Amazon Is Making an AI-Animated TV Show. The company licensed the character for a new Amazon series--made with AI--without her consent. Author and illustrator Loryn Brantz never imagined that a popular cartoon character she created almost a decade ago would one day be the subject of an intellectual property dispute involving BuzzFeed, Amazon's video streaming service, and generative artificial intelligence. But that's exactly the situation she finds herself in today. "Nothing said in good faith by managers and executives was followed through with," Brantz says of BuzzFeed, her former employer.

Are tech companies using your private data to train AI models? Leading tech companies are in a race to release and improve artificial intelligence (AI) products, leaving users in the United States to puzzle out how much of their personal data could be extracted to train AI tools. Meta (which owns Facebook, Instagram, Threads and WhatsApp), Google and LinkedIn have all rolled out AI app features that have the capacity to draw on users' public profiles or emails. Google and LinkedIn offer users ways to opt out of the AI features, while Meta's AI tool provides no means for its users to say "no, thanks." Anthropic's AI hacking claims divide experts Posts warned that the platforms' AI tool rollouts make most private information available for tech company harvesting .

Jeffrey and Cheryll Ku shared a video recorded on their Ring doorbell showing the terrifying moment the Eaton Fire approached their home. Altadena residents Jeffrey and Cheryll Ku shared harrowing footage of their Jan. The Kus are among Los Angeles residents forced to flee from the wildfires that tore through the city. On social media, the Kus described the experience as "34 minutes of pure terror." "The Eaton fire had just started in the hillside above us and we had to act FAST," Jeffrey Ku wrote in an Instagram post.

TV actress Michelle Keegan has revealed she's expecting a baby with her husband, former footballer Mark Wright. Taking to Instagram, the Coronation Street star posted a photo of the couple on the beach to break the news on Sunday. In the caption, Michelle – who is holding her growing baby bump – wrote: '2025 is going to be a special one for us...' While the pair have yet to reveal the due date for their baby, AI now predicts what the offspring will look like. MailOnline asked AI tools Midjourney and ChatGPT: 'Can you generate a realistic image of British actress Michelle Keegan and her husband Mark Wright's baby?' These AI image generators use trained artificial'neural networks' – programmes inspired by the human brain – to create images from scratch.

The work presented in this paper makes three scientific contributions with a specific focus on mining and analysis of COVID-19-related posts on Instagram. First, it presents a multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset, available at https://dx.doi.org/10.21227/d46p-v480, contains Instagram posts in 161 different languages as well as 535,021 distinct hashtags. After the development of this dataset, multilingual sentiment analysis was performed, which involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset. Second, it presents the results of performing sentiment analysis per year from 2020 to 2024. The findings revealed the trends in sentiment related to COVID-19 on Instagram since the beginning of the pandemic. For instance, between 2020 and 2024, the sentiment trends show a notable shift, with positive sentiment decreasing from 38.35% to 28.69%, while neutral sentiment rising from 44.19% to 58.34%. Finally, the paper also presents findings of language-specific sentiment analysis. This analysis highlighted similar and contrasting trends of sentiment across posts published in different languages on Instagram. For instance, out of all English posts, 49.68% were positive, 14.84% were negative, and 35.48% were neutral. In contrast, among Hindi posts, 4.40% were positive, 57.04% were negative, and 38.56% were neutral, reflecting distinct differences in the sentiment distribution between these two languages.

The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. No prior work related to social media mining has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper aims to address this research gap and makes two scientific contributions to this field. First, it presents a multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. The dataset, available at https://dx.doi.org/10.21227/7fvc-y093, contains Instagram posts about mpox in 52 languages. For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were performed. This process included classifying each post into (i) one of the sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutral, (ii) hate or not hate, and (iii) anxiety/stress detected or no anxiety/stress detected. These results are presented as separate attributes in the dataset. Second, this paper presents the results of performing sentiment analysis, hate speech analysis, and anxiety or stress analysis. The variation of the sentiment classes - fear, surprise, joy, sadness, anger, disgust, and neutral were observed to be 27.95%, 2.57%, 8.69%, 5.94%, 2.69%, 1.53%, and 50.64%, respectively. In terms of hate speech detection, 95.75% of the posts did not contain hate and the remaining 4.25% of the posts contained hate. Finally, 72.05% of the posts did not indicate any anxiety/stress, and the remaining 27.95% of the posts represented some form of anxiety/stress.

Minutes after the presidential debate ended on Tuesday, Taylor Swift mobilized her enormous fanbase in support of Kamala Harris by endorsing her in an Instagram post that quickly garnered 8 million likes. Swift's decision wasn't altogether surprising, given that she supported Joe Biden in the 2020 election and recently offered hints, in true Taylor fashion, that she was headed in this direction. But what was especially notable in her Instagram post was that it spent as much time praising Kamala Harris as it did warning the public about the dangers of AI. "Recently I was made aware that AI of'me' falsely endorsing Donald Trump's presidential run was posted to his site. It really conjured up my fears around AI, and the dangers of spreading misinformation," Swift wrote. "It brought me to the conclusion that I need to be very transparent about my actual plans for this election as a voter. The simplest way to combat misinformation is with the truth."

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI's GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs.

Social media influencers account for a growing share of marketing worldwide. We demonstrate the existence of a novel form of market failure in this advertising market: influencer cartels, where groups of influencers collude to increase their advertising revenue by inflating their engagement. Our theoretical model shows that influencer cartels can improve consumer welfare if they expand social media engagement to the target audience, or reduce welfare if they divert engagement to less relevant audiences. We validate the model empirically using novel data on influencer cartels combined with machine learning tools, and derive policy implications for how to maximize consumer welfare.

Excited to learn about Artificial Intelligence and how you are already using it every day? Join Ornella Altunyan and Dmitry Soshkinov as they show you how to use machine learning - you might even get some tips on how to increase your likes on Instagram. We'll walk through some key concepts, show off some projects with AI and ML on Azure, and share resources for continued learning.