News Sites Research Articles

Using the “Media as Mediator” Approach to Understand the Influence of Communication Channel Trust on COVID-19 Protective Behaviors: England, January 2022

In a secondary analysis, we examine how trust in pro-recommendation versus alternative communication channels mediated effects of demographic, personality, lifestyle, and political variables on COVID-19 protective behavior in England. In so doing, we adapt the media-as-mediator approach to the pandemic context. Respondents reported that family, close friends, primary care medical providers, and mainstream news media were relatively supportive of public health recommendations, and social media friend networks, faith/community groups, alternative news sites, and alternative health practitioners were relatively unsupportive. Parallel mediation analyses showed that effects of age, dutiful civic-mindedness, sensation-seeking, healthy lifestyle orientation, and more marginally, race on COVID-19 protective behavior were mediated by trust in these pro-recommendation and/or alternative communication channels. In some cases, trust in exemplars of both types of channels resulted in these channels influences largely canceling one another out.

Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling Study.

e-Cigarette (electronic cigarette) use has been a public health issue in the United States. On June 23, 2022, the US Food and Drug Administration (FDA) issued marketing denial orders (MDOs) to Juul Labs Inc for all their products currently marketed in the United States. However, one day later, on June 24, 2022, a federal appeals court granted a temporary reprieve to Juul Labs that allowed it to keep its e-cigarettes on the market. As the conversation around Juul continues to evolve, it is crucial to gain insights into the sentiments and opinions expressed by individuals on social media. This study aims to conduct a comprehensive analysis of tweets before and after the ban on Juul, aiming to shed light on public perceptions and sentiments surrounding this contentious topic and to better understand the life cycle of public health-related policy on social media. Natural language processing (NLP) techniques were used, including state-of-the-art BERTopic topic modeling and sentiment analysis. A total of 6023 tweets and 22,288 replies or retweets were collected from Twitter (rebranded as X in 2023) between June 2022 and October 2022. The encoded topics were used in time-trend analysis to depict the boom-and-bust cycle. Content analyses of retweets were also performed to better understand public perceptions and sentiments about this contentious topic. The attention surrounding the FDA's ban on Juul lasted no longer than a week on Twitter. Not only the news (ie, tweets with a YouTube link that directs to the news site) related to the announcement itself, but the surrounding discussions (eg, potential consequences of this ban or block and concerns toward kids or youth health) diminished shortly after June 23, 2022, the date when the ban was officially announced. Although a short rebound was observed on July 4, 2022, which was contributed by the suspension on the following day, discussions dried out in 2 days. Out of the top 50 most retweeted tweets, we observed that, except for neutral (23/45, 51%) sentiment that broadcasted the announcement, posters responded more negatively (19/45, 42%) to the FDA's ban. We observed a short life cycle for this news announcement, with a preponderance of negative sentiment toward the FDA's ban on Juul. Policy makers could use tactics such as issuing ongoing updates and reminders about the ban, highlighting its impact on public health, and actively engaging with influential social media users who can help maintain the conversation.

MEDIA REPRESENTATION OF THE IRAQI KURDISTAN REGION DURING THE REFERENDUM: A LINGUISTIC ANALYSIS

This study conducted a linguistic analysis of the media representation of the Iraqi Kurdistan Region during the Kurdistan Referendum on September 25, 2017. The research aimed to bridge two methodologies: corpus linguistics and critical discourse analysis. By examining how the media portrayed the Iraqi Kurdistan Region during the referendum, this study shed light on the linguistic dimensions of media coverage and the issues surrounding the referendum. Data were collected from six different news sites worldwide and analysed using corpus tools through AntConc. The findings showed that Western media outlets in the United Kingdom and the United States, including BBC News, CNN, Fox News, and Sky News, represented the Iraqi Kurdistan Region Referendum negatively. In contrast, Rudaw, a local media channel in Iraqs Kurdistan Region, provided a completely positive portrayal, while Al Jazeera maintained an unbiased attitude.

Reported speech and gender in the news: Who is quoted, how are they quoted, and why it matters

News stories have a well-defined generic structure, consisting of components such as headline, lede, and body, with reported speech a prominent feature, especially in hard news stories. Reported speech serves multiple purposes, from providing evidentiality and intertextuality to contributing to the construction of newsworthiness and to the context creation of news. It is also a site of potential bias in who is cited and how, including with respect to the gender of sources. Using a large corpus of English-language news stories for all of 2023 from the main five mainstream news outlets in Canada (over 370,000 articles from news websites), I examine the gender distribution of those quoted, the syntactic variation in the structure of quotes, and the types of reporting verbs. The study provides a comprehensive overview of the extend of gender bias in contemporary Canadian news, at the same time offering insights into the nature of reported speech in modern news and how it endures and evolves, including in news meant for digital-only publication.

Patterns of News Consumption during the COVID-19 Pandemic Crisis: A 2.5 Year Longitudinal Study in the Netherlands

ABSTRACT During major long-term crises, such as the COVID-19 pandemic, news media are crucial sources of information for the public. This study aimed to explore the frequency of COVID-19-related news consumption based on (1) phase of the pandemic, (2) socio-demographic characteristics, and (3) news information channels. The study used a dynamic cohort design with 18 rounds of data collection, including 306,692 responses from 83,180 unique respondents in the Netherlands from 17 April 2020 to 11 September 2022. Results showed that the frequency of general COVID-19-related news media consumption varied throughout the pandemic, following a general decreasing trend with relative spikes often coinciding with periods of stricter behavioural regulations. TV news, newspapers, and online news websites were the most popular news information channels among the respondents. Furthermore, multilevel regression analyses identified several socio-demographic factors influencing news consumption and preferred channels, namely age, migration background, living status, health status, and trust in government — these results remained stable throughout the pandemic. This study highlights the importance of selecting appropriate news channels to effectively reach different socio-demographic groups and shows that during a prolonged crisis, news consumption about the crisis fluctuates with worsening conditions but generally follows a decreasing trend.

Negative online news articles are shared more to social media

Prior research demonstrates that news-related social media posts using negative language are re-posted more, rewarding users who produce negative content. We investigate whether negative material from external news sites is also introduced to social media through more user posts, offering comparable incentives for journalists to adopt a negative tone. Data from four US and UK news sites (95,282 articles) and two social media platforms (579,182,075 posts on Facebook and Twitter, now X) show social media users are 1.91 times more likely to share links to negative news articles. The impact of negativity varies by news site and social media platform and, for political articles, is moderated by topic focus, with users showing a greater inclination to share negative articles referring to opposing political groups. Additionally, negativity amplifies news dissemination on social media to a greater extent when accounting for the re-sharing of user posts containing article links. These findings suggest a higher prevalence of negatively toned articles on Facebook and Twitter compared to online news sites. Further, should journalists respond to the incentives created by the heightened sharing of negative articles to social media platforms, this could even increase negative news exposure for those who do not use social media.

Examining the Alignment of Ideological Expression with Behavior or Deception: An Experimental Investigation of Chinese Youth's Political Attitudes Towards America

<i>Background</i> In the context of globalization, as one of the most significant bilateral relations in the world, the dynamics of China-US relations exert a considerable influence on the world. Simultaneously, the accelerated advancement of Internet technology has provided a more expansive array of avenues for acquiring information. Through Social media, news websites, and international forums, Chinese youth groups demonstrate a heightened interest in global affairs. However, the divergence between disparate cultural and political systems has resulted in Chinese youth developing nuanced and multifaceted political dispositions when engaging with the United States and other Western nations. It is therefore imperative to study the political attitudes of Chinese youth, particularly to the United States. <i>Objectives</i> The concept of political attitude expression refers to the external manifestation of an individual's ideological position's usefulness. However, there is a lack of empirical research examining the alignment between an individual's inner motivation and their expression of political attitudes. Based on the aforementioned information, an experimental study was devised and executed with the aim of discerning political attitudes. The study focused on individuals' external expressions of political attitudes and their intrinsic motivations, specifically in relation to the United States. The objective was to examine whether specific factors could potentially influence the external manifestation of individuals' internal ideologies.<i> Main ideas</i> Various factors contribute to the expression of individual ideologies. The ideologies expressed by individuals do not solely reflect their ideological beliefs, but rather arise from a complex interplay of moderating factors influenced by individual rationality regarding economic costs and social circumstances. <i>Conclusions</i> (1) The political attitudes of Chinese youth toward the United States in general demonstrate a left-leaning trend, with low favorability toward the U.S. (2) Economic interests exert an influence on ideological expression, and individuals holding a left-leaning ideological position may elect to accept financial incentives and formally demonstrate high favorability toward the U.S., driven by economic interests. (3) Transformations in the social milieu may precipitate a phenomenon whereby individuals are inclined to "follow the crowd," concealing their authentic political dispositions and eschewing genuine political expression.

The Application of Zwart’s Shift Model to the English-Arabic Translation of Technology News

The present research paper discusses the applicability of Zwart’s “Comparative–Descriptive Model of Translation Shifts” (1984) to translating Sci-Tech news from English into Arabic. This comprehensive model primarily investigates the translation shifts occurring in literary discourse. To the best of the reCsearcher’s knowledge, very few attempts have been made to apply this shift analysis-based model to the translation of other types of discourse. The paper applies this model to translating scientific discourse, particularly technology news, on several reliable news websites, such as BBC and CNBC. English news on the English websites (ST) and the Arabic translation (TT) on these websites are used as data for the comparative and descriptive analysis. Zwart’s comparative model describes the translation shifts on the microstructural level by comparing the architranseme and the SL and TL transoms. The descriptive model provides insights into the shifts on the macrostructural level. The paper concludes that shifts occurring in the translation of Sci-Tech news can be best described, analysed, and categorised within Zwart’s comparative model. It also concludes that shifts on the microstructural level do not necessarily lead to a noticeable macrostructural change in the primary language metafunctions (interpersonal, ideational, and textual) as in the translation of literary works. Syntactic shifts may, however, result in a macrostructural change of the textual metafunction, which alters the propositional content in some instances.

Cross-Cutting Exposure in Online News Comments: The Effects of Group Identification and Uncivil Discourse on Opinion Conformity and Expression

ABSTRACT Drawing upon the social identity model of deindividuation effects, this study investigated the effects of intergroup relations and discursive quality on individuals’ responses to comments that challenge their existing beliefs on news websites. A two-session online experiment with a 2 (group membership: in-group vs. out-group) × 2 (comment tone: civil vs. uncivil) between-subjects factorial design was conducted. The results showed that exposure to counter-attitudinal comments from in-group members, compared to out-group members, led to greater opinion conformity and less expression of disagreement. Additionally, there was a significant interaction between group membership and comment tone on disagreement expression. When counter-attitudinal comments were uncivil, individuals exposed to in-group comments expressed less dissent in their replies than those exposed to out-group comments. This tendency, however, was not significant when the counter-attitudinal comments were expressed in a civil tone.

Infant formula donations and code violations during earthquake relief efforts in Türkiye in 2023: an observational study

BackgroundOn 6 February 2023, an Mw 7.8 earthquake struck southern and central Türkiye and north-western Syria, affecting the lives of 4.6 and 2.5 million children, respectively. In such crises, infants who are dependent on commercial milk formula (CMF) face increased vulnerability to diseases and malnutrition as safe preparation of CMF becomes difficult and sometimes impossible. The Operational Guidance on Infant and Young Child Feeding in Emergencies (OG-IFE) provides guidance on protecting and supporting recommended infant and young child feeding and minimizing the risks that come with CMF feeding. In addition, the International Code of Marketing of Breastmilk Substitutes (the Code) ensures adequate nutrition for infants by protecting and promoting breastfeeding and ensuring the proper usage of CMF. This study aims to document violations of the Code and the OG-IFE during the earthquake relief efforts to help strengthen infant and young child feeding emergency responses and inform future disaster relief policies.MethodsData was collected from 6 February to 10 March 2023 through Internet sources. Social media, news websites, and the Emergency Nutrition Network forum were used for data collection. Turkish content was translated into English for analysis, with a focus on donation-related information and Code violations related to baby food and infant feeding.ResultsA total of 40 reports on CMF, complementary food, and feeding equipment donations were collected. Three main types of violations of the OG-IFE and the Code were identified, with the majority of them being incidences of individuals, humanitarian organizations, and government agencies seeking or accepting donations. Infant formula companies continued to violate the Code by donating large quantities of CMF and feeding equipment to the Turkish Red Crescent, government agencies, and disaster relief infant and young child feeding (IYCF) coordination authorities.ConclusionsThese incidents reflected a systematic violation of the Code and non-compliance with the OG-IFE. Globally accepted IYCF standards and recommendations were not consistently followed due to fragmented early responses. There is a critical need to step up efforts to ensure appropriate and safe IYCF practice protecting and supporting breastfed and non-breastfed infants in emergencies.

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.

Content Analysis of Online Resources Regarding Needs for Advance Care Planning.

This study aimed to investigate advance care planning needs expressed online. This study collected data from online community posts and healthcare news sites. The search keywords included "death," "euthanasia," "life-sustaining medical care," "life-sustaining treatment," "advance directives," "advance medical directives," and "advance care planning." Data collection spanned from February 2018 to February 14, 2020. Out of 2,288 posts, 1,190 were included in the final analysis. Data analysis was conducted using NVivo 12, a qualitative data analysis software program. Content analysis categorized patients' advance care planning needs into eight themes, 11 theme clusters, and 33 meaningful statements. Similarly, care providers' advance care planning needs were categorized into eight themes, 14 theme clusters, and 42 meaningful statements. The identified themes of care needs included life-sustaining medical care, decision-making related to life-sustaining medical care, physical care, environmental care, supportive and spiritual care, respect, preparing for death, and family. This study identified care needs from the perspectives of patients and their families. The findings may serve as preliminary data for future research and clinical applications.

Examining the Barriers to Licensed Private Cannabis Retailers in Canada: A Quantitative Content Analysis of Canadian News Media Coverage

Background Since the Government of Canada legalized non-medicinal cannabis use/consumption on October 17, 2018, licensed private cannabis retailers have faced numerous challenges. These challenges negatively impacted retail operations and potentially undermined public health and safety. We aimed to identify these challenges to inform policy decisions. Methods We conducted a quantitative news media content analysis using Nexis Uni and Eureka databases to identify articles from Canada's highest-circulating newspapers and CBC News website from 2017 to 2022, referencing at least one barrier to private cannabis retailers. We screened and extracted data using Covidence and deductively coded the data using our newly developed comprehensive cannabis retail framework and inductively as new themes emerged. The barriers identified in the media were quantified through descriptive analyses. Results The search yielded 9,371 articles, of which 307 relevant articles were included. The main findings revealed that the barriers most commonly reported by the media were related to government regulations, supply chain, and competitors. The salience of these barriers also changed over time. These barriers were most frequently mentioned in the Globe and Mail, Calgary Herald, Toronto Star, and the National Post. Conclusion Our framework was suitable for describing the data and identifying several media-portrayed barriers to private cannabis retail operations in Canada and how they differed in salience over time. However, a more in-depth understanding of the barriers from retailers’ perspectives may further support the government's policy agenda of achieving public health and safety. The results from this study may also serve as a baseline measure to evaluate Canada's cannabis retail market and provide new insights into the growing body of literature about the cannabis retail market.

Archaeo-media: breaking the binary and building agency in archaeological news reporting

ABSTRACT The role of news media in the dissemination of archaeological research is beginning to receive some attention, but this is inadequate when considering the scale of the news media as a tool for public archaeology and mass-distribution of archaeological research in digital news sites. Archaeology needs to urgently address this oversight and begin to construct appropriate and sustainable working relationships with the news media, founded on a critical evaluation of current strategies, to regulate the information that is disseminated through this medium. This paper takes a British perspective, though the themes and necessary improvements have global significance. I suggest that we begin to appreciate the role of the archaeologist in the construction of knowledge through the mass media by embracing ‘archaeo-media’- a neologism here proposed to explore the intersections and interactions between archaeology and news media.

Russian internet news sites, 2008–2018: Fostering an informed citizenry?

Since Perestroika, Russian journalism has gone through a liberalization. However, the short lived apex of journalistic freedom that took place in the late 1980s and early 1990s was followed by a setback in particular since Putin’s accession to power in 2000. The situation for the freedom of the daily press has stagnated. Despite this, the findings based on qualitative text analysis of news articles in some of the most important Russian news sites, strongly indicate that during 2008–2018 readers of news sites were being addressed as active and knowledgeable citizens. The methods focussing on perspectivation and the use of sources, exploring what voices that are used to get messages through and how they are represented, are applied to four case studies casting light on the period. The findings imply that a number of Russian Internet outlets had strengthened their role as advocates of the Fourth Estate. The results further indicate a sharp distinction between news sites that are utilizing traditional Western journalistic devices, and news sites employing a traditional Russian/Soviet journalistic approach. Thus, the roles of the audiences as active citizens were to a certain extent strengthened.

Measuring vaccination coverage and concerns of vaccine holdouts from web search logs

To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or not granular enough, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here we show how search engine logs and machine learning can help to fill these gaps, using anonymized Bing data from February to August 2021. First, we develop a vaccine intent classifier that accurately detects when a user is seeking the COVID-19 vaccine on Bing. Our classifier demonstrates strong agreement with CDC vaccination rates, while preceding CDC reporting by 1–2 weeks, and estimates more granular ZIP-level rates, revealing local heterogeneity in vaccine seeking. To study vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 67% likelier to click on untrusted news sites, and are much more concerned about vaccine requirements, development, and vaccine myths. Even within holdouts, clusters emerge with different concerns and openness to the vaccine. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators predict when individuals convert from holding out to seeking the vaccine.

Creating, publishing, and spreading processes of health-related contents in internet news sites: evaluation of the opinions of actors in health communication.

The accuracy and reliability of health information disseminated through news is crucial, as it directly impacts both individual and societal health outcomes. This study aims to analyze the publication process of health content in Türkiye and its implications for public health. By examining the perspectives of various health communication stakeholders, the study seeks to identify existing issues and propose potential solutions. The research uses a mixed-methods approach, including baseline content analysis of 846 news by 133 criteria, quantitative research with 78 participants encompassing bureaucrats, academics, journalists, and health association members, and 15 in-depth interviews for comprehensive insights. The content analysis indicated that 23.2% of the analyzed news articles lacked credible sources, while 63% did not mention the author's name. A striking 96.2% of respondents stated that inaccurate health news poses a risk to public health, emphasizing the urgent need for standardized reporting practices. The majority (90.9%) pinpointed the media as the primary catalysts for infodemic spread, with 93.5% citing gatekeepers as barriers to accurate information. Eroding trust in media, fueled by unethical practices, harms both media credibility and effective public health interventions. The study underscores the necessity for a collaborative approach among public institutions, academia, and media, focusing on responsibility, regulation, and sanctions against the infodemic. The research advocates for a balanced approach that prioritizes health rights and press freedom within a stakeholder-driven framework, highlighting that legislation alone cannot fully enhance the digital information ecosystem.

Determining the Public Opinion on Rappler as A News Media Critic of the Administration

With the rise of social media and online platforms wherein information is only just a click away, there is still a grey area as to whether online users trust news sites and pages on the digital space. This study aims to assess and analyze the public perception of Rappler as a news media outlet wherein it highlights its role as a news media critic of the Duterte administration. The researchers collected the needed data to identify the public opinion of Filipino adults who are aged 18 and above regarding Rappler's critical way of reporting. This study is supported by data, articles, and related literature that are mainly focused on media trust, media credibility, and related factors towards the reported trust in media. Moreover, this study expounds the surrounding issues related to Rappler's investigative journalism. The study has been operationalized through online surveys and by setting certain eligibility requirements, which served as the qualifier for the respondents before answering the questionnaire. Additionally, this study shows a glimpse of the Media Effects Theory and its correlation to the study's nature. Upon analyzing the gathered data, results show that most respondents trust Rappler as a news media outlet critical of the Philippine government.

Fine‐Tuned Pretrained Transformer for Amharic News Headline Generation

ABSTRACTAmharic is one of the under‐resourced languages, making news headline generation particularly challenging due to the scarcity of high‐quality linguistic datasets necessary for training effective natural language processing models. In this study, we fine‐tuned the small check point of the T5v1.1 model (t5‐small) to perform Amharic news headline generation with an Amharic dataset that is comprised of over 70k news articles along with their headline. Fine‐tuning the model involves dataset collection from Amharic news websites, text cleaning, news article size optimization using the TF‐IDF algorithm, and tokenization. In addition, a tokenizer model is developed using the byte pair encoding (BPE) algorithm prior to feeding the dataset for feature extraction and summarization. Metrics including Rouge‐L, BLEU, and Meteor were used to evaluate the performance of the model and a score of 0.5, 0.24, and 0.71, respectively, was achieved on the test partition of the dataset that contains 7230 instances. The results were good relative to result of the t5 model without fine‐tuning, which are 0.1, 0.03, and 0.14, respectively. A postprocessing technique using a rule‐based approach was used for further improving summaries generated by the model. The addition of the postprocessing helped the system to achieve Rouge‐L, BLEU, and Meteor scores of 0.72, 0.52, and 0.81, respectively. The result value is relatively better than the result achieved by the nonfine‐tuned T5v1.1 model and the result of previous studies report on abstractive‐based text summarization for Amharic language, which had a 0.27 Rouge‐L score. This contributes a valuable insight for practical application and further improvement of the model in the future by increasing the article length, using more training data, using machine learning–based adaptive postprocessing techniques, and fine‐tuning other available pretrained models for text summarization.

Mitigating subjectivity and bias in AI development indices: A robust approach to redefining country rankings

Artificial Intelligence (AI) indices have emerged to assess countries’ progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices are derived from a linear weighted sum of various criteria, employing deterministic weights. However, this approach fails to capture interactions among criteria, and the use of deterministic weights is susceptible to debate. To mitigate these issues, we conduct a methodological analysis based on Choquet integral (CI) and Stochastic Multicriteria Acceptability Analysis 2 (SMAA-2). We assess correlations between different AI dimensions and employ CI to model them. Additionally, we apply SMAA-2 to conduct a sensitivity analysis using both weighted sum and CI in order to evaluate the stability of the indices with regard to the weights. Finally, we introduce a ranking methodology based on SMAA-2, which considers several sets of weights to derive the ranking of countries. In the computational analysis, we evaluate our approach using the dataset employed in The Global AI Index, as proposed by the British news website Tortoise. The results reveal that our approach effectively mitigates bias. Furthermore, we scrutinize changes in the ranking resulting from weight adjustments and demonstrate that our proposed rankings closely align with those derived from variations in weights, indicating robustness.