Online discussions frequently involve conspiracy theories, which can contribute to the proliferation of belief in them. However, not all discussions surrounding conspiracy theories promote them, as some are intended to debunk them. Existing research has relied on simple proxies or focused on a constrained set of signals to identify conspiracy theories, which limits our understanding of conspiratorial discussions across different topics and online communities. This work establishes a general scheme for classifying discussions related to conspiracy theories based on authors' perspectives on the conspiracy belief, which can be expressed explicitly through narrative elements, such as the agent, action, or objective, or implicitly through references to known theories, such as chemtrails or the New World Order. We leverage human-labeled ground truth to train a BERT-based model for classifying online CTs, which we then compared to the Generative Pre-trained Transformer machine (GPT) for detecting online conspiratorial content. Despite GPT's known strengths in its expressiveness and contextual understanding, our study revealed significant flaws in its logical reasoning, while also demonstrating comparable strengths from our classifiers. We present the first large-scale classification study using posts from the most active conspiracy-related Reddit forums and find that only one-third of the posts are classified as positive. This research sheds light on the potential applications of large language models in tasks demanding nuanced contextual comprehension.

Large Language Model (LLM) techniques play an increasingly important role in Natural Language to SQL (NL2SQL) translation. LLMs trained by extensive corpora have strong natural language understanding and basic SQL generation abilities without additional tuning specific to NL2SQL tasks. Existing LLMs-based NL2SQL approaches try to improve the translation by enhancing the LLMs with an emphasis on user intention understanding. However, LLMs sometimes fail to generate appropriate SQL due to their lack of knowledge in organizing complex logical operator composition. A promising method is to input the LLMs with demonstrations, which include known NL2SQL translations from various databases. LLMs can learn to organize operator compositions from the input demonstrations for the given task. In this paper, we propose PURPLE (Pre-trained models Utilized to Retrieve Prompts for Logical Enhancement), which improves accuracy by retrieving demonstrations containing the requisite logical operator composition for the NL2SQL task on hand, thereby guiding LLMs to produce better SQL translation. PURPLE achieves a new state-of-the-art performance of 80.5% exact-set match accuracy and 87.8% execution match accuracy on the validation set of the popular NL2SQL benchmark Spider. PURPLE maintains high accuracy across diverse benchmarks, budgetary constraints, and various LLMs, showing robustness and cost-effectiveness.

Polarization, declining trust, and wavering support for democratic norms are pressing threats to U.S. democracy. Exposure to verified and quality news may lower individual susceptibility to these threats and make citizens more resilient to misinformation, populism, and hyperpartisan rhetoric. This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment (from 1/19/2023 to 2/3/2023) on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. To further test differential effects by gender of the bots, treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our over-time intervention enhances the following of news media organization, the sharing and the liking of news content and the tweeting about politics and the liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control. Most of these results, however, were small in magnitude and confined to the already politically interested Twitter users, as indicated by their pre-treatment tweeting about politics. These findings have implications for social media and news organizations, and also offer direction for future work on how Large Language Models and other computational interventions can effectively enhance individual on-platform engagement with quality news and public affairs.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Political ads featuring deceptive synthetic media would be required to include disclosure language under a bill passed Thursday by the New Hampshire House. Sophisticated artificial intelligence tools, such as voice-cloning software and image generators, already are in use in elections in the U.S. and around the world, leading to concerns about the rapid spread of misinformation. The New Hampshire State House, in Concord, New Hampshire, as photographed in April 2017.

AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative impacts and talks potential federal legislation to control it. Books about the dizzying impact of the internet and artificial intelligence are among finalists for a new book prize that aims to help fix the gender imbalance in nonfiction publishing. The shortlisted six books for the inaugural Women's Prize for Nonfiction, announced on Wednesday, include Canadian author-activist Naomi Klein's "Doppleganger," a plunge into online misinformation, and British journalist Madhumita Murgia's "Code-Dependent: Living in the Shadow of AI." The 38,000 award is a sister to the 29-year-old Women's Prize for Fiction and is open to female English-language writers from any country in any nonfiction genre. The finalists also include autobiographical works -- poet Safiya Sinclair's "How to Say Babylon: A Jamaican Memoir" and British art critic Laura Cumming's "Thunderclap: A Memoir of Art and Life and Sudden Death."

When OpenAI revealed its new generative video model, Sora, last month, it invited a handful of filmmakers to try it out. This week the company published the results: seven surreal short films that leave no doubt that the future of generative video is coming fast. The first batch of models that could turn text into video appeared in late 2022, from companies including Meta, Google, and video-tech startup Runway. It was a neat trick, but the results were grainy, glitchy, and just a few seconds long. Fast-forward 18 months, and the best of Sora's high-definition, photorealistic output is so stunning that some breathless observers are predicting the death of Hollywood.

One problem with most generative video tools is that it's hard to maintain consistency across frames. When OpenAI asked Shy Kids to try out Sora, the band wanted to see how far they could push it. "We thought a fun, interesting experiment would be--could we make a consistent character?" says Shy Kids member Walter Woodman. "We think it was mostly successful." Generative models can also struggle with anatomical details like hands and faces.

Fast-forward 18 months, and the best of Sora's high-definition, photorealistic output is so stunning that some breathless observers are predicting the death of Hollywood. Runway's latest models can produce short clips that rival those made by blockbuster animation studios. Midjourney and Stability AI, the firms behind two of the most popular text-to-image models, are now working on video as well. A number of companies are racing to make a business on the back of these breakthroughs. Most are figuring out what that business is as they go.

"In the industrial age, automation came for the shoemaker and the factory-line worker," writes Dennis Yi Tenen near the start of Literary Theory for Robots. "Today, it has come for the writer, the professor, the physician, the programmer and the attorney." Like the end-of-the-planet movies that pelted the multiplexes at the turn of the millennium, newspapers and – increasingly – bookshops are awash with economists, futurologists and social semioticians talking up, down and about artificial intelligence. Even Henry Kissinger, in The Age of AI (2021), spoke of "epoch-making transformations" and an imminent "revolution in human affairs". Tenen, a tenured professor of English at New York's Columbia University, isn't nearly as apocalyptic as he initially makes out.

Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the performance ceiling of the specific recommendation task. Motivated by this, in this paper, we propose a flexible framework dubbed heterogeneous interaction rating network (HIRE). HIRE dose not solely rely on the pre-defined interaction pattern or the manually constructed heterogeneous information network. Instead, we devise a Heterogeneous Interaction Module (HIM) to jointly model the heterogeneous interactions and directly infer the important interactions via the observed data. In the experiments, we evaluate our model under three cold-start settings on three real-world datasets. The experimental results show that HIRE outperforms other baselines by a large margin. Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model.