Detecting when public discourse shifts in response to major events is crucial for understanding societal dynamics. Real-world data is high-dimensional, sparse, and noisy, making changepoint detection in this domain a challenging endeavor. In this paper, we leverage neural networks for changepoint detection in news data, introducing a method based on the so-called learning-by-confusion scheme, which was originally developed for detecting phase transitions in physical systems. We train classifiers to distinguish between articles from different time periods. The resulting classification accuracy is used to estimate the total variation distance between underlying content distributions, where significant distances highlight changepoints. We demonstrate the effectiveness of this method on both synthetic datasets and real-world data from The Guardian newspaper, successfully identifying major historical events including 9/11, the COVID-19 pandemic, and presidential elections. Our approach requires minimal domain knowledge, can autonomously discover significant shifts in public discourse, and yields a quantitative measure of change in content, making it valuable for journalism, policy analysis, and crisis monitoring.

During the COVID-19 pandemic, the news media coverage encompassed a wide range of topics that includes viral transmission, allocation of medical resources, and government response measures. There have been studies on sentiment analysis of social media platforms during COVID-19 to understand the public response given the rise of cases and government strategies implemented to control the spread of the virus. Sentiment analysis can provide a better understanding of changes in societal opinions and emotional trends during the pandemic. Apart from social media, newspapers have played a vital role in the dissemination of information, including information from the government, experts, and also the public about various topics. A study of sentiment analysis of newspaper sources during COVID-19 for selected countries can give an overview of how the media covered the pandemic. In this study, we select The Guardian newspaper and provide a sentiment analysis during various stages of COVID-19 that includes initial transmission, lockdowns and vaccination. We employ novel large language models (LLMs) and refine them with expert-labelled sentiment analysis data. We also provide an analysis of sentiments experienced pre-pandemic for comparison. The results indicate that during the early pandemic stages, public sentiment prioritised urgent crisis response, later shifting focus to addressing the impact on health and the economy. In comparison with related studies about social media sentiment analyses, we found a discrepancy between The Guardian with dominance of negative sentiments (sad, annoyed, anxious and denial), suggesting that social media offers a more diversified emotional reflection. We found a grim narrative in The Guardian with overall dominance of negative sentiments, pre and during COVID-19 across news sections including Australia, UK, World News, and Opinion

Recently, with the advancement of deep learning, several applications in text classification have advanced significantly. However, this improvement comes with a cost because deep learning is vulnerable to adversarial examples. This weakness indicates that deep learning is not very robust. Fortunately, the input of a text classifier is discrete. Hence, it can prevent the classifier from state-of-the-art attacks. Nonetheless, previous works have generated black-box attacks that successfully manipulate the discrete values of the input to find adversarial examples. Therefore, instead of changing the discrete values, we transform the input into its embedding vector containing real values to perform the state-of-the-art white-box attacks. Then, we convert the perturbed embedding vector back into a text and name it an adversarial example. In summary, we create a framework that measures the robustness of a text classifier by using the gradients of the classifier.

Police in Texas investigating a Tesla car crash in which two men died will serve search warrants on the company to ascertain if the vehicle's autopilot mode was engaged at the time of the incident. However Tesla's CEO, Elon Musk, has said the self-driving feature was not being used, based on an internal probe by the company. In the incident, two men, both in their 50s, were killed after their 2019 Tesla Model S crashed into a tree and caught fire. According to police reports, the car was travelling at a high speed and failed to negotiate a curve in the road. Texas police noted that nobody was at the driving seat at the time of impact, raising doubts about the involvement of the car's autopilot mode.

More and more is happening…. More connectivity occurs now in a calendar year than occurred in a million years a billion years ago. So somehow as we approach the present, we find ourselves in an ever denser realm of activity, interrelationship, connectivity, and the result of this is more of the same: producing a shrinking globe, ever more immersive technologies, dissolution of political, social, gender, class boundaries, of all sorts…. We're about to become unrecognizable to ourselves as a species. And then there's that new Netflix docudrama, The Social Dilemma, that, in the words of one review, "examines the various ways social media and social networking companies have manipulated human psychology to rewire the human brain and what it means for society in general."

Artificial Intelligence (AI) offers acceleration in the treatment process of patients as it can estimate and analyze data quickly when symptoms occur, according to an expert. Misdiagnoses experienced during examinations performed in diagnosis stages of diseases will significantly drop with AI, a social media specialist told Anadolu Agency. Deniz Unay said the process will take place when thousands of similar cases with patient history are analyzed in seconds and physician errors will be minimized. "According to a study conducted in the U.S., 20% of the medical errors occurred during the initial examination due to insufficient time for the patient-physician interviews, and these errors caused wrong treatment processes," Unay said. "Considering an estimated annual figure of around 87,000 cases around the world, the fact that artificial intelligence can estimate and analyze data quickly reveals that it can accelerate treatment processes," the expert opined.

The BBC is "really worried" by a new tactic by fake news propagandists and fraudsters who are exploiting the rise of chat apps to spread false content carrying the broadcaster's trusted branding. The organisation has found itself repeatedly targeted by what appears to be a mixture of state-backed political activists, opportunistic fraudsters and malicious pranksters, who are grafting the BBC's famous logo onto false reports that are shared on the largely unregulated chat platforms. The trend represents a new dimension in the fake news threat and a major challenge to democratic processes and to news organisations in protecting their brand reputations. The BBC last week felt obliged to issue a formal warning after a clip purporting to show the BBC reporting on the outbreak of nuclear war between Russian and NATO forces in the Baltic went viral on WhatsApp and other chat platforms as a piece of breaking news. In other instances, the BBC's branding has been used to give a false sense of authentication to concocted election results, and in creating malicious news'reports' designed to damage corporate targets.

Mary "Missy" Cummings, Director of the Humans and Autonomy Lab (HAL) at Duke University, and co-chair of the Global Future Council on Artificial Intelligence and Robotics, says the technology will work best in collaboration with humans. While cab drivers may fear for their jobs, she envisages a worldwide shortage of roboticists in 2030. Artificial intelligence and robotics are showing up in every part of life, anywhere from driving, to the cellphones we use, how our data is managed in the world, how our homes are going to be built in the future. So given its ubiquity, it really is important to start addressing the strengths and limitations of artificial intelligence. Tell me about the technological breakthroughs we have already seen, and what you expect to see in the coming years?

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