In our work, though, we do not seek to influence As generative techniques pervade the audio domain, the behavior of the model so drastically, but rather to there has been increasing interest in tracing back through "tag" the data in such a way that the model reproduces these complicated models to understand how they draw the tag, similarly to how [10] watermark their training on their training data to synthesize new examples, both data for a diffusion image model. We are also inspired to ensure that they use properly licensed data and also to by the recent lawsuit by Getty Images against Stable elucidate their black box behavior. In this paper, we show Diffusion when it was discovered that the latter would that if imperceptible echoes are hidden in the training often reproduce the former's watermarks in its output data, a wide variety of audio to audio architectures (differentiable [29]. We would like to do something similar with audio, digital signal processing (DDSP), Realtime but to keep it imperceptible. Audio Variational autoEncoder (RAVE), and "Dance All of the above approaches use neural networks to Diffusion") will reproduce these echoes in their outputs.

Accurately predicting sports viewership is crucial for optimizing ad sales and revenue forecasting. Social media platforms, such as Reddit, provide a wealth of user-generated content that reflects audience engagement and interest. In this study, we propose a regression-based approach to predict sports viewership using social media metrics, including post counts, comments, scores, and sentiment analysis from TextBlob and VADER. Through iterative improvements, such as focusing on major sports subreddits, incorporating categorical features, and handling outliers by sport, the model achieved an $R^2$ of 0.99, a Mean Absolute Error (MAE) of 1.27 million viewers, and a Root Mean Squared Error (RMSE) of 2.33 million viewers on the full dataset. These results demonstrate the model's ability to accurately capture patterns in audience behavior, offering significant potential for pre-event revenue forecasting and targeted advertising strategies.

Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.

Mis- and disinformation, commonly collectively called fake news, continue to menace society. Perhaps, the impact of this age-old problem is presently most plain in politics and healthcare. However, fake news is affecting an increasing number of domains. It takes many different forms and continues to shapeshift as technology advances. Though it arguably most widely spreads in textual form, e.g., through social media posts and blog articles. Thus, it is imperative to thwart the spread of textual misinformation, which necessitates its initial detection. This thesis contributes to the creation of representations that are useful for detecting misinformation. Firstly, it develops a novel method for extracting textual features from news articles for misinformation detection. These features harness the disparity between the thematic coherence of authentic and false news stories. In other words, the composition of themes discussed in both groups significantly differs as the story progresses. Secondly, it demonstrates the effectiveness of topic features for fake news detection, using classification and clustering. Clustering is particularly useful because it alleviates the need for a labelled dataset, which can be labour-intensive and time-consuming to amass. More generally, it contributes towards a better understanding of misinformation and ways of detecting it using Machine Learning and Natural Language Processing.

Last week, to formally consecrate the beginning of the holiday season, my fiancée threw on Christmas With the Kranks. The wretched 2004 Tim Allen film notched an impressively bad 5 percent on Rotten Tomatoes, with most critics complaining about the screenplay's awkward marriage of lifeless Middle American sentimentality with the sort of visual gags you might find in Progressive commercials. Christmas With the Kranks is not making the Criterion Collection anytime soon, and I think it's fair to say that there are better ways to spend a winter evening, but if you value the season like we do--and intend to have a Christmas movie on-screen at all times until New Year's--then you must ration the heavy hitters of the genre for the premium slots on the calendar. Or, in other words, if you want to watch Home Alone on Dec. 22, then you might be forced to spend a night with Tim Allen on Dec. 5. We all know what the classics are. The Mount Rushmore of Christmas movies, at least according to mainstream millennial opinion, are Elf, A Charlie Brown Christmas, the Chuck Jones–animated How the Grinch Stole Christmas, and the aforementioned Home Alone.

Rep. Jeff Van Drew, R-N.J., responds to high-ranking officials who have dismissed his previous claim that'reliable' sources said mystery drones flying over New Jersey originated from an Iranian mothership. Rep. Jeff Van Drew, R-N.J., fired back after high-ranking officials dismissed claims he gathered from "very qualified" and "reliable" sources linking mystery drones flying over New Jersey airspace to an Iranian "mothership" parked off the U.S. East Coast. "They've been incredibly stupid and incredibly weak with this," Van Drew said Thursday on "America's Newsroom." "We know they're not backyard drones that some hobbyist is using because they're much more sophisticated than that. We know that they're not a commercial company within the United States because we don't even have this level of sophistication yet. We are a full decade behind where China is with drones. The government claims it's not them. They say it's not them, so who is it?"

Apple Intelligence is essentially a snazzy brand name for Apple's new-found focus on AI, triggered by the huge success of the ChatGPT. Here's a look at some of the best features of Apple Intelligence, which comes to the UK via the new iOS 18.2 operating system. Surely the biggest part of Apple Intelligence is the integration of OpenAI's hugely popular chatbot ChatGPT with Siri, Apple's in-built virtual assistant. With better'language-understanding capabilities' enabled by ChatGPT, Siri will help you across multiple apps and'accelerate everyday tasks', Apple said. You'll be able to press and hold the side button to activate Siri as normal, but with ChatGPT behind it Siri will be able to'answer thousands of questions about how to do something' that it couldn't before.

Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas suggests that specific user characteristics impact the users' perception of the explanation. However, we rarely find this type of evaluation for recommender systems explanations. This paper addresses this gap by surveying 124 papers in which recommender systems explanations were evaluated in user studies. We analyzed their participant descriptions and study results where the impact of user characteristics on the explanation effects was measured. Our findings suggest that the results from the surveyed studies predominantly cover specific users who do not necessarily represent the users of recommender systems in the evaluation domain. This may seriously hamper the generalizability of any insights we may gain from current studies on explanations in recommender systems. We further find inconsistencies in the data reporting, which impacts the reproducibility of the reported results. Hence, we recommend actions to move toward a more inclusive and reproducible evaluation.

Understanding when and why consumers engage with stories is crucial for content creators and platforms. While existing theories suggest that audience beliefs of what is going to happen should play an important role in engagement decisions, empirical work has mostly focused on developing techniques to directly extract features from actual content, rather than capturing forward-looking beliefs, due to the lack of a principled way to model such beliefs in unstructured narrative data. To complement existing feature extraction techniques, this paper introduces a novel framework that leverages large language models to model audience forward-looking beliefs about how stories might unfold. Our method generates multiple potential continuations for each story and extracts features related to expectations, uncertainty, and surprise using established content analysis techniques. Applying our method to over 30,000 book chapters from Wattpad, we demonstrate that our framework complements existing feature engineering techniques by amplifying their marginal explanatory power on average by 31%. The results reveal that different types of engagement-continuing to read, commenting, and voting-are driven by distinct combinations of current and anticipated content features. Our framework provides a novel way to study and explore how audience forward-looking beliefs shape their engagement with narrative media, with implications for marketing strategy in content-focused industries.

With the advent of publicly available AI-based text-to-image systems, the process of creating photorealistic but fully synthetic images has been largely democratized. This can pose a threat to the public through a simplified spread of disinformation. Machine detectors and human media expertise can help to differentiate between AI-generated (fake) and real images and counteract this danger. Although AI generation models are highly prompt-dependent, the impact of the prompt on the fake detection performance has rarely been investigated yet. This work therefore examines the influence of the prompt's level of detail on the detectability of fake images, both with an AI detector and in a user study. For this purpose, we create a novel dataset, COCOXGEN, which consists of real photos from the COCO dataset as well as images generated with SDXL and Fooocus using prompts of two standardized lengths. Our user study with 200 participants shows that images generated with longer, more detailed prompts are detected significantly more easily than those generated with short prompts. Similarly, an AI-based detection model achieves better performance on images generated with longer prompts. However, humans and AI models seem to pay attention to different details, as we show in a heat map analysis.