that's Reddit. Tan, who works in communications, regularly jumps on the site for skincare advice, to view reactions to shows she watches, such as The Traitors, and for help planning her upcoming wedding in May. It's a very empathetic place, she says of Reddit. For my wedding, I've found help emotionally, logistically and inspiration-wise. Tan believes people are consulting the online discussion platform more as they're craving human interaction in the world of increasing AI slop.

Reddit is being touted as an antidote to AI-generated content. Reddit is being touted as an antidote to AI-generated content. Platform is now Britain's fourth most visited social media site as users seek out human-generated content Reddit, the online discussion platform, has overtaken TikTok as Britain's fourth most visited social media service, as search algorithms and gen Z have dramatically transformed its prominence. The platform has undergone huge growth over the last two years, with an 88% increase in the proportion of UK internet users it reaches. Three in five Brits online now encounter the site, up from a third in 2023, according to Ofcom .

Users of AI image generators are offering each other instructions on how to use the tech to alter pictures of women into realistic, revealing deepfakes. Some users of popular chatbots are generating bikini deepfakes using photos of fully clothed women as their source material. Most of these fake images appear to be generated without the consent of the women in the photos. Some of these same users are also offering advice to others on how to use the generative AI tools to strip the clothes off of women in photos and make them appear to be wearing bikinis. Under a now-deleted Reddit post titled "gemini nsfw image generation is so easy," users traded tips for how to get Gemini, Google's generative AI model, to make pictures of women in revealing clothes.

"You'd be hard pressed to find someone who has spent more time building or obsessing over the online zeitgeist, for better or for worse," Alexis Ohanian introduced himself at the BRIDGE Summit in Abu Dhabi on Monday. Ohanian, a founding partner at venture capital firm Seven Seven Six, is perhaps best known as the co-founder and former executive chairman of Reddit. "Being chronically online was part of the job," he said, as part of a conversation with TIME executive editor Nikhil Kumar. TIME is a media partner of the BRIDGE Summit, which has gathered a global community of creators, policymakers, investors, technologists, media institutions, and cultural leaders to discuss the landscape and future of media. But the advent of artificial intelligence has made platforms like Reddit, which once served as hubs of connection, less human, Ohanian said.

This study presents a comprehensive analysis of public sentiment toward traffic management policies in Knoxville, Tennessee, utilizing social media data from Twitter and Reddit platforms. We collected and analyzed 7906 posts spanning January 2022 to December 2023, employing Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling. Our findings reveal predominantly negative sentiment, with significant variations across platforms and topics. Twitter exhibited more negative sentiment compared to Reddit. Topic modeling identified six distinct themes, with construction-related topics showing the most negative sentiment while general traffic discussions were more positive. Spatiotemporal analysis revealed geographic and temporal patterns in sentiment expression. The research demonstrates social media's potential as a real-time public sentiment monitoring tool for transportation planning and policy evaluation.

Reddit is considered one of the most human spaces left on the internet, but mods and users are overwhelmed with slop posts in the most popular subreddits. A Reddit post about a bride who demands a wedding guest wear a specific, unflattering shade is sure to provoke rage, let alone one about a bridesmaid or mother of the groom who wants to wear white. A scenario where a parent asks someone on an airplane to switch seats so they can sit next to their young child is likely to invoke the same rush of anger. But those posts may trigger a Reddit moderator's annoyance for a different reason--they are common themes within a growing genre of AI -generated, fake posts. These are examples that spring to mind for Cassie, one of dozens of moderators for r/AmItheAsshole .

Nova Forge lets Amazon's customers train frontier models for different tasks--a potential breakthrough in making AI actually useful for businesses. Amazon has announced a new family of frontier artificial intelligence models--and a new way for customers to build frontier models of their own. The ecommerce giant announced the second generation of its Nova AI models at re:Invent, a company conference held in Las Vegas. The models are nowhere near as popular as those offered by rivals like OpenAI and Google, but Amazon's plan to make them highly customizable could see them gain traction with its cloud users. Amazon detailed two improved large language models, Nova Lite and Nova Pro; a new realtime voice model called Nova Sonic; and a more experimental model called Nova Omni that performs a simulated kind of reasoning using images, audio, and video as well as text.

Social media data has become a vital resource for studying mental health, offering real-time insights into thoughts, emotions, and behaviors that traditional methods often miss. Progress in this area has been facilitated by benchmark datasets for mental health analysis; however, most existing benchmarks have become outdated due to limited data availability, inadequate cleaning, and the inherently diverse nature of social media content (e.g., multilingual and harmful material). We present a new benchmark dataset, \textbf{MindSET}, curated from Reddit using self-reported diagnoses to address these limitations. The annotated dataset contains over \textbf{13M} annotated posts across seven mental health conditions, more than twice the size of previous benchmarks. To ensure data quality, we applied rigorous preprocessing steps, including language filtering, and removal of Not Safe for Work (NSFW) and duplicate content. We further performed a linguistic analysis using LIWC to examine psychological term frequencies across the eight groups represented in the dataset. To demonstrate the dataset utility, we conducted binary classification experiments for diagnosis detection using both fine-tuned language models and Bag-of-Words (BoW) features. Models trained on MindSET consistently outperformed those trained on previous benchmarks, achieving up to an \textbf{18-point} improvement in F1 for Autism detection. Overall, MindSET provides a robust foundation for researchers exploring the intersection of social media and mental health, supporting both early risk detection and deeper analysis of emerging psychological trends.

Visual content memorability has intrigued the scientific community for decades, with applications ranging widely, from understanding nuanced aspects of human memory to enhancing content design. A significant challenge in progressing the field lies in the expensive process of collecting memorability annotations from humans. This limits the diversity and scalability of datasets for modeling visual content memorability. Most existing datasets are limited to collecting aggregate memorability scores for visual content, not capturing the nuanced memorability signals present in natural, open-ended recall descriptions. In this work, we introduce the first large-scale unsupervised dataset designed explicitly for modeling visual memorability signals, containing over 82,000 videos, accompanied by descriptive recall data. We leverage tip-of-the-tongue (ToT) retrieval queries from online platforms such as Reddit. We demonstrate that our unsupervised dataset provides rich signals for two memorability-related tasks: recall generation and ToT retrieval. Large vision-language models fine-tuned on our dataset outperform state-of-the-art models such as GPT-4o in generating open-ended memorability descriptions for visual content. We also employ a contrastive training strategy to create the first model capable of performing multimodal ToT retrieval. Our dataset and models present a novel direction, facilitating progress in visual content memorability research.

Sparse GNN aggregations (CSR SpMM/SDDMM) vary widely in performance with degree skew, feature width, and GPU micro-architecture. We present AutoSAGE, an input-aware CUDA scheduler that chooses tiling and mapping per input using a lightweight estimate refined by on-device micro-probes, with a guardrail that safely falls back to vendor kernels and a persistent cache for deterministic replay. AutoSAGE covers SpMM and SDDMM and composes into a CSR attention pipeline (SDDMM -> row-softmax -> SpMM). On Reddit and OGBN-Products, it matches vendor baselines at bandwidth-bound feature widths and finds gains at small widths; on synthetic sparsity and skew stress tests it achieves up to 4.7x kernel-level speedups. We release CUDA sources, Python bindings, a reproducible harness, and replayable cache logs.