When assisted reproductive technology (ART) opens the door for inclusivity in a highly charged cultural milieu: an exploration of news articles on the establishment of ART clinics in Zimbabwe.

The study focuses on how infertility and assisted reproductive technology (ART) have been portrayed in the Zimbabwean print news media, specifically looking at articles related to the country's two private fertility clinics established in 2016 and 2017 respectively. Through thematic analysis of 35 news articles, seven prominent themes were developed: infertility as an undesirable and stigmatised condition; stress and the feminisation of infertility; the impact of societal and familial pressure to have children; ART as a ray of hope for infertile couples; growing acceptance of ART; availability, accessibility and affordability of ART; and the use of alternative medicines to cure infertility. The research highlights the coexistence of traditional medicine and ART in Zimbabwe, as well as the impact of stigma, pressure, and gender dynamics on infertile couples. Study findings signal how costly ART treatments may drive individuals towards potentially harmful traditional remedies. They also underscore the need for increased awareness of infertility, efforts to reduce stigma, and addressing barriers to ART access, particularly for men. Overall, findings shed light on the complexities surrounding infertility in Zimbabwe and the importance of addressing these issues in pursuit of better reproductive healthcare outcomes.

Shifting perspectives: From "Epilepsy" to "Cerebroelectric Disorder".

BACKGROUND & OBJECTIVE: Epilepsy has long been associated with stigma and misconceptions. In response, the Korean Epilepsy Society initiated the Epilepsy Renaming project in 2008 to replace the stigmatizing term with a neutral and scientifically grounded name, "cerebroelectric disorder". This study explores the impact of changing terminology on the public discourse surrounding epilepsy. METHODS: Online news articles from distinct time periods (2001-2003, 2011-2014, 2017-2018, and 2020-2022) were analyzed using text data analysis techniques, including Latent Dirichlet Allocation topic modeling, frequency analysis, and sentiment analysis. The inclusion of data from 2017 to 2018 allowed for an examination of discourse trends independent of the COVID-19 pandemic's influence. Correlation of words in each period was visualized via network maps. Migraine was set as control term to highlight changes in perception devoid of significant stigma intervention efforts. RESULTS: The analysis revealed a significant shift in terminology preference, with cerebroelectric disorder gradually replacing epilepsy in news articles. The discourse surrounding epilepsy evolved over time from focusing on healthcare and economic aspects to patient-centered discussions, emphasizing the daily lives of individuals with epilepsy. This shift towards more empathetic and less stigmatized language was contrasted against the discourse on migraine, highlighting the specific impact of the terminological change on epilepsy's perception. CONCLUSION: The adoption of the neutral term "cerebroelectric disorder" in South Korea has influenced the discourse surrounding epilepsy, leading to more patient-centered discussions and a reduction in stigma. This study highlights the importance of terminology in shaping public perceptions of diseases and suggests that changing terminology can positively impact the understanding and destigmatization of epilepsy.

Dataset of controversial news posts in Spanish from the reader's perspective.

This paper presents a corpus of Spanish news posts obtained from X with the annotation of controversy made via crowdsourcing. A total of 60 tweets were obtained from 8 different newspapers. For the annotation task, a survey was developed and sent to 31 different participants to answer it with the controversy level they perceived from the news post summary and headline presented on the post. The most frequent selected option was assigned as the initial controversy level of the post. The final annotation of the corpus was made via an analysis of the raw data by computing the Inter Annotator Agreement (IAA). The analysis showed that the binarization of the data was the most convenient way to annotate it. A potential use for this dataset is detailed in further sections.

Young graduates and economic recession: Lessons from the pandemic to prevent the (re)incidence of mental health symptoms.

Economic conditions affect the youth labour market and can leave deep scars. This exploratory study examines the emotional responses and mental health symptoms of young graduates during their transition into the labour market in the pandemic context. It draws on 42 news articles with statements from 86 graduates from a set of European and non-European countries. The graduates had jobs or internships cancelled, numerous applications unanswered or were dismissed from jobs they had recently started. Young people adopt a variety of coping strategies, which are often invisible and cause deep suffering due to anxiety, disappointment, fear, and depression. Their apprehension and uncertainty leave them in a state of limbo. The specific impacts of the pandemic on young people's lives serve as a warning of the need to protect future generations of graduates. More support is required worldwide to manage the mental health issues that affect young graduates, especially during economic recessions.

ANAD: Arabic news article dataset.

In this paper, we present a modern standard Arabic dataset based on Arabic news articles collected over a one-year period from 01/01/2021 to 12/31/2021. In total, from 12 Arabic news websites, over 500,000 articles were collected, the selection of which was driven by a variety of topics, including sports, economies, local news, politics, tech, tourism, entertainment, cars, health, and art. The development of this dataset will enable data scientists to explore and experiment effectively in the field of natural language processing, and the dataset can also be used to develop machine learning and deep learning models to classify articles according to topic. The dataset is available for download at https://github.com/alaybaa/ArabicArticlesDataset/tree/main.

You Are Not Welcome! A Media Analysis of Risk Factors, Prevalence and Management of Free-Roaming Dogs in Iran.

History has witnessed a long-term relationship between humans and animals. Historical documents and modern findings prove that humans' needs to use animals for companions or services are commonplace in many parts of the world, leading to the domestication of certain animals. Yet, modern societies have degraded many natural habitats for wildlife, confining them to small patches of landscapes or urban areas. Whether a domesticated/free-roaming animal or a wild species, their close contact with humans can create cumbersome situations for both species. This paper explores a link between online media content and on-the-ground efforts to manage free-roaming dogs as a rare case study. As indicated by news articles, the municipal costs of managing free-roaming dogs in Iranian cities have increased, and this can potentially derail the control of such dogs in the long run. This paper lays out pivotal factors for recent increasing human-animal encounters, which have led to many challenges (e.g., rabies) across cities in Iran. We show that some urban features (e.g., topography) can influence the presence and behaviours of free-roaming animals in the cities. The findings of this paper can be related to other developing countries where the plague of rabies is rising.

How to detect propaganda from social media? Exploitation of semantic and fine-tuned language models.

Online propaganda is a mechanism to influence the opinions of social media users. It is a growing menace to public health, democratic institutions, and public society. The present study proposes a propaganda detection framework as a binary classification model based on a news repository. Several feature models are explored to develop a robust model such as part-of-speech, LIWC, word uni-gram, Embeddings from Language Models (ELMo), FastText, word2vec, latent semantic analysis (LSA), and char tri-gram feature models. Moreover, fine-tuning of the BERT is also performed. Three oversampling methods are investigated to handle the imbalance status of the Qprop dataset. SMOTE Edited Nearest Neighbors (ENN) presented the best results. The fine-tuning of BERT revealed that the BERT-320 sequence length is the best model. As a standalone model, the char tri-gram presented superior performance as compared to other features. The robust performance is observed against the combination of char tri-gram + BERT and char tri-gram + word2vec and they outperformed the two state-of-the-art baselines. In contrast to prior approaches, the addition of feature selection further improves the performance and achieved more than 97.60% recall, f1-score, and AUC on the dev and test part of the dataset. The findings of the present study can be used to organize news articles for various public news websites.

Adherence to COVID-19 health protocols in an online news context in the Philippines: A manifest content analysis.

OBJECTIVES: Globally, adherence to COVID-19 health and safety protocols played a crucial role in preventing the spread of the virus. Thus, this study analyzed online news articles reporting adherence to COVID-19 health and safety protocols in the Philippines. DESIGN: Manifest content analysis. SAMPLE: News articles (n = 192) from three major online news portals in the Philippines. MEASUREMENT: Published online news articles were collected during the peak of the COVID-19 pandemic (March 2020 to March 2021). Bengtsson's content analysis approach was used to analyze the data. Member-checking and intercoder reliability validated the study's results. RESULTS: Three main themes emerged: (a) adherence, (b) non-adherence, and (c) partial adherence. The subthemes were labeled who, what, when, where, and why. The same behavior, social distancing, was the most adhered to and non-adhered COVID-19 health protocol. This protocol has the highest occurrences in political protest, religious-related activities, and self-initiated quarantine. Leisure activities both showed non-adherence and partial adherence. CONCLUSIONS: Online news articles depicted Filipinos' adherence to health and safety protocols. Their adherence was primarily determined by one's group or community, social norms, and values. The government and its public health agencies should strengthen current efforts and continuously re-evaluate existing policies to modify ineffective and confusing safety health protocols.

Combining sentiment analysis classifiers to explore multilingual news articles covering London 2012 and Rio 2016 Olympics.

This study aims to present an approach for the challenges of working with Sentiment Analysis (SA) applied to news articles in a multilingual corpus. It looks at the use and combination of multiple algorithms to explore news articles published in English and Portuguese. It presents a methodology that starts by evaluating and combining four SA algorithms (SenticNet, SentiStrength, Vader and BERT, being BERT trained in two datasets) to improve the quality of outputs. A thorough review of the algorithms' limitations is conducted using SHAP, an explainable AI tool, resulting in a list of issues that researchers must consider before using SA to interpret texts. We propose a combination of the three best classifiers (Vader, Amazon BERT and Sent140 BERT) to identify contradictory results, improving the quality of the positive, neutral and negative labels assigned to the texts. Challenges with translation are addressed, indicating possible solutions for non-English corpora. As a case study, the method is applied to the study of the media coverage of London 2012 and Rio 2016 Olympic legacies. The combination of different classifiers has proved to be efficient, revealing the unbalance between the media coverage of London 2012, much more positive, and Rio 2016, more negative.

Superlatives in news articles reporting non-FDA approved indications for use of cannabis and cannabis products with a focus on psychiatric disorders: a cross-sectional analysis.

The exaggerated language used in news articles to describe the benefits of cannabis for conditions without FDA indications may mislead the public and healthcare providers. Thus, this study's objective was to investigate the use of exaggerated language in news articles focused on cannabis and cannabis-derived products. Using a cross-sectional study design, we searched Google News from March 3, 2020, and September 3, 2019 for 11 prespecified superlative terms along with the search terms "cannabis," "cannabidiol," "pot," "marijuana," "weed," and "CBD." Articles were evaluated for these exaggerative terms describing cannabis and cannabis-derived products along with additional news article characteristics. Screening and data extraction occurred in a masked, duplicate fashion. We identified 612 superlative terms in 374 different news articles focused on cannabis and cannabis-derived products from 262 news outlets. Only 26 (of 374, 7.0%) news articles provided clinical data. In total, superlative terms were used to describe cannabis and cannabis-derived products for the treatment of 91 medical conditions, of which only 2 are FDA approved. The most common psychiatric disorder indicated was anxiety disorder appearing in 88 news articles. Superlatives in news articles covering the treatment of psychiatric illnesses with cannabis and cannabis-derived products are common.

CLICK-ID: A novel dataset for Indonesian clickbait headlines.

News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas.

Media Reports as a Source for Monitoring Impact of Influenza on Hospital Care: Qualitative Content Analysis.

BACKGROUND: The Netherlands, like most European countries, has a robust influenza surveillance system in primary care. However, there is a lack of real-time nationally representative data on hospital admissions for complications of influenza. Anecdotal information about hospital capacity problems during influenza epidemics can, therefore, not be substantiated. OBJECTIVE: The aim of this study was to assess whether media reports could provide relevant information for estimating the impact of influenza on hospital capacity, in the absence of hospital surveillance data. METHODS: Dutch news articles on influenza in hospitals during the influenza season (week 40 of 2017 until week 20 of 2018) were searched in a Web-based media monitoring program (Coosto). Trends in the number of weekly articles were compared with trends in 5 different influenza surveillance systems. A content analysis was performed on a selection of news articles, and information on the hospital, department, problem, and preventive or response measures was collected. RESULTS: The trend in weekly news articles correlated significantly with the trends in all 5 surveillance systems, including severe acute respiratory infections (SARI) surveillance. However, the peak in all 5 surveillance systems preceded the peak in news articles. Content analysis showed hospitals (N=69) had major capacity problems (46/69, 67%), resulting in admission stops (9/46, 20%), postponement of nonurgent surgical procedures (29/46, 63%), or both (8/46, 17%). Only few hospitals reported the use of point-of-care testing (5/69, 7%) or a separate influenza ward (3/69, 4%) to accelerate clinical management, but most resorted to ad hoc crisis management (34/69, 49%). CONCLUSIONS: Media reports showed that the 2017/2018 influenza epidemic caused serious problems in hospitals throughout the country. However, because of the time lag in media reporting, it is not a suitable alternative for near real-time SARI surveillance. A robust SARI surveillance program is important to inform decision making.

SANAD: Single-label Arabic News Articles Dataset for automatic text categorization.

Text Classification is one of the most popular Natural Language Processing (NLP) tasks. Text classification (aka categorization) is an active research topic in recent years. However, much less attention was directed towards this task in Arabic, due to the lack of rich representative resources for training an Arabic text classifier. Therefore, we introduce a large Single-labeled Arabic News Articles Dataset (SANAD) of textual data collected from three news portals. The dataset is a large one consisting of almost 200k articles distributed into seven categories that we offer to the research community on Arabic computational linguistics. We anticipate that this rich dataset would make a great aid for a variety of NLP tasks on Modern Standard Arabic (MSA) textual data, especially for single label text classification purposes. We present the data in raw form. SANAD is composed of three main datasets scraped from three news portals, which are AlKhaleej, AlArabiya, and Akhbarona. SANAD is made public and freely available at https://data.mendeley.com/datasets/57zpx667y9.

Expert quotes and exaggeration in health news: a retrospective quantitative content analysis.

Background This research is an investigation into the role of expert quotes in health news, specifically whether news articles containing a quote from an independent expert are less often exaggerated than articles without such a quote. Methods Retrospective quantitative content analysis of journal articles, press releases, and associated news articles was performed. The investigated sample are press releases on peer-reviewed health research and the associated research articles and news stories. Our sample consisted of 462 press releases and 668 news articles from the UK (2011) and 129 press releases and 185 news articles from The Netherlands (2015). We hand-coded all journal articles, press releases and news articles for correlational claims, using a well-tested codebook. The main outcome measures are types of sources that were quoted and exaggeration of correlational claims. We used counts, 2x2 tables and odds ratios to assess the relationship between presence of quotes and exaggeration of the causal claim. Results Overall, 99.1% of the UK press releases and 84.5% of the Dutch press releases contain at least one quote. For the associated news articles these percentages are: 88.6% in the UK and 69.7% in the Netherlands. Authors of the study are most often quoted and only 7.5% of UK and 7.0% of Dutch news articles contained a new quote by an expert source, i.e. one not provided by the press release. The relative odds that an article without an external expert quote contains an exaggeration of causality is 2.6. Conclusions The number of articles containing a quote from an independent expert is low, but articles that cite an external expert do contain less exaggeration.