Search Results (46)

Background/Objectives: The influence of media on the public opinion, especially regarding health topics, is profound. This study investigates how Spanish media may reinforce a positive image of dietary supplements for children, potentially leading to harmful health attitudes and behaviors. Methods: The researchers conducted a quantitative content analysis of 912 news articles from Spanish media outlets discussing dietary supplements for children between 2015 and 2021. They used a frequency analysis and a proportion comparison to analyze variables such as the reach of news, tone of news, mentions of health professional consultation, association with natural products, media specialization, intertextuality, and headline mentions. Results: The study found a 60% increase in publications discussing dietary supplements for children during the study period. The content analysis indicates that these articles predominantly present dietary supplements in a positive light, often without robust scientific evidence. Furthermore, many do not emphasize the need for medical consultation, which may contribute to unsupervised consumption, particularly among minors. This highlights the critical importance of professional guidance when considering dietary supplements for children. Additionally, the frequent emphasis on the “natural” attributes of these products raises concerns regarding consumer perceptions and potential safety risks. Conclusions: The study reveals a problem regarding the portrayal of dietary supplements for children in Spanish media. The overly optimistic image, lack of scientific basis, and failure to recommend medical supervision may contribute to unsupervised consumption among minors, risking their health due to misinformed decisions influenced by media portrayal. Full article

Sarcasm detection is a crucial task in natural language processing (NLP), particularly in sentiment analysis and opinion mining, where sarcasm can distort sentiment interpretation. Accurately identifying sarcasm remains challenging due to its context-dependent nature and linguistic complexity across informal text sources like social media and conversational dialogues. This study utilizes three benchmark datasets, namely, News Headlines, Mustard, and Reddit (SARC), which contain diverse sarcastic expressions from headlines, scripted dialogues, and online conversations. The proposed methodology leverages transformer-based models (RoBERTa and DistilBERT), integrating context summarization, metadata extraction, and conversational structure preservation to enhance sarcasm detection. The novelty of this research lies in combining contextual summarization with metadata-enhanced embeddings to improve model interpretability and efficiency. Performance evaluation is based on accuracy, F1 score, and the Jaccard coefficient, ensuring a comprehensive assessment. Experimental results demonstrate that RoBERTa achieves 98.5% accuracy with metadata, while DistilBERT offers a 1.74x speedup, highlighting the trade-off between accuracy and computational efficiency for real-world sarcasm detection applications. Full article

This study explores possible applications of AI technology in online journalism, given the predictions that speed and adaptation to the new medium will increase the penetration of automation in the production business. The literature shows that while the human supervision of journalistic workflow is still considered vital, the journalistic workflow is changing in nature, with the writing of micro-content being entrusted to ChatGPT-3.5 among the most visible features. This research assesses readers’ reactions to different headline styles as tested on a sample of 624 students from Timisoara, Romania, asked to evaluate the qualities of a mix of human-written vs. AI-generated headlines. The results show that AI-generated, informative headlines were perceived by more than half of the respondents as the most trustworthy and representative of the media content. Clickbait headlines, regardless of their source, were considered misleading and rated as manipulative (44.7%). In addition, 54.5% of respondents reported a decrease in trust regarding publications that frequently use clickbait techniques. A linguistic analysis was conducted to grasp the qualities of the headlines that triggered the registered responses. This study provides insights into the potential of AI-enabled tools to reshape headline writing practices in digital journalism. Full article

The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study compares the performance in the TLFSA task of various sentiment analysis techniques, including rule-based models (VADER), fine-tuned transformer-based models (DistilFinRoBERTa and Deberta-v3-base-absa-v1.1) as well as zero-shot large language models (ChatGPT and Gemini). The dataset utilized for this analysis, a novel contribution of this research, comprises 1476 manually annotated Bloomberg headlines and is made publicly available (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset) to encourage future research on this subject. The results indicate that the fine-tuned Deberta-v3-base-absa-v1.1 model performs better across all evaluation metrics than other evaluated models in TLFSA. However, LLMs such as ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro provide similar performance levels without the need for task-specific fine-tuning or additional training. The study contributes to assessing the performance of LLMs for financial sentiment analysis, providing useful insights into their possible application in the financial domain. Full article

This study focuses on the case of news headlines in current online journalism, looking into the current possibilities opened by ChatGPT to generate such texts in an attention-grabbing manner. To assess the reaction of online readers to headlines (clickbait or click-worthy), an online survey was applied, involving Romanian students. A total of 100 original human-authored articles with clickbait headlines were extracted from a relevant Romanian database. ChatGPT was used to generate alternative headlines (one clickbait and one informative) based on the original texts. The resulting corpus of 100 headline triplets was offered to students for evaluation. More than 70% of the 600 participants in the survey preferred AI-generated headlines over the human-authored ones, indicating their experiences and behaviors in media consumption. The preferred headlines were further analyzed along lexical and grammatical characteristics, and stylistically, to pinpoint the features sparking readers’ curiosity and engagement. While on a cognitive level the investigated audience rejected clickbait headlines as being deceitful and frustrating, in practice less than 34% favored neutral and objective headlines. Also, the linguistic analysis provided insights into the mechanics of reader engagement and the effectiveness of various headline strategies. The results are useful to anticipate the adoption of AI as a creative partner in Romanian media practice. Full article

Sentiment analysis is a well-known task that has been used to analyse customer feedback reviews and media headlines to detect the sentimental personality or polarisation of a given text. With the growth of social media and other online platforms, like Twitter (now branded as X), Facebook, blogs, and others, it has been used in the investment community to monitor customer feedback, reviews, and news headlines about financial institutions’ products and services to ensure business success and prioritise aspects of customer relationship management. Supervised learning algorithms have been popularly employed for this task, but the performance of these models has been compromised due to the brevity of the content and the presence of idiomatic expressions, sound imitations, and abbreviations. Additionally, the pre-training of a larger language model (PTLM) struggles to capture bidirectional contextual knowledge learnt through word dependency because the sentence-level representation fails to take broad features into account. We develop a novel structure called language feature extraction and adaptation for reviews (LFEAR), an advanced natural language model that amalgamates retrieval-augmented generation (RAG) with a conversation format for an auto-regressive fine-tuning model (ARFT). This helps to overcome the limitations of lexicon-based tools and the reliance on pre-defined sentiment lexicons, which may not fully capture the range of sentiments in natural language and address questions on various topics and tasks. LFEAR is fine-tuned on Hellopeter reviews that incorporate industry-specific contextual information retrieval to show resilience and flexibility for various tasks, including analysing sentiments in reviews of restaurants, movies, politics, and financial products. The proposed model achieved an average precision score of 98.45%, answer correctness of 93.85%, and context precision of 97.69% based on Retrieval-Augmented Generation Assessment (RAGAS) metrics. The LFEAR model is effective in conducting sentiment analysis across various domains due to its adaptability and scalable inference mechanism. It considers unique language characteristics and patterns in specific domains to ensure accurate sentiment annotation. This is particularly beneficial for individuals in the financial sector, such as investors and institutions, including those listed on the Johannesburg Stock Exchange (JSE), which is the primary stock exchange in South Africa and plays a significant role in the country’s financial market. Future initiatives will focus on incorporating a wider range of data sources and improving the system’s ability to express nuanced sentiments effectively, enhancing its usefulness in diverse real-world scenarios. Full article

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. Full article

This study explores the role of populist leaders as gatekeepers on social media, seeking to understand how André Ventura, president of Chega!, uses news to legitimize his political discourse. The methodology involved collecting 90 tweets containing legacy media news features, posted by Ventura on the social media platform X. These tweets cover key political events such as the resignation of Portugal’s Prime Minister, the dissolution of the Portuguese Parliament, and European elections. Quantitative analysis using Voyant Tools identified key terms related to Ventura’s ideological stance, while Critical Discourse Analysis (CDA) examined how these terms support his political narrative. The findings reveal a strategic use of news to promote themes like nationalism, immigration control, corruption and social dichotomy between “us” and “them”. Ventura’s tweets leverage news headlines to enhance his persuasive appeal, acting as heuristic shortcuts to reinforce his political messages. This study highlights the relevance of understanding social media’s role in promoting populism and suggests avenues for future research, including comparative analyses of other populist leaders and the impact of these narratives on voter behavior and perceptions. Full article

The competition to attract audiences has led to an increase in sensational or misleading headlines and content, with the aim of garnering user clicks in the news media. This dynamic alters the journalistic manner in which news is presented, and it does so by reducing informative quality and eroding the trust of the audience. This study examines the proliferation of clickbait strategies on the front pages of reputable international ‘serious’ press and how it manifests in readers’ consumption and sharing habits. We carried out a comparative content analysis of digital news articles from four international media sources (N = 1680): The Guardian (UK), The New York Times (USA), El País (Spain) and Público (Portugal). Our results confirm the existence of clickbait (N = 516) on the front pages, the most read content and the articles most shared on social media. Most clickbait titles resort to headline strategies of containing incomplete information that affect both hard and soft news topics. This particular finding highlights the inclusion of clickbait in the agenda of ‘serious’ journalism, despite the negative implications on information quality and trust. Associated with irrelevant content, this ‘hook’ captures the attention of the online audience more than the social media audience. Full article

Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, and improved computational capabilities, creating robust models capable of accurately predicting stock market movement is now feasible. This study aims to construct a predictive model using news headlines to predict stock market movement direction. It conducts a comparative analysis of five supervised classification machine learning algorithms—logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN)—to predict the next day’s movement direction of the close price of the Nepal Stock Exchange (NEPSE) index. Sentiment scores from news headlines are computed using the Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob sentiment analyzer. The models’ performance is evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results reveal that all five models perform equally well when using sentiment scores from the TextBlob analyzer. Similarly, all models exhibit almost identical performance when using sentiment scores from the VADER analyzer, except for minor variations in AUC in SVM vs. LR and SVM vs. ANN. Moreover, models perform relatively better when using sentiment scores from the TextBlob analyzer compared to the VADER analyzer. These findings are further validated through statistical tests. Full article

Given the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was studied. The simplest model (Naïve Bayes) achieved 85.1% accuracy on a holdout dataset, while the most complex model (Neural Network using BERT) demonstrated 89.3% accuracy. The most useful data preparation steps were identified, and another goal examined the underlying complexity and computational costs of automating the categorization process. It was discovered the BERT model required 170x more time to train, was slower to predict by a factor of 18,600, and required 27x more disk space to save, indicating it may be the best choice for low-volume applications when the highest accuracy is needed. However, for larger-scale operations where a slight performance degradation is tolerated, the Naïve Bayes algorithm could be the best choice. Nearly one in four records in the Toutiao dataset are duplicates, and this is the first published analysis with duplicates removed. Full article

The media’s representation of intimate partner femicides has been contributing to addressing gender-based violence as a structural phenomenon. Aiming to understand which crime elements are valued and how they might contribute to victim blaming, the present study explores the portrayal of intimate partner femicides in Portugal through the analysis of newspaper headlines. The core of the analysis comprises 853 newspaper headlines published between 2000 and 2017, which were subjected to a categorical content analysis. The results suggest two major trends that are aligned with the scope of the two newspapers analyzed. While some headlines offer informative perspectives on crime and its characteristics, the majority tend to sensationalize the narratives, potentially legitimizing violence against women. The results of this study enrich the social and academic debate on the media’s potential influence in preventing and combating gender-based violence. Moreover, by shedding light on the media’s representation of intimate partner femicides, the study reinforces the importance of a broader discussion on the role of journalism in fostering social change. Full article

Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns. Full article

Social media platforms are valuable data sources in the study of public reactions to events such as natural disasters and epidemics. This research assesses for selected countries around the globe the time lag between daily reports of COVID-19 cases and GDELT (Global Database of Events, Language, and Tone) and Twitter (X) COVID-19 mentions between February 2020 and April 2021 using time series analysis. Results show that GDELT articles and tweets preceded COVID-19 infections in Australia, Brazil, France, Greece, India, Italy, the U.S., Canada, Germany, and the U.K., while for Poland and the Philippines, tweets preceded and GDELT articles lagged behind COVID-19 disease incidences, respectively. This shows that the application of social media and news data for surveillance and management of pandemics needs to be assessed on a case-by-case basis for different countries. It also points towards the applicability of time series data analysis for only a limited number of countries due to strict data requirements (e.g., stationarity). A deviation from generally observed lag patterns in a country, i.e., periods with low COVID-19 infections but unusually high numbers of COVID-19-related GDELT articles or tweets, signals an anomaly. We use the seasonal hybrid extreme Studentized deviate test to detect such anomalies. This is followed by text analysis of news headlines from NewsBank and Google on the date of these anomalies to determine the probable event causing an anomaly, which includes elections, holidays, and protests. Full article

This research seeks to find out to what extent the Spanish press reflects the role played by the Red Cross during the first 100 days of the war in Ukraine. It aims to identify the main characteristics of the information in which the organization has taken a leading role in the press. The theoretical framework includes a literature review on the strategic relationship between the press and non-governmental organizations (NGOs) for the benefit of their reputation, as well as the role of the Red Cross in armed conflicts, and the link between the third sector and geopolitics. In order to examine how different media treated the Red Cross as the protagonist of the news, articles published in the written press that included “Red Cross” as keywords in the headline were selected through the Onclusive platform (formerly Kantar Media). The period of analysis covered the first 100 days of war in Ukraine, from 24 February to 3 June 2022. The methodology used was developed in two phases: the first based on content analysis, and the second focused on the description and interpretation of the informative development of the sample. The results reveal that the role of the Red Cross in the conflict is not the focus of media attention and that its name has become a lure for political communication, for social events or for the publicity of the more traditional corporate social responsibility (CSR) of companies. We can say that the relationship between the Red Cross and the written press has not contributed to explaining or clearly expressing the institution’s task or mission in a war. This is why it can be deduced that the articles analyzed do not improve the brand value and its positioning among readers. Full article