Traditional News Outlets Research Articles

Local Newspaper Decline and Political Polarization – Evidence from a Multi-Party Setting

Abstract How does the decline of traditional news outlets affect political polarization? We provide novel evidence on this question by examining the link between local newspaper exits, media consumption, and electoral behaviour in a multiparty setting. Our empirical analysis combines a unique panel of all German local newspapers between 1980 and 2009, electoral returns, and an annual media consumption survey of more than 670,000 respondents. Using a difference-in-differences design, we demonstrate that local newspaper exits increase electoral polarization. Additional analysis points to changes in media consumption as the underlying mechanism driving this result: following local news exits, consumers substitute local news with national tabloid news. Our findings extend prior results in the US context to a multiparty setting and shed new light on the causal chain running from changing local news landscapes to electoral behaviour.

Determining Trust in Information: Initial Literature Review

ABSTRACTPew Research Center's findings reveal a notable trend: U.S. adults under the age of thirty exhibit a level of trust in information on social media that is comparable to their trust in traditional news outlets (Liedke & Gottfriend, 2022). This is particularly significant given the growing concern over misinformation, with a staggering 95% of Americans acknowledging its prevalence in society (Seitz & Fingerhut, 2021). To explore the factors influencing individuals' trust in information, researchers conducted a preliminary review of the literature, focusing on information science. This preliminary review identified seven major themes (health, politics & business, information sources, social factors, age groups, education & language, and privacy & security) that will guide a more extensive systematic review in the future. These themes provide valuable insights into the complexities of trust in information and should serve as a foundation for further investigation. Insights derived from this preliminary review are invaluable for scholars and practitioners seeking a deeper understanding of information trust dynamics. By illuminating these complex factors, this research lays the groundwork for future studies and interventions aimed at enhancing information trust among the general public.

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

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

Not Quite Filling the Void: Comparing the Perceptions of Local Online Groups and Local Media Pages on Facebook

With the steady closure of local newspapers, news consumers increasingly turn to community forums and neighborhood apps to fill the information void. This study investigates how local online groups are perceived relative to more traditional local news outlets, and compares the benefits provided by each information source. Based on prior theoretical contributions, we develop a framework for measuring the benefits of local information on individual-level pro-community attitudes (attachment, knowledge, and civic attitudes.) In a field experiment (N=170), we asked frequent Facebook users living in four U.S. cities to start following local news pages or local online groups on Facebook for one month, and compared their perceptions of source quality and changes in pro-community attitudes. We find that posts from local news pages are perceived to be of higher quality than posts from local online groups. However, following local news pages or local groups did not lead to significant changes in pro-community attitudes during our study period. We discuss implications for the future study of local news in a changing media ecology.

Semantic Networks of Election Fraud: Comparing the Twitter Discourses of the U.S. and Korean Presidential Elections

Traditional news outlets, such as newspapers and television, are no longer major sources of news. These media channels have been replaced by social platforms, which have increased in value as information distributors. This change in communication is an underlying reason for the election fraud controversies that occurred in the United States and South Korea, which hold high standards of democracy, during similar periods. This study investigates a model for sharing political disputes over social networks, especially Twitter, and illustrates the influence of political polarization. This study examines Twitter content around the presidential elections in the United States and South Korea in 2020 and 2022, respectively. It applies semantic network analysis and structural topic modeling to describe and compare the dynamics of online discourse on the issue of election fraud. The results show that online spaces such as Twitter serve as public spheres for discussion among active political participants. Social networks are key settings for forming and spreading election fraud controversies in the United States and South Korea, with differences in content. In addition, the study applies large-volume text data and new analytical methods such as the structural topic model to examine the in-depth relationships among political issues in cyberspace.

Stylometric Fake News Detection Based on Natural Language Processing Using Named Entity Recognition: In-Domain and Cross-Domain Analysis

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

Innovating Online Journalism: New Ways of Storytelling

ABSTRACT Journalism faces a number of challenges: patterns of news consumption have changed and audiences for traditional news outlets are declining. In this context, we explore whether the “inverted pyramid” model – a system of news writing that arranges facts in descending order of importance, which remains predominant in journalism – is the most effective way of communicating information online. Based on a mixed-methods approach using qualitative data from workshops and expert consultations, we developed a series of new “prototypes” of online news storytelling and tested them with a wide range of audiences (N = 1268). Building on earlier work, we find that linear forms of storytelling - rarely used in news - are more effective in transferring knowledge to news consumers and are seen as more engaging, convenient and useful than the traditional inverted pyramid. We then identified key principles that should underlie a more user-focussed approach to narratives in online news.

Application and prospect of new media in sports news dissemination

In the process of the rapid development of Internet technology, new media has been maturing with the development of information technology, providing a new way for information dissemination. The media promotes sports and draws people's attention to them. In this research, the same topic was revealed with documentary data, the qualitative method was used to clarify the topic, and all the accurate information that was published in reliable books, scientific magazines, and internet sites was collected. The application of new media in the process of sports news dissemination requires research and analysis of the application and development of new media, which is conducive to mastering the application of new media in sports news, understanding the development trend of new media, and giving full play to sports news. spread effect. Research findings suggest that new media has revolutionized the way sports news is disseminated and consumed. With the rise of social media platforms, sports fans now have access to instant updates and analysis from a variety of sources, including traditional news outlets, blogs, and social media influencers. The application of new media in sports news dissemination has led to increased engagement between sports fans, athletes, teams, and media outlets. Social media platforms such as Twitter, Instagram, and Facebook allow for real-time communication and interaction between these groups, creating a more immersive and interactive experience for fans.

¿Qué pasa with American News Media? How Digital-Native Latinx News Serves Community Information Needs Using Messaging Apps

Traditional and legacy Spanish-language news outlets are shrinking, leaving many communities in the U.S. without news that satisfies their critical information needs. At the same time, news media entrepreneurs are adopting and adapting to technological changes that offer new venues for news delivery. We use a mixed-methods research approach to analyze Latinx digital-native news media organizations that distribute news in Spanish via mobile instant messaging (MIM) apps. Results show that journalists took a bottom-up approach to meeting the information needs of their audiences, choosing MIMs because their readers were already established on those platforms. Our work also reveals that news delivered via MIMs focuses mostly on health, emergencies and public safety, civic information, and economic development.

An explainable model for content moderation

The spread of fake news on social media has eroded trust in traditional news outlets and institutions. In response, social media platforms have incorporated machine learning, algorithms that can learn patterns in data without explicit programming, into their content moderation systems to help remove fake posts. However, these algorithms often misinterpret language and make poor moderation decisions. We need to better understand how machine learning algorithms interpret language. Explainability is a challenge with neural networks, a popular machine-learning model inspired by how neurons communicate in the brain. In this study, we sought to develop an explainable model for content moderation comparable in accuracy to a traditional neural network, focusing on classifying real and fake news articles. We hypothesized that by identifying keywords and quantifying their contribution to an article’s credibility, we could predict credibility by summing the contributions of all the keywords in an article. We trained a convolutional neural network to classify articles using a 3,000-keyword vocabulary, achieving 85.8% accuracy. We used the Shapley additive explanations algorithm to calculate each keyword's median contribution to the model’s predictions. We created a linear model that summed the median contributions of keywords in an article, achieving a comparable 81.0% accuracy. We then examined keywords with the largest median contributions. Clickbait and COVID-19 terms correlated with fake news. Legal and political terminology correlated with real news. Our results demonstrate the potential for explainable models to improve our understanding of content moderation algorithms and fake news linguistics.

Applications of Machine Learning for Fake News Detection in Social Networks

The value of online media for getting news is questionable. People seek out and devour news from online media because it is convenient, inexpensive, and widely disseminated. In contrast, it facilitates the widespread distribution of "counterfeit news," or news of lower quality that includes fabricated data. Many people and institutions are negatively impacted by the widespread circulation of false information. As a result, detecting fake news via social media has emerged as a topic of interest for academics. Searching for and reading the news is becoming increasingly convenient as a result of the widespread availability, quick expansion, and widespread dissemination of traditional news outlets and social media. Nowadays, there is a plethora of information that can be found on social media, and it can be difficult to tell what is real and what is not. The distribution costs of releasing news via social media are inexpensive, and anyone can do it. The widespread circulation of false information could have devastating effects on both individuals and communities. Developing a reliable machine learning method for spotting fake news is the focus of this work.

Fake news detection on Twitter

Purpose Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter. Design/methodology/approach This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer. Findings This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem. Originality/value This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.

How Climate Movement Actors and News Media Frame Climate Change and Strike: Evidence from Analyzing Twitter and News Media Discourse from 2018 to 2021

Twitter enables an online public sphere for social movement actors, news organizations, and others to frame climate change and the climate movement. In this paper, we analyze five million English tweets posted from 2018 to 2021 demonstrating how peaks in Twitter activity relate to key events and how the framing of the climate strike discourse has evolved over the past three years. We also collected over 30,000 news articles from major news sources in English-speaking countries (Australia, Canada, United States, United Kingdom) to demonstrate how climate movement actors and media differ in their framing of this issue, attention to policy solutions, attribution of blame, and efforts to mobilize citizens to act on this issue. News outlets tend to report on global politicians’ (in)action toward climate policy, the consequences of climate change, and industry's response to the climate crisis. Differently, climate movement actors on Twitter advocate for political actions and policy changes as well as addressing the social justice issues surrounding climate change. We also revealed that conversations around the climate movement on Twitter are highly politicized, with a substantial number of tweets targeting politicians, partisans, and country actors. These findings contribute to our understanding of how people use social media to frame political issues and collective action, in comparison to the traditional mainstream news outlets.

Does social media keep me alarmed? The effects of expectations surrounding social media attributes and exposure to messages of social (in)stability on substitutive social media news use

abstract Social media news is pervasively replacing traditional news outlets in people’s news diets. From an active-audience perspective, this study employed a factorial preregistered experiment with a pretest and mediators to examine how (1) low versus high expectations about social media’s potential to obtain information in an efficient way and (2) exposure to messages that trigger perceptions of social (in)stability influence Chinese young adults’ intentions to substitute social media for other news sources. This study found that participants who believed that social media as a news source could reduce information costs were more willing to substitute social media for other news sources and that participants who were primed with messages of social instability were less willing to do so. The two effects were mediated separately through reliance on different social media attributes: proximal cues and recommendation systems. Arguably, both attributes can contribute to reducing perceived information costs. Exposure to messages of social (in)stability also moderates the effect between expectations regarding attributes and reliance on recommendation systems and has implications for Schudson’s “monitorial citizen” model in the social media age.

Knowledge of and preparedness for COVID-19 among Somali healthcare professionals: A cross-sectional study.

Somalia is considered severely underprepared to contain an outbreak of COVID-19, with critical shortages in healthcare personnel and treatment resources. In limited-resource settings such as Somalia, providing healthcare workers with adequate information on COVID-19 is crucial to improve patient outcomes and mitigate the spread of the SARS-CoV-2 virus. This study assessed the knowledge of, preparedness for, and perceptions toward COVID-19 prevention and treatment among Somali healthcare workers. A descriptive, cross-sectional survey was completed by 364 Somali healthcare workers in summer of 2020 utilizing a convenience sampling method. Participants' most accessed sources of COVID-19 information were from social media (64.8%), official government and international health organization websites (51.1%,), and traditional media sources such as radio, TV, and newspapers (48.1%). A majority of participants demonstrated strong knowledge of treatment of COVID-19, the severity of COVID-19, and the possible outcomes of COVID-19, but only 5 out of 10 symptoms listed were correctly identified by more than 75% of participants. Although participants indicated seeing a median number of 10 patients per week with COVID-19 related symptoms, access to essential medical resources, such as N95 masks (30.2%), facial protective shields (24.5%), and disposable gowns (21.4%), were limited. Moreover, 31.3% agreed that Somalia was in a good position to contain an emerging outbreak of COVID-19. In addition, 40.4% of participants agreed that the Somali government's response to the pandemic was sufficient to protect Somali healthcare professionals. This study provides evidence for the need to equip Somali healthcare providers with more information, personal protective equipment, and treatment resources such that they can safely and adequately care for COVID-19 patients and contain the spread of the virus. Social media and traditional news outlets may be effective outlets to communicate information regarding COVID-19 and the Somali government's response to frontline healthcare workers.

Determinants of Participatory Journalism and Communication for Development of the Contemporary Press: Perspective from the United States of America

Recent years have seen a dramatic change in the relationship between online media and the audience, and user generated content is a common feature of news websites in the United States of America. Control over the contents of the media space, once held by traditional news outlets, today is shared by bloggers and other social media participants. Participatory journalism’ has been anticipated as a form of journalism in which the audience no longer ‘merely’ receives news, but, enabled by digital technologies, is involved in its production and dissemination. First, participatory journalism and communication is considered in the context of the historical evolution of public communication. A methodological strategy for systematically analyzing citizen participation opportunities in the media is then proposed and applied. A sample of 16 online newspapers offers preliminary data that suggest news organizations are interpreting online user participation mainly as an opportunity for their readers to debate current events, while other stages of the news production process are closed to citizen involvement or controlled by professional journalists when participation is allowed. However, different strategies exist among the studied sample, and contextual factors should be considered in further research. Because of such pressing factors participatory journalism and communication is marginalized and genuine participation is the missing link in the development of the press process. To avert such trends, the paper calls for professionalism for the development of the press communication, structural change of the organization and holistic approach of development of the press for successful development of the press endeavors. Keywords: Participatory Journalism, Communication, Press development of the press, California, USA.

Case Study Report-Analysis of Hack by Triple J and Junkee

An increasing number of young people have lost their interests in browsing news and sharing their opinions on traditional news outlets, because they are stereotyped in most mainstream media organizations which also treat youth’s comments as without any authoritative. To correct the mainstream media's misrepresentation and ignorance of young people, some online media platforms have devoted to develop youth journalism and encourage their digital engagement. In this report, the author choose Hack by Triple J and Junkee, the representative youth media in Australia, as two cases to analyze the reasons why they are more popular among Aussie youth than conventional media outlets and how they Reinvigorate young people's political participation.

Polarized platforms? How partisanship shapes perceptions of “algorithmic news bias”

The use of artificial intelligence-based algorithms for the curation of news content by social media platforms like Facebook and Twitter has upended the gatekeeping role long held by traditional news outlets. This has caused some US policymakers to argue that platforms are skewing news diets against them, and such claims are beginning to take hold among some voters. In a nationally representative survey experiment, we explore whether traditional models of media bias perceptions extend to beliefs about algorithmic news bias. We find that partisan cues effectively shape individuals’ attitudes about algorithmic news bias but have asymmetrical effects. Specifically, whereas in-group directional partisan cues stimulate bias perceptions for members of both parties, Democrats, but not Republicans, also respond to out-group cues. We conclude with a discussion about the implications for the formation of attitudes about new technologies and the potential for polarization.

Not all undecided voters are alike: Evidence from an Israeli election

The literature dealing with undecided voters – a growing group of citizens in many democracies that can determine who wins in election campaigns – suggests two very different profiles. The first approach describes undecided voters as being generally uninformed about politics, while the second sees undecideds as sophisticated citizens who follow a campaign closely before making their final voting decision. The current study tries to make sense of this contrast, while examining differences between sophisticated and less sophisticated undecideds (their level of sophistication was based on their political interest and knowledge). Using two panel surveys, conducted before and after the April 2019 elections in Israel (N = 1427; N = 912), we examine a number of hypotheses about differences in terms of the undecided citizens' demographic backgrounds, how they search for political information during the election campaign, how they come to make their final decisions, and whether they ended up voting. The findings indicate that the typical sophisticated undecided voter is a citizen from a more privileged social background, exhibits greater trust in traditional media, consumes more news to follow the campaign (from various traditional news outlets and social media), is more likely to carry out online discussions about the elections, is more likely to base his or her decision on policy issues, is more likely to debate between parties within the same ideological camp (internal floater), and more likely to vote than less sophisticated undecided voters. Our typology, which makes a distinction between sophisticated and less sophisticated undecided voters, as well as these findings (and the comparison to the committed voters), can help political scientists and practitioners widen their understanding regarding this important group of voters in todays' complex political reality.

Digital public sphere and Palestine-Israel conflict: A conceptual analysis of news coverage

The news coverage of conflicts has transformed with the introduction of digital media and social media platforms. The available literature on media coverage of Palestine-Israel conflict is mainly focused on traditional news coverage or social media dimension of the conflict information. There exists a literature gap on social media coverage of Palestine-Israel conflict by the traditional news organizations. This study explores the changing pattern of traditional media’s coverage of the longstanding conflict in the wake of new communication technologies through appraisal of the existing literature. The analysis revealed that the traditional news organizations have adopted social networking platforms as a business model to cover the Palestine-Israel conflict. It was found that the traditional news outlets use the new media because it is cost-effective and have instantaneous outreach to maximum number of netizens. The new tools of communication offer multimedia platforms where conflict-related text, videos and images can be posted simultaneously. The study proposes to conduct future research on media-conflict relationship by focusing the use of new communication tools by the traditional media