News Events Research Articles

An Alien in the Newsroom: AI Anxiety in European and American Newspapers

The media portrayal of artificial intelligence (AI) directly impacts how audiences conceptualize this technology and, therefore, its use, development, and regulation. This study aims to measure a key aspect of this problem: the feeling of AI anxiety conveyed by news outlets that represent this technology as a sort of “alien” that is autonomous, opaque, and independent of humans. To do so, we build an AI anxiety index based on principal component analysis (PCA) and apply it to a corpus of headlines (n = 1682) about AI published before and after the launch of ChatGPT in ten newspapers: The New York Times, The Guardian, El País, Le Monde, Frankfurter Allgemeine Zeitung, San Francisco Chronicle, Manchester Evening News, La Voz de Galicia, Ouest France, and Münchner Merkur. The results show that ChatGPT not only boosted the number of AI headlines × 5.16) but also reduced positive sentiments (−26.46%) and increased negatives (58.84%). The AI anxiety index also grew (10.59%), albeit driven by regional media (61.41%), while it fell in national media (−6.82%). Finally, the discussion of the variables that compose the index reveals the opportunities and challenges faced by national and regional media in avoiding the feeling of AI anxiety.

News dissemination: a semantic approach to barrier classification

AbstractThe dissemination of information worldwide is significantly facilitated by the news media, with many events having global relevance across various regions. However, certain news events receive limited coverage restricted to specific geographic areas, due to the barriers that hinder the spread of information. These barriers can be attributed to political, geographical, economic, cultural, or linguistic factors. In this research, we propose an approach for classifying these barriers by extracting semantic information from news articles using Wikipedia-concepts. Our methodology involves the collection of news articles, each annotated to indicate the specific barrier types, leveraging metadata from news publishers. Subsequently, we employ Wikipedia-concepts, in conjunction with the content of the news articles, as features to determine the barriers to news dissemination. Our approach is then compared with traditional text classification techniques, deep learning methods, and transformer-based models. We have performed experiments on news articles from ten categories of topics including health, sports, business, etc. The findings indicate that 1) Utilizing semantic knowledge yields distinct concepts across the ten categories, thereby enhancing the effectiveness and speed of the classification model. 2) The proposed approach, incorporating Wikipedia-concepts-based semantic knowledge, leads to improved performance in barrier classification when compared to using solely the body text of news articles. Specifically, there is an increase in the average F1-scores for four out of five barriers, with the economic barrier rising from 0.65 to 0.68, the linguistic barrier from 0.71 to 0.72, the political barrier from 0.68 to 0.70, and the geographical barrier from 0.63 to 0.68.

LA PROPAGANDE HOUPHOUETISTE AUTOUR DE LIMAGE DE GBAGBO LAURENT DANS LES QUOTIDIENS DINFORMATIONS GENERALES IVOIRIENS

This article analyses the Gbagbo Laurent image construction on houphouetism. To achieve this objective, the article is based on the results of content analysis between 2000 and 2023 from five (5) national daily newspapers. They are: the time, evening news, our way, fraternity morning and inter. The data were processed by thematic content analysis, discourse analysis, the comprehensive method of Max Weber and the field theory of BOURDIEU. The results of this analysis show that the houphouetist propaganda on Gbgagbo Laurent has three fundamental aspects. These are: fidelity to France, an outside economy and West African people migrants integration. Then, values extended by daily newspapers toward Gbagbo Laurent: aheros, a pan-africanist and a democrat. The end, this analysis show two caracteristic of this propaganda. These are: a resistant figtherto colonialism and continuing founder work.

Discretionary dissemination on Twitter

AbstractThe study provides large‐scale descriptive evidence on the timing and nature of corporate financial tweeting. Using an unsupervised machine learning approach to analyze 24 million tweets posted by S&P 1500 firms from 2012 to 2020, we find that firms are more likely to tweet financial information around significantly negative or positive news events, such as earnings announcements and the filing of financial statements. This convex U‐shaped relation between the likelihood of posting financial tweets and the materiality of accounting events becomes stronger over time. Whereas research based on early samples concludes that firms are less likely to disseminate financial information on Twitter when the news is bad and material, the symmetric dissemination behavior we find suggests that these conclusions should be revised. We also show that a machine learning algorithm (Twitter‐Latent Dirichlet Allocation) is superior to a dictionary approach in classifying short messages like tweets.

The Role of Photography in Breaking News Events: The Trump Assassination as an Example

This paper explores the role of photography in breaking news events through photographs taken by Evan Vucci during the Trump assassination. Using content analysis, the study analyzed 105 public articles to explore the visual guiding power of photography and how the media uses images to influence public opinion. The study found that photography can quickly attract public attention and influence the emotional level, while media can shape a specific narrative framework through selective display. The life of a news report lies in vividness, truth, and vividness. The same is true of news photography. Capturing real moments with both visual and emotional impact is the best news photo [1]. Meanwhile, it points out the shortcomings of China’s WeChat in reporting, such as ignoring the victims and the deep-rooted causes, and suggests that the media should reflect on the problems in reporting and take social responsibility. The study also offers suggestions for future reporting practices, emphasizing the key role of photojournalism in shaping public perception and the importance of media ethics. The limitation of the study is that the sample mainly focuses on WeChat, with a short timeframe, and future research will expand the sample and deepen the analysis of the audience’s psychological and emotional responses.

Application and Empirical Analysis of Random Volatility Model in Financial Markets

The Stochastic Volatility Model (SVM), as a key innovation in modern financial engineering, has profoundly changed our understanding of the volatility characteristics of financial markets. This model not only considers the volatility of asset prices as a time-varying random variable, but also simulates this uncertainty by introducing stochastic processes such as Brownian motion or more complex stochastic differential equations, thereby achieving a precise characterization of the dynamic characteristics of volatility. Compared to traditional fixed or historical average volatility assumptions, SVM can capture sudden changes in market sentiment, the impact of news events, and the influence of macroeconomic factors on volatility, providing investors with a more realistic perspective on the market. This article not only deeply analyzes the theoretical basis and construction methods of SVM, but also verifies its effectiveness and applicability in different financial market environments through empirical analysis and actual market data, providing valuable references and insights for financial practitioners, scholars, and policy makers.

News-Driven Volatility: A Deep Dive into Leading Nifty Healthcare Stocks

This study conducts a comprehensive analysis of stock price volatility within the healthcare sector, concentrating on five prominent companies: Sun Pharma, Dr. Reddy, Cipla, Apollo Hospitals, and Divis Laboratories. Leveraging annual daily data from the Nifty Healthcare Index for 2023, the research delves into the intricate relationship between stock price fluctuations and market-sensitive news events. Employing a robust methodology that integrates slope calculations, ANOVA, Tukey’s HSD tests, moving averages, and standard deviation metrics, the study evaluates the differential impact of these events on stock volatility. The findings reveal significant volatility, with Apollo Hospitals demonstrating the highest levels of price variability. The research highlights the profound influence of regulatory shifts, financial performance metrics, and macroeconomic conditions on stock behavior. These insights are pivotal for investors and financial analysts seeking to navigate the complex dynamics of the healthcare market. The application of moving averages further refines the analysis, offering a sophisticated lens through which to assess and interpret market trends, ultimately enhancing the strategic decision-making process within this critical sector.

The Construction of a Hybrid Media Storm: A Belgian Case Study

ABSTRACT In today's hybrid media system, amplified news events rage not only in “older” media such as legacy media but also on “newer” digital platforms such as online alternative news media outlets and social media. However, the role of both legacy news media and digital platforms in constructing events has been little explored. This study examines a hybrid media storm surrounding a Belgian political scandal in 2021. Based on a quantitative content analysis of 1,436 print and online articles from six Flemish newspapers and the public broadcaster’s website, 204 articles from three right-leaning alternative media, and 801 tweets between 1 February and 31 October 2021, the study reveals that digital platforms allow actors with little or no voice in legacy news media to influence the narrative of events on those platforms by actively promoting specific frames. Right-leaning alternative media's coverage, although less pluralistic than legacy media, still offers a broader frame repertoire than Twitter where the framing is one-sided. The case study shows that right-leaning alternative media and Twitter do not necessarily broaden the range of views, but are often a crucial factor in the amplification of specific frames at the expense of others.

Transmediatic depth of «The New York Times» and «El País» in their coverage of Boris Johnson’s resignation

A decade after the implementation of multimedia frameworks in the news media, it is necessary to determine which mechanisms have been incorporated into the daily routines of the news media. To accomplish this, we analysed the channels, forms and intertextual modalities employed by two leading international newspapers in their coverage of an unpredictable news event with global repercussions and a specific period: the UK government crisis of July 2022, which led to the resignation of the British Prime Minister Boris Johnson. We used a qualitative methodology of transmedia information analysis (Calvo & Serrano Tellería, 2021), adapted to assess the daily operations of online media in updating their content. Additionally, interviews were conducted with the teams responsible for this coverage. The coverage analysis revealed a transmedia depth of ten to eleven informative modalities. We found several residual differences in their audience participation policies. Certain generalist modalities, such as live streaming, video news, podcasts or social networks, were well established and used to attract readership. Furthermore, various models of newsletters and discussion forums with varying levels of access restriction were employed. However, neither of the studied media outlets offered an auditory transcription of their content. While a specific transmedia information model is emerging in digital media, further research should be undertaken to complement these findings.

Debates over the role of Traditional Chinese Medicine on COVID-19: A computational comparison between professionals and laypersons in Chinese online knowledge community

Leveraging a large collection of textual data (N = 21,539) from a Chinese online community, we employed structural topic modeling to investigate the thematic disparities between professionals and laypersons, regarding the effectiveness of Traditional Chinese Medicine (TCM) on COVID-19. Findings reveal that laypersons are the dominant communicators in terms of discussion volume, who often focus on relevant news events, societal or political aspects of TCM. In contrast, professionals keep concentrating on issues related to medical expertise, and do not shift attentions as frequent as laypersons. Despite the dominant influence of professionals on laypersons’ agenda, two-way agenda interactions identified confirm that lay public is empowered to negotiate with elite professionals under certain topics. Our results provide novel insights into the dynamic nature of attentions, behaviors, and relations among prominent communication actors, and encourage future research to examine the individual-level and societal-level impacts of these constructs in the emerging online media landscape.

A survey on temporal knowledge graph embedding: Models and applications

Knowledge graph embedding (KGE), as a pivotal technology in artificial intelligence, plays a significant role in enhancing the logical reasoning and management efficiency of downstream tasks in knowledge graphs (KGs). It maps the intricate structure of a KG to a continuous vector space. Conventional KGE techniques primarily focus on representing static data within a KG. However, in the real world, facts frequently change over time, as exemplified by evolving social relationships and news events. The effective utilization of embedding technologies to represent KGs that integrate temporal data has gained significant scholarly interest. This paper comprehensively reviews the existing methods for learning KG representations that incorporate temporal data. It offers a highly intuitive perspective by categorizing temporal KGE (TKGE) methods into seven main classes based on dynamic evolution models and extensions of static KGE. The review covers various aspects of TKGE, including the background, problem definition, symbolic representation, training process, commonly used datasets, evaluation schemes, and relevant research. Furthermore, detailed descriptions of related embedding models are provided, followed by an introduction to typical downstream tasks in temporal KG scenarios. Finally, the paper concludes by summarizing the challenges faced in TKGE and outlining future research directions.

Real-Time Extraction of News Events Based on BERT Model

Abstract For the large number of news reports generated every day, in order to obtain effective information from these unstructured news text data more efficiently. In this paper, we study the real-time crawling of news data from news websites through crawling techniques and propose a BERT model-based approach to extract events from news long text. In this study, NetEase news website is selected as an example for real-time extraction to crawl the news data of this website. BERT model as a pre-trained model based on two-way encoded representation of transformer performs well on natural language understanding and natural language generation tasks. In this study, we will fine-tune the training based on BERT model on news corpus related dataset and perform sequence annotation through CRF layer to finally complete the event extraction task. In this paper, the DUEE dataset is chosen to train the model, and the experiments show that the overall performance of the BERT model is better than other network models. Finally, the model of this paper is further optimised, using the ALBERT and RoBERTa models improved on the basis of the BERT model, experiments were conducted, the results show that both models are improved compared to the BERT model, the ALBERT model algorithm performs the best, the model algorithm's F1 value is 1% higher than that of BERT. The results show that the performance is optimised.

Geo-spatial hypertext in virtual reality: mapping and navigating global news event spaces

ABSTRACT Every day, a myriad of events take place that are documented and shared online through news articles from a variety of sources. As a result, as users navigate the Web, the volume of data can lead to information overload, making it difficult to find specific details about an event. We present News in Time and Space (NiTS) to address this issue: NiTS is a fully immersive system integrated into Va.Si.Li-Lab that organises textual information in a geospatial hypertext system in virtual reality. With NiTS, users can visualise, filter and interact with information currently based on GDELT on a virtual globe providing document networks to analyse global events and trends. The article describes NiTS, its event semantics and architecture. It evaluates NiTS in comparison to a classic search engine website, extended by NiTS's information filtering capabilities to make it comparable. Our comparison with this website technology, which is directly linked to the user's usage habits, shows that NiTS enables comparable information exploration even if the users have little or no experience with VR. That is, we observe an equivalent search result behaviour, but with the advantage that VR allows users to get their results with a higher level of usability without distracting them from their tasks. Through its integration with Va.Si.Li-Lab, a simulation-based learning environment, NiTS can be used in simulations of learning processes aimed at studying critical online reasoning, where Va.Si.Li-Lab guarantees that this can be done in relation to individual or groups of learners.

LEST: Large language models and spatio-temporal data analysis for enhanced Sino-US exchange rate forecasting

Exchange rate forecasting has a significant impact on a country’s balance of international payments, financial security and stability. Under the context of Sino-US trade war, a series of news events have happened thus increasing the abnormal fluctuations in the RMB exchange rate, and these fluctuations have made it more difficult to forecast the exchange rate. The analysis of news events’ impact on exchange rate fluctuations focused on assigning emotional labels to different economic news and political events by building an emotional lexicon. However, the previous method of using an offline emotional lexicon to analyze events could not be updated promptly making it difficult to better exploit implicit information of news events. Also, the offline emotional lexicon requires manual annotation of events which is unavoidable and subjective and therefore influences the sentiment analysis of the event. Therefore, we build a fusion forecasting framework of spatio-temporal and emotional information for accurate, real-time exchange rate forecasting. First, in the preprocessing phase, we leveraged the situational learning capabilities of Large Language Models (LLMs), specifically ChatGPT, combined with prompt engineering to enable ChatGPT to provide real-time, online, and precise automated sentiment analyses of Sino-US trade events. Second, we proposed the SG-TL fusion model, which integrates Spatio-Temporal Graph Convolutional Networks (STGCN) and Gated Recurrent Unit (GRU) models in the feature processing approach to accurately extract exchange rate features in spatial and temporal dimensions. We also employed Transformer and Long Short-Term Memory (LSTM) models in the forecasting phase to forecast the Sino-US exchange rates. Finally, Experiment results demonstrate that the exchange rate forecasting framework LEST can accurately capture the exchange rate fluctuations during the period of Sino-US trade war. Furthermore, its forecasting performance is more precise, efficient, and stable than existing works. The source code of our developed method is publicly available at https://gitee.com/andyham_andy.ham/lest-forecasting-framework.

Exploring the Correlation between Emojis and Mood Expression in Thai Twitter Discourse

Mood, a long-lasting affective state detached from specific stimuli, plays an important role in behavior. Although sentiment analysis and emotion classification have garnered attention, research on mood classification remains in its early stages. This study adopts a two-dimensional structure of affect, comprising ”pleasantness” and ”activation,” to classify mood patterns. Emojis, graphic symbols representing emotions and concepts, are widely used in computer-mediated communication. Unlike previous studies that consider emojis as direct labels for emotion or sentiment, this work uses a pre-trained large language model which integrates both text and emojis to develop a mood classification model. Our contributions are three-fold. First, we annotate 10,000 Thai tweets with mood to train the models and release the dataset to the public. Second, we show that emojis contribute to determining mood to a lesser extent than text, far from mapping directly to mood. Third, through the application of the trained model, we observe the correlation of moods during the Thai political turmoil of 2019-2020 on Thai Twitter and find a significant correlation. These moods closely reflect the news events and reveal one side of Thai public opinion during the turmoil.

“What the Heck Is Going on in the Group!”: Challenging Journalistic Authority by Pseudo-Professional WhatsApp News Groups

ABSTRACT This paper addresses the ways new entrants and mobile technologies have become significant challenges to the old news’ legitimacy models by focusing on pseudo-professional WhatsApp news groups from a journalistic authority standpoint. Based on a comparative textual analysis of how a breaking news event was handled by a leading Israeli pseudo-professional WhatsApp news group, compared to how it was covered by the Israeli News13 TV news broadcast, the findings indicate how, by adopting professional news practices and establishing unique relations of information exchange with traditional news outlets, journalistic authority is challenged by pseudo-professional WhatsApp news groups in today’s mobile news environment.

Analyzing the impact of financial news on the stock market using Natural Language Processing techniques

In the current financial domain, the impact of financial news on the stock market has become increasingly significant. Its rapid dissemination speed and wide coverage have made it a key factor in influencing investor sentiment and decision-making. Therefore, this study aims to explore how to utilize Natural Language Processing (NLP) technologies to analyze the impact of financial news on the stock market. The research involves collecting multiple sources of financial news and corresponding stock market data, and employs methods such as text preprocessing, sentiment analysis, and event extraction to process and analyze the news content. By constructing machine learning models, this study attempts to quantify the sentiment orientation of financial news and the impact of specific events on stock market volatility. The research questions focus on how to effectively extract valuable information from financial news and predict its specific impact on the stock market. The results indicate that sentiment orientation and key events in financial news are significantly correlated with short-term fluctuations in the stock market, especially for news reports on specific industries or companies. Moreover, the application of deep learning models further enhances the accuracy of predicting stock market reactions. This study not only provides financial market analysts with a new analytical tool but also offers a new perspective on understanding how financial news affects the stock market.

Research on privacy leakage of celebrity's ID card number based on real‐name authentication

AbstractThe Internet real‐name system is widely implemented among Chinese Internet users, and many commonly used apps in China exist the functions of real‐name authentication. However, our study found that many apps do not have effective restrictions on user's operations of real‐name authentication, resulting in users being able to frequently perform unsuccessful real‐name authentication attempts. This vulnerability can help an attacker crack celebrity's ID card number by enumeration attacks, and a feasible cracking method was proposed in this paper. First, the information of birth date, birth place, and life experiences of a celebrity is collected from the platforms that display celebrities' personal information (e.g., Wikipedia, Baidu Baike, etc.). In this process, an information extraction method is used to infer permanent residences from life experiences. Then, the possible ID card numbers of a celebrity can be constructed by using the information of birth date, birth place, and permanent residences. Finally, these possible ID card numbers will be verified by sending requests to platforms that have vulnerabilities in the function of user real‐name authentication, until the real ID card number of a celebrity being cracked. This paper conducted cracking experiments on two groups of celebrities. The first group of celebrities is collected from the news events of privacy leakage that were publicly available online, and the second group of celebrities is randomly selected from two encyclopedia platforms. The experimental results showed that the success rate of cracking the ID card numbers of celebrities is 53.9%, which verified the effectiveness of the proposed cracking method. Besides, this paper proposed some security precaution suggestions to solve this security problem, and the implementation, feasibility, potential impact, expected effectiveness of these measures were also analyzed. To our knowledge, our paper is the first to point out the issue of privacy leakage of celebrity's ID card number caused by apps' real‐name authentication functions in China. We believe that our research will attract widespread attention from society regarding celebrity's privacy information protection.

Retail investors and ESG news

An important debate exists around the extent to which retail investors make sustainable investments and, if they do, why. We contribute to this debate by investigating the aggregate trading patterns of retail investors around a comprehensive sample of key environmental, social, and governance (ESG) news events for U.S. firms. We show that ESG news events appear to be an important factor in retail investors’ portfolio allocation decisions. Yet, inconsistent with arguments about retail investors’ nonpecuniary preferences, our evidence shows that retail investors mainly trade on this information when they deem it financially material to a company’s stock performance. We also find their net trading demand predicts abnormal returns in the subsample of financially material events, consistent with retail traders benefiting from incorporating ESG-related information into their decision-making when it influences firm value. Overall we conclude that the average U.S. retail investor cares about firms’ ESG activities but primarily to the extent these activities matter for company financial performance.

Research on the Image of Yanqing in Mainstream Media Coverage of the Winter Olympics

The successful hosting of large-scale sports events can assist in the construction and dissemination of urban media image, and enhance urban competitiveness. This article mainly takes news reports on Yanqing from three mainstream media outlets, namely People's Daily, Beijing Daily, and Beijing Evening News, during the Winter Olympics as research samples. Using methods such as content analysis, the article analyzes Yanqing's urban media image from multiple perspectives by analyzing the emotional color, coverage density, themes, and keyword frequency of news reports. It presents a good media image of Yanqing built at different levels such as government, environment, culture, and society during the Winter Olympics.