Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.

In this paper, we present the fifth installment of the NELA-GT datasets, NELA-GT-2022. The dataset contains 1,778,361 articles from 361 outlets between January 1st, 2022 and December 31st, 2022. Just as in past releases of the dataset, NELA-GT-2022 includes outlet-level veracity labels from Media Bias/Fact Check and tweets embedded in collected news articles. The NELA-GT-2022 dataset can be found at: https://doi.org/10.7910/DVN/AMCV2H

What movie do these emojis describe? That prompt was one of 204 tasks chosen last year to test the ability of various large language models (LLMs) -- the computational engines behind AI chatbots such as ChatGPT. The simplest LLMs produced surreal responses. "The movie is a movie about a man who is a man who is a man," one began. Medium-complexity models came closer, guessing The Emoji Movie.

Meet the newest broadcast journalist recruited by China's state-controlled newspaper, who is powered by artificial intelligence (AI). Named'Ren Xiaorong', the virtual young woman claims to be able to answer questions and deliver news broadcasts 24 hours a day, 365 days a year. But there's a catch - the avatar is only able to answer pre-set questions, and the responses she gives heavily promote the Central Committee of the Chinese Communist Party (CCP) line. The questions relate to the'Two Sessions' political conference that ended on Monday, where delegates in the country discussed and approved legislation. Ren has been developed by the CCP's official news outlet, People's Daily, and says she has the skills of'thousands of news anchors'. Meet the newest broadcast journalist recruited by China's state-controlled newspaper, who is powered by artificial intelligence (AI) She was unveiled on Weibo, a social networking platform in China, in a video, dressed smartly in a black blazer and stood in front of a city skyline.

Samsung users have been left furious after discovering that their smartphone's'Space Zoom' camera tool uses artificial intelligence (AI) to'enhance' photos. A Reddit user called out the tech giant after they purposefully blurred a photo of the moon, and then took a picture of that photo with their Galaxy S23 Ultra smartphone. The resulting image was much more detailed, showing craters and surface texture that were not visible in the original. User'ibreakphotos' says that this proves Samsung's AI is'doing most of the work' as'the optics aren't capable of resolving the detail that you see'. The company has responded, admitting that it uses AI to recognise when the moon is in shot, but did not rule out the internet sleuth's accusation.

Fox News correspondent Mike Tobin has the latest on tensions amid the Russia-Ukraine war on'Special Report.' EXCLUSIVE: House Foreign Affairs Committee Chairman Michael McCaul called Russia's interception of a U.S. drone a "blatant intimidation tactic," and stressed the need for the United States to continue its support for Ukraine and security efforts in the region. A Russian Su-27 fighter plane collided with a U.S. MQ-9 Reaper drone, which was conducting "routine operations" over the Black Sea on Tuesday. The jet in question was one of two Su-27's flying in tandem when the collision occurred in international airspace over international waters. "To be clear, the Black Sea is not a Russian lake. The U.S. surveillance drone intercepted by a Russian aircraft was operating in international airspace," McCaul told Fox News Digital.

Last November, at the Stockholm University of the Arts, a human and an AI made music together. The performance began with musician David Dolan playing a grand piano into a microphone. As he played, a computer system, designed and overseen by composer and Kingston University researcher Oded Ben-Tal, "listened" to the piece, extracting data on pitch, rhythm, and timbre. Then, it added its own accompaniment, improvising just like a person would. Some sounds were transformations of Dolan's piano; some were new sounds synthesized on the fly.

Billionaire Robin Li launches on March 16 Baidu's conversational chatbot, Ernie Bot, at the company's headquarters in Beijing. Billionaire and Baidu CEO Robin Li on Thursday officially unveiled the Chinese search giant's answer to ChatGPT, as his company seeks to outdo domestic competitors in developing conversational artificial intelligence technologies, which have recently taken the world's second largest economy by storm. At a highly anticipated event at Baidu's Beijing headquarters, the 54-year-old Li walked onto the stage and displayed the company's chatbot service, Ernie Bot. But instead of a live demo, the audience was only shown videos of Ernie Bot performing a range of prepared and pre-recorded tasks, including answering questions about a popular novelist, composing press releases as well as working on mathematics problems. So attendees weren't given a chance to interact with Ernie Bot in real-time, though the company said the service will be available to a selected group of users starting on Thursday.

A study was conducted to prove AI software could be used to translate and generate illustrations without any human intervention. This was done with the purpose of showing and distributing it to the external customer, Pratham Books. The project aligns with the company's vision by leveraging the generalisation and scalability of Machine Learning algorithms, offering significant cost efficiency increases to a wide range of literary audiences in varied geographical locations. A comparative study methodology was utilised to determine the best performant method out of the 3 devised, Prompt Augmentation using Keywords, CLIP Embedding Mask, and Cross Attention Control with Editorial Prompts. A thorough evaluation process was completed using both quantitative and qualitative measures. Each method had its own strengths and weaknesses, but through the evaluation, method 1 was found to have the best yielding results. Promising future advancements may be made to further increase image quality by incorporating Large Language Models and personalised stylistic models. The presented approach can also be adapted to Video and 3D sculpture generation for novel illustrations in digital webbooks.

Emotion recognition from a given music track has heavily relied on acoustic features, social tags, and metadata but is seldom focused on lyrics. There are no datasets of Indian language songs that contain both valence and arousal manual ratings of lyrics. We present a new manually annotated dataset of Telugu songs' lyrics collected from Spotify with valence and arousal annotated on a discrete scale. A fairly high inter-annotator agreement was observed for both valence and arousal. Subsequently, we create two music emotion recognition models by using two classification techniques to identify valence, arousal and respective emotion quadrant from lyrics. Support vector machine (SVM) with term frequency-inverse document frequency (TF-IDF) features and fine-tuning the pre-trained XLMRoBERTa (XLM-R) model were used for valence, arousal and quadrant classification tasks. Fine-tuned XLMRoBERTa performs better than the SVM by improving macro-averaged F1-scores of 54.69%, 67.61%, 34.13% to 77.90%, 80.71% and 58.33% for valence, arousal and quadrant classifications, respectively, on 10-fold cross-validation. In addition, we compare our lyrics annotations with Spotify's annotations of valence and energy (same as arousal), which are based on entire music tracks. The implications of our findings are discussed. Finally, we make the dataset publicly available with lyrics, annotations and Spotify IDs.