News Information Research Articles

The effects of efficacy framing in news information and health anxiety on coronavirus-disease-2019-related cognitive outcomes and interpretation bias.

Within the coronavirus-disease-2019 (COVID-19) pandemic, disease-related information is omnipresent in the media, whereas information about how to manage the pandemic is less often covered. Under the context where threat is present, this study investigated whether and how the strength of efficacy framing (i.e., the perspective adopted by a communicating text that emphasizes one's possibilities to cope with an external threat) of COVID-19-related news, as well as its interaction with trait health anxiety under the COVID-19 context, related to people's COVID-19-related cognitive outcomes. One hundred and ninety-three participants reported demographics, trait health anxiety, and COVID-19-related behaviors (e.g., precautionary measures, information-seeking behaviors). They then either read high-efficacy (n = 112; e.g., cure rate) or low-efficacy (n = 81; e.g., mortality rate) information about COVID-19. Afterward, their tendency to interpret illness- and COVID-19-related information more negatively, and other COVID-19-related cognitions (e.g., risk perception, behavioral change intentions) were assessed. High-efficacy framing resulted in lower-risk perception and marginally weaker COVID-19-related interpretation bias, compared with low-efficacy framing. There was some evidence of an interaction with health anxiety such that high-efficacy framing, compared with low-efficacy framing, was associated with greater intention to adopt protective behaviors, particularly for individuals with higher levels of health anxiety. Media framing of COVID-19 information affects how people respond to the pandemic; a high-efficacy communication style might more effectively encourage healthy behaviors than a low-efficacy narrative, particularly for people who are already anxious about their health. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An Integrated Multi-Task Model for Fake News Detection

Fake news detection attracts many researchers’ attention due to the negative impacts on the society. Most existing fake news detection approaches mainly focus on semantic analysis of news’ contents. However, the detection performance will dramatically decrease when the content of news is short. In this paper, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fake news detection multi-task learning (FDML)</i> model based on the following observations: 1) some certain topics have higher percentages of fake news; and 2) some certain news authors have higher intentions to publish fake news. FDML model investigates the impact of topic labels for the fake news and introduce contextual information of news at the same time to boost the detection performance on the short fake news. Specifically, the FDML model consists of representation learning and multi-task learning parts to train the fake news detection task and the news topic classification task, simultaneously. As far as we know, this is the first fake news detection work that integrates the above two tasks. The experiment results show that the FDML model outperforms state-of-the-art methods on real-world fake news dataset.

Law Enforcement Through Social Media Pressure

Problems related to efforts to apply the law from the past have seized the attention of the Indonesian people because they are considered a very important phenomenon because it has the potential to have significant implications for Indonesian law which is basically still widely misused by legal practitioners. If viewed from the perspective of legal policy formulation that is correlated with various legal institutions at various levels, the performance of the work shown is categorized as not yet ideal and has not been able to realize the actual target of implementing the law. This encourages people to often express their disappointment through social media. From information and dissemination of social media news, it often develops into public opinion which in the language of social media communication is often called “viral news” which sometimes leads to the encouragement of law enforcement to act no longer because of law enforcement efforts but has become the fulfillment of public satisfaction and tends to ignore legal norms. Social media has made people sometimes change their image in the hope of getting sympathy and favorable public opinion for them so that it often becomes hyperreality, not reality anymore. Hyperreality is a form and state where you cannot distinguish between reality and fantasy. Through this paper, the researcher tries to do mini research through the socio-legal method where through this method the author seeks to explore further and at the same time explore law enforcement by not only based on the study of legal norms or doctrines, but also trying to see the full context of norms and their application. This study is solely aimed at providing an overview to the justice-seeking community that in the midst of uncertainty and rampant politicization of law enforcement, social media can be used as a force of coercion (pressure) as a means of law enforcement with justice.

Drivers of Covid-19 Vaccine Hesitancy in Southern Nigeria

Hesitancy against the Covid-19 vaccines cuts across all nations of the world, but this may not be unconnected to the trolls of misinformation and politicization surrounding the Covid-19 science. Findings from this study reveal that 84.7% of the study population lacks adequate information about the Covid-19 vaccines. 50% of the study population is skeptical about the safety of the Covid-19 vaccines. 100% of the study participants make use of the internet and other social media platforms for their sources of news information. 55.3% of the study population would follow the advice of their healthcare workers pertaining to the Covid-19 vaccines. 26.7% of the study population would listen to instructions given by their parents pertaining to the Covid-19 vaccines, while 20.7% of the study population would follow the guidance of their religious leaders respectively. Thus, the main drivers of Covid-19 vaccine hesitancy in Southern Nigeria are a lack of correct information about the Covid-19 vaccines, misinformation on social media platforms, resistance from religious leaders and parents, and a lack of support for the Covid-19 vaccine uptake on the part of healthcare workers. It is, therefore, imperative that in order to overcome the present challenge, the population of Southern Nigeria should be educated on Covid-19 related subjects and engaged with health promotion initiatives. While accepting the Covid-19 vaccines by the hesitant populations in Southern Nigeria is largely dependent upon the attitudes and influences of religious leaders, parents, and healthcare workers, a much larger study is required to fully establish the ramifications of these important findings. Keywords: Covid-19, Healthcare Workers, Immunization, SARS-CoV-2, Southern Nigeria, Vaccine Hesitancy.

Covid-19 Vaccine Hesitancy in Northern Nigeria

The rapid development of the Covid-19 vaccine candidates through new and improved technologies and the rapidity with which these vaccine candidates were trialed and approved for public administration was highly commendable. However, the rapid administration of the Covid-19 vaccines raises a lot of concern among various world populations. This concern, relating directly to the safety and intents of the big pharmaceutical companies and governments, has led to the greatest form of health promotion initiative resistance in recorded human history. The universal level of hesitancy against the Covid-19 vaccines is now a subject of major concern among public health experts. This study which seeks to identify factors fostering Covid-19 vaccine hesitancy was conducted among the COVID-19 vaccine hesitant populations in Northern Nigeria. Findings from this study indicate that 84% of the study population makes use of the internet and other social media platforms for their news information sources. 42% of the study population would follow the guidance of their healthcare workers on issues relating to the Covid-19 vaccines. Covid-19 vaccine hesitancy in Northern Nigeria is largely fostered by a lack of appropriate information and a huge knowledge gap about the Covid-19 vaccines. It is, therefore, imperative that Government engages the young people of Northern Nigeria as critical stakeholders in the fight against Covid-19 vaccine hesitancy. Proper and tailored health educational programs, and meaningful engagements of religious leaders, parents, and healthcare workers in health promotion activities can help overcome the current challenge of Covid-19 vaccine hesitancy in Northern Nigeria. Keywords: Covid-19, Healthcare Workers, Immunization, Vaccine, Northern Nigeria, Vaccine Hesitancy.

Analysis of the Impact of Age, Education and Gender on Individuals’ Perception of Label Efficacy for Online Content

Online content is consumed by most Americans and is a primary source of their news information. It impacts millions’ perception of the world around them. Problematically, individuals who seek to deceive or manipulate the public can use targeted online content to do so and this content is readily consumed and believed by many. The use of labeling as a way to alert consumers of potential deceptive content has been proposed. This paper looks at factors which impact its perceived trustworthiness and, thus, potential use by Americans and analyzes these factors based on age, education level and gender. This analysis shows that, while labeling and all label types enjoy broad support, the level of support and uncertainty about labeling varies by age and education level with different labels outperforming for given age and education levels. Gender, alternately, was not shown to have a tremendous impact on respondents’ perspectives regarding labeling; however, females where shown to support labeling more, on average, but also report more uncertainty.

Research on The Diminishing Effectiveness of Hot News Communication in The Era of Financial Media

In the era of media convergence, the rapid development of communication technology has given birth to a variety of media forms and communication channels. Various information platforms integrate multiple channels such as communication, network, content and receiving terminals to provide huge amounts of news information for audiences and users. These contents are complicated, and the speed of successive information dissemination is extremely fast, so there is a phenomenon that some news information is attracting attention at the beginning, and then the effectiveness of dissemination gradually decreases. Only by deeply studying the phenomenon of diminishing effectiveness of hot news dissemination, and exploring its formation mechanism, can we put forward solutions to better avoid the possibility of its emergence, and more effectively meet the audience's in-depth needs for news information.

Effect of Virtual Reality News Presentation on News Learning

This is the first study in which the effects of viewing VR/360-degree video with and without a Cardboard device on news learning outcomes are examined. Cognitive load theory and the dual-coding hypothesis were adopted as the theoretical framework. An experiment was performed with 213 participants watching news clips. The findings show that the participants learned news information better from normal broadcast video or when they watched VR/360-degree video without a Cardboard device than when they watched VR/360-degree video with a Cardboard device. The theoretical and practical implications of these findings are discussed.

Linked in the dark: A network approach to understanding information flows within the Dutch Telegramsphere

ABSTRACT Recent studies have shown that the stricter content moderation policies imposed by mainstream social networking sites (SNSs) stimulated the growth of low-moderated but relatively open discussion platforms such as Telegram. Despite Telegram’s growing popularity among (deplatformed) digital exiles, and high potential for news dissemination, information consumption, mobilization, and radicalization, little is known about information flows with respect to politically and socially relevant topics within the Telegramsphere. We scrutinize the Telegramsphere as an information-sharing ecosystem of current affairs by uncovering how information flows indicated by content-overlap and shared users influenced the structure of Telegram networks and shaped communities over time. Using state-of-the-art web-mining, neural topic modeling, and social network analysis techniques on a unique data set that spans the full messaging history (N = 2,033,661) of 174 Dutch-language public Telegram chats/channels, we show that over time, conspiracy-themed, far-right activist, and COVID-19-sceptical communities dominated the Dutch Telegramsphere of current affairs. Our findings raise concerns with respect to Telegram’s polarization and radicalization capacity in the context of consuming socially and politically relevant information online.

Research on Data News Propagation Path Based on the Big Data Algorithm

News propagation originates from a person/location, dwelling with an event that grabs significance. News data propagation relies on telecommunication and big data for precise content distribution and mitigation of false news. Considering these factors, the event-dependent data propagation technique (EDPT) was introduced to improve the data precision. These data refer to the news information originating and propagating from digital media. The data analysis considers the external factors for fake information and precise projection medium for preventing multiviewed false circulations. In this technique, the liability of the information is analyzed using a linear pattern support vector classifier. The data modification and propagation changes are classified based on liability information across the circulation time. The SVM classifier identifies these two factors with close liability validation, preventing false data. The data accumulation and analysis rates for the abovementioned classifications are performed in the propagation process using the classifier hyperplane. This plane is updated from the previous propagation point from which the events are identified. The proposed technique’s performance is analyzed using propagation accuracy, precision, false rate, time, and rate.

Front-Page News Classification Model Based on the Stacking of Textual Context and Attribute Information

The front-page news in authoritative newspapers usually represents extremely significant national policies. Accurately classifying the front-page news from an amount of news helps us to quickly acquire and deeply understand the changing political and economic situations. In this paper, we propose a front-page news classification model StackText based on stacking the textual context and attribute information of news. In the proposed model, we first balance the classification size in the training set through the weighted random sampling algorithm, then construct the context and attribute feature vectors through the textual embedding and statistical analysis of news, and finally pretrain a classifier StackNet based on a neural network to realize the front-page news classification in the testing set. Taking People’s Daily as the experimental example, we compare the proposed model with benchmark methods based on statistical learning and deep learning. Based on the news sets in four stages of People’s Daily, the experimental results of the front-page news classification show that the proposed model achieves the highest average accuracy and a better balance between precision and recall, which is verified by the corresponding F 1 -score.

Gestalt Roots and Social Activism: An Interview with Carolyn J. Lukensmeyer

Gestalt Roots and Social Activism: An Interview with Carolyn J. Lukensmeyer

Socially significant information and issues of the Kazakhstanis’ trust in the media

This article presents the results of the sociological survey of the Kazakhstan society on the issues of the information society and mass communication, which was a part of the programtargeted-funded project of the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan “Development of information technologies and systems for the sustainable personal development as a foundation for the development of digital Kazakhstan”. The study aimed at identifying preferences of various social categories in the choice of news and socially significant topics in the media space, and at assessing the influence of news of different thematic focus on the personal sustainable development (psychological/emotional, social and intellectual stability). The comprehensive analysis of the media as a channel of social influence has become more urgent under the pandemic, which is reflected in the wide use of the term ‘infodemic’ to assess information processes since the first half of 2020. The article considers the information agenda when choosing and assessing the importance of news content in the Kazakhstan media. Based on the postulate about significant differences in the information agenda of different social groups, the authors focus on the main factors of trust in information channels. The empirical data showed a correlation between the social significance of news information and the level of social trust in media. The study is based on the theoretical model combining two complementary factors: on the one hand, the influence of the media as possessing if not a monopoly (P. Bourdieu) then significant preferences in affecting public opinion and value orientations of various groups; on the other hand, the media dependence as determined by supply and demand. Based on various theories of the information agenda setting, the authors confirmed the need to take into account the social-demographic characteristics when forming the media content - when it is necessary to convey some specific information to different social groups.

Portal Berita Berbasis Web Pada PT. Jambi Ekspres Televisi (JEKTV)

The development of technology has a positive impact in the delivery of news or information to the public to be easier and faster without any distance limitation in accessing the news. News portal is one of the media that is widely accessed by the public to get information through the internet. At PT. Jambi Ekspres Televisi (JEKTV) does not have a news portal to deliver such information quickly. The methodology used in designing this news portal is the prototype method. The purpose of designing this news portal is expected to help the company's performance in serving the news needed by the community quickly and the community can receive the news information up to date.

Relevancy or Diversity?

Reading information in news feed apps has become a kind of popular content consumption in recent years. However, there are contradictory conclusions about the recommendation strategies. Although some previous research has studied the algorithms to improve the accuracy of the combined of relevancy and diversity strategies, how to compromise them according to user attitude and behavior from individual level is limited. This study aims to solve the dilemma at a theoretical level, and the authors find that the degree of multi-context use is a key boundary. Specifically, 1) the location-based recommendation method may lose effectiveness when the degree of a multi-context is high. 2) The users who prefer to use news feed systems in various contexts in their daily life are more likely to read diverse information. The authors suppose that with the increase of the degree of multi-context use, the location-based recommendation may trigger users perceived privacy threat, which will reduce their satisfaction and participation intention.

(Retracted) News image text classification algorithm with bidirectional encoder representations from transformers model

With the development of Internet technology and the transformation of news media to informatization, news images, texts, and other information have exploded. The news image and text classification can effectively solve the disorder problem of news information. The early news image text classification is to establish artificial classifiers according to specific classification rules, but the classification error rate is high and the classification speed is slow. Later, machine learning technology replaced manual classification of news image texts. Although the classification efficiency has been greatly improved, the classification time is still a bit long. The bidirectional encoder representations from transformers (BERT) model uses transformer and encoder to pretrain news image text to improve classification efficiency. By comparing the differences between machine learning and BERT models in news image text classification, the experiments showed that the average precision, recall, and F1 values of the news image text classification algorithm using the BERT model were 96.6%, 95.7%, and 96.1%, respectively. All three evaluation criteria were about 5% more than the classification algorithm of the machine learning model. The classification speed of the news image text classification algorithm using the BERT model was 1.8 times that of the news image text classification algorithm based on support vector machine. Therefore, the news image text classification algorithm using the BERT model can improve the classification accuracy and efficiency of news image text.

Information flow estimation: A study of news on Twitter

News media has long been an ecosystem of creation, reproduction, and critique, where news outlets report on current events and add commentary to ongoing stories. Understanding the dynamics of news information creation and dispersion is important to accurately ascribe credit to influential work and understand how societal narratives develop. These dynamics can be modelled through a combination of information-theoretic natural language processing and networks; and can be parameterised using large quantities of textual data. However, it is challenging to see “the wood for the trees”, i.e., to detect small but important flows of information in a sea of noise. Here we develop new comparative techniques to estimate temporal information flow between pairs of text producers. Using both simulated and real text data we compare the reliability and sensitivity of methods for estimating textual information flow, showing that a metric that normalises by local neighbourhood structure provides a robust estimate of information flow in large networks. We apply this metric to a large corpus of news organisations on Twitter and demonstrate its usefulness in identifying influence within an information ecosystem, finding that average information contribution to the network is not correlated with the number of followers or the number of tweets. This suggests that small local organisations and right-wing organisations which have lower average follower counts still contribute significant information to the ecosystem. Further, the methods are applied to smaller full-text datasets of specific news events across news sites and Russian troll accounts on Twitter. The information flow estimation reveals and quantifies features of how these events develop and the role of groups of trolls in setting disinformation narratives. In summary, this work provides a new methodology for examining the information transmitted between content producers in any connected system of natural language, a toolkit with applications to the many networked discourses of our online world.

Moral panics about the integrity of information in democratic systems:Comparing tabloid news to disinformation

ABSTRACT Contemporary concerns about the integrity of information are not easily dismissible as merely a perennial cycle of moral panic. In an attempt to provide context and map territory for future research, this conceptual paper draws comparisons between disinformation, the deliberate fabrication and dissemination of false information, and tabloid news – a news format with a century’s old reputation for sparking consternation over journalistic truth telling. Sociological functions, information economy particularities, and message packaging features are comparatively scrutinized, revealing areas of divergence, convergence, and symbiotic interface between disinformation and tabloid news that likely afford the success of both formats.

Characteristics associated with optimistic or pessimistic perception about the probability of contracting COVID-19: A cross-sectional study of Japanese older adults

IntroductionExcessive optimistic perception about the probability of acquiring coronavirus disease (COVID-19) may hinder people from exercising preventive measures, whereas excessive pessimistic perception can induce psychological problems. Not much focus has been paid to this topic, and prior studies are only online surveys. We determined the characteristics of older adults with optimistic and pessimistic perceptions of the probability of contracting COVID-19. MethodsWe used data from the Japan Gerontological Evaluation Study (JAGES), including 18,045 participants aged ≥ 65 years (mean age: 75.7 years) who were physically and cognitively independent. Self-reported questionnaires were sent to 11 municipalities between November 2020 and February 2021. Multinomial logistic regression was used for data analysis. ResultsThe characteristics of 1,596 (8.8%) participants with optimistic perception and 1,276 (7.1%) with pessimistic perception were compared with that of others (80.4%) with moderate perception. Optimism about infection probability was positively associated with older age; better perceived financial conditions but negatively associated with higher education level; trust in TV news programs, TV information programs, and government-issued newsletters; depressive symptoms; and higher levels of reciprocity. Pessimism was negatively associated with higher levels of social cohesion. In contrast, it was positively associated with engagement in paid work; trust in TV news programs, the Internet, and information from medical staff; and depressive symptoms. ConclusionOptimistic and pessimistic perceptions about the probability of acquiring infection correlated differently with various characteristics. Thus, risk communication during a pandemic should be tailored based on specific individual characteristics.

Irish audiences and news information from official sources during Covid-19

Abstract Audiences exist in highly personalised, high-choice media environments built on a hybrid of established traditional brands and informal digital networks. Officials trying to reach the public must navigate such spaces, but public reluctance to consume news coverage is a challenge for health and government officials when trying to communicate with and inform the public during a national health crisis like Covid-19. Based on a representative survey (N=2,031) from the 2021 Reuters Digital News Report, this article focuses on Irish audiences’ information sources during the pandemic; in particular, how government and political sources were used and perceived. The article is a secondary analysis of the data set and focuses on three questions from the survey related to (i) sources of information about Covid-19, (ii) concern about sources of false or misleading information about Covid-19, and (iii) sources of local information about politics and local updates on Covid-19. The article finds that official sources were relatively effective in being heard, and that health agencies like the Health Service Executive and the National Public Health Emergency Team were more salient than politicians, suggesting the pandemic was perhaps apolitical in the eyes of the public, which is often a key strategy for effective crisis communication. Politicians and government actors also succeeded in not being perceived as the main source of concern in terms of false or misleading information, as audiences were more worried about activists. The article also reiterates the importance of health officials reaching out beyond traditional news distribution channels to engage groups who may not access news through traditional channels.