Television networks face high financial risk when making programming decisions, often relying on limited historical data to forecast episodic viewership. This study introduces a machine learning framework that integrates natural language processing (NLP) features from over 25000 television episodes with traditional viewership data to enhance predictive accuracy. By extracting emotional tone, cognitive complexity, and narrative structure from episode dialogue, we evaluate forecasting performance using SARIMAX, rolling XGBoost, and feature selection models. While prior viewership remains a strong baseline predictor, NLP features contribute meaningful improvements for some series. We also introduce a similarity scoring method based on Euclidean distance between aggregate dialogue vectors to compare shows by content. Tested across diverse genres, including Better Call Saul and Abbott Elementary, our framework reveals genre-specific performance and offers interpretable metrics for writers, executives, and marketers seeking data-driven insight into audience behavior.

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.

Only in America would people get randy heading into a solar eclipse event. Pornhub revealed that terms like'eclipse sex' and'eclipse orgasm' became the top searches on Monday, rising by a shocking 6,800 percent. Curious users looked up friskier terms like'sex during eclipse' and'eclipse my c***.' 'Eclipse karma' also topped the list, which appeared to be a porn star performing sexual acts on different partners. However, people appeared to take a break from their Pornhub eclipse fetishes for roughly an hour to watch the celestial event, but resumed normal levels once the moon bypassed the sun. If people were hoping to find a solar eclipse sub-genre on Pornhub, they were left disappointed. There was one video of the eclipse uploaded to Pornhub - but nothing explicit in the footage.

This paper examines the diffusion of media slant, specifically how partisan content from national cable news affects local newspapers in the U.S., 2005-2008. We use a text-based measure of cable news slant trained on content from Fox News Channel (FNC), CNN, and MSNBC to analyze how local newspapers adopt FNC's slant over CNN/MSNBC's. Our findings show that local news becomes more similar to FNC content in response to an exogenous increase in local FNC viewership. This shift is not limited to borrowing from cable news, but rather, local newspapers' own content changes. Further, cable TV slant polarizes local news content.

Super Bowl advertisers this year want Americans to forget about pandemic woes and focus on the future: of electric vehicles, mind-reading Alexas, robots and cryptocurrency, and also to harken back to the nostalgic past of '90s movies like Austin Powers and The Cable Guy. The Los Angeles Rams will take on the Cincinnati Bengals during Super Bowl 56 on Sunday at the SoFi Stadium in Inglewood, California. But for many, the big show of the night will be the commercials. Advertisers are hoping to deliver a dose of escapism with light humour and star-studded entertainment amid the pandemic, high inflation and tensions between Russia and Ukraine. "Marketers are recognising Americans have had a very heavy, difficult two-year period and are responding by bringing some good old-fashioned entertainment for Super Bowl Sunday," said Kimberly Whitler, marketing professor at the University of Virginia.

According to Hans Moravec, adjunct faculty member at the Robotics Institute of Carnegie Mellon University, a representation "landscape of human competence" has often been used to gauge the potential of AI. He represented AI capability as a rising sea level, increasingly covering the landscape of human competence. Human functions like rote memorization and arithmetic were the lowlands to be first flooded by AI. Playing the Go game, speech recognition and language translation were considered mountains. But recently, AI has clearly outdone humans in these areas too.

Machine Learning still comes under the domain of unchartered territories, and we are constantly learning the applications of this modern-day technology. It has enabled computer systems to learn progressively and analyze patterns in data to adjust their actions automatically. Machine Learning algorithms have found widespread applications in healthcare, cybersecurity, data security, and the financial world. Even eCommerce portals such as Amazon and Flipkart are deploying cutting-edge Machine Learning capabilities to ascertain requirements and recommend products to their users. It was in 2016 that YouTube, the largest video sharing and viewing platform integrated machine learning in its suggestions algorithm, and it was not until the previous year that creators realized what hit their viewing metrics.

With revelations emerging since last Friday that political data company Cambridge Analytica obtained private info from more than 50 million Facebook accounts beginning in 2014 and later used it to boost the Trump presidential campaign, Facebook's data collection and use has again come under scrutiny. For many users, it's been an abrupt wakeup call about how much data they've been sharing with the company and the third-party apps that it hosts. Cambridge Analytica, for example, reportedly had access to users' locations, "likes," and other personal details and used it to develop psychographic profiles of voters' behavioral traits. The recent news should give users pause about the privacy configurations of their own accounts. If you are one such user, here's a quick tutorial on how to minimize the amount of data available on your Facebook account.

In 2008, four students at the MIT Sloan School of Management developed a system for captioning online video that was far more efficient than traditional methods, which involve pausing a video frequently to write text and mark time codes. The system used automated speech-recognition software to produce "rough-draft" transcripts, displayed on a simple interface, that could easily be edited. Landing a gig to caption videos from five MIT OpenCourseWare (OCW) classes, the students were able to caption 100 hours of content in a fraction the time of manual captioning. This marked the beginning of captioning-service company 3Play Media, which now boasts more than 1,000 clients and an equal number of contracted editors processing hundreds of hours of content per day. Clients include academic institutions, government agencies, and big-name companies -- such as Netflix, Viacom, and Time Warner Cable -- as well as many users of video-sharing websites.

We propose an online scheduling algorithm for posting crowdsourcing tasks which maximizes a novel metric called task viewership. This metric is computed using stochastic model based on coverage process and it measures the likelihood that a task is viewed by multiple crowd workers, which is correlated to the likelihood that it will be selected and completed.