News Content Research Articles

News Coping and Resistance: An Examination of Entertainment as Self-Care in the Digital Black Press

ABSTRACT Scholarship on the mainstream press in the United States has argued that in the new media age, consumers prefer entertainment while journalists prefer hard news—yet digital Black press journalists and readers value both types of content. In the Black press, entertainment is produced and consumed as an alternative to mainstream media's often unfavorable hard news coverage of African Americans. The race-based trauma that African Americans experience through news consumption can negatively impact mental health and cause news avoidance, but some are resisting this by creating spaces for Black joy online. This research uses interviews with journalists and focus groups with readers to investigate the utility of entertainment news content in the Black press. I find that entertainment in the digital Black press contributes to positive and comprehensive coverage of the African American community, gives consumers a means to cope with or protect themselves from reading traumatic news coverage on topics such as Black Lives Matter, and can act as a form of self-care. I conclude that digital Black press entertainment content can counteract communal stress for African Americans who may experience high levels of news-related anxiety.

Right Topic, Right Source? Source Diversity and Balance in Right-Wing Alternative News Content Across Topics

ABSTRACT This article investigates how the hybrid nature of right-wing alternative news media striving for journalistic legitimacy and partisan credibility plays out on source and topical diversity and balance in article content. The article draws on a sample of 1000 randomly selected articles published by 20 right-wing alternative online news media from six countries (the US, the UK, Germany, Austria, Denmark, and Sweden) from March 2019 to February 2020 (i.e., in “routine” pre-COVID-19 times). The results show that most of the alternative media outlets in the sample cover relatively broad topical spectra. More specifically, US and UK media primarily focus on politics and policy issues, whereas Scandinavian media are more heavily geared toward societal issues and crime coverage. Overall, right-wing alternative news content is characterized by a variety of partisan and non-partisan sources. However, core partisan topic areas, such as politics and mass media, are more likely to include partisan and especially right-wing sources. Often, with respect to these topics, right-wing sources are evaluated positively, and left-wing sources are evaluated negatively. Finally, right-wing and non-right-wing sources often appear in separate articles rather than in direct confrontation.

Focusing on fake news’ contents: The association between ingroup identification, perceived outgroup threat, analytical‐intuitive thinking and detecting fake news

AbstractThis study aims to reveal the fake news content in the context of the social identity approach and to examine the mediating role of perceived outgroup on the association between ingroup identification and detecting fake news blaming ingroup, outgroup, or fictional groups. Study 1 found that fake news could be gathered under six themes: contacted‐outgroup blaming, represented‐outgroup blaming, outgroup derogation, outgroup appreciation, ingroup glorification, and phantom‐mastermind blaming. In preregistered Study 2 with representative non‐weird participants (N = 216), we examined the mediating role of perceived outgroup threat on the association between ingroup identification and detecting fake news revealed in Study 1. Perceived outgroup threat was only mediating for detecting outgroup‐blaming fake news when intuitive and analytical thinking styles were controlled. Detecting ingroup‐blaming fake news was associated with ingroup identification. Analytical thinking predicted only detecting phantom‐mastermind‐blaming fake news. Findings demonstrated that the contents of fake news play a vital role in detecting them, and variables pointing to content (i.e., ingroup identification for ingroup‐blaming fake news, and perceived outgroup threat for outgroup‐blaming fake news) are predictive for detecting fake news.

Fake news and the political economy of the media: A perspective of Ghanaian journalists

The enduring rate at which fake news is disrupting the political economy of the media is the subject of this study. Using a largely qualitative instrument of intensive interviews with 18 journalists purposively selected for the study, we discovered that fake news is one of the banes of journalism in Ghana. Fake news's notorious role in the political and economic realities of the country is not uncommon, as it has created uncountable political controversies. With several of the country's media owned by politicians or persons with a vested interest in politics, it became a practice for players to utilise fake news as a propaganda tool to outwit their competitors through the media. For these architects to fully achieve their goal, social media and affiliated mainstream media were used as instruments to propagate fabricated content against their opponents, especially during the 2020 general elections. This paper also identified the use of artificial intelligence and robotics in the creation of fake news content. The study recommends the need for media organisations in the country to seek the services of fact-checkers as a means of verifying stories, as well as to intensify education on the identification of fake news and possible flagging.

TChecker: A Content Enrichment Approach for Fake News Detection on Social Media

The spread of fake news on social media continues to be one of the main challenges facing internet users, prohibiting them from discerning authentic from fabricated pieces of information. Hence, identifying the veracity of the content in social posts becomes an important challenge, especially with more people continuing to use social media as their main channel for news consumption. Although a number of machine learning models were proposed in the literature to tackle this challenge, the majority rely on the textual content of the post to identify its veracity, which poses a limitation to the performance of such models, especially on platforms where the content of the users’ post is limited (e.g., Twitter, where each post is limited to 140 characters). In this paper, we propose a deep-learning approach for tackling the fake news detection problem that incorporates the content of both the social post and the associated news article as well as the context of the social post, coined TChecker. Throughout the experiments, we use the benchmark dataset FakeNewsNet to illustrate that our proposed model (TChecker) is able to achieve higher performance across all metrics against a number of baseline models that utilize the social content only as well as models combining both social and news content.

MITOS ILUSTRASI FOTO FERDI SAMBO DALAM PEMBERITAAN MEDIA ONLINE

In today's information technology era, modern society is greatly spoiled by the ease of access to news. One of the information media is online media reporting, which offers news content in written form as well as color photo illustrations, making it easier for readers to understand the content of the news. However, photo illustrations as news content can sometimes be confusing, such as in the reporting of the premeditated murder committed by Ferdi Sambo. Therefore, it is important to deeply understand the meaning of the photo illustrations from the news content about Ferdi Sambo so as not to be trapped in a superficial interpretation of a news story. The interpretation of Ferdi Sambo's photo illustrations in online media uses a qualitative methodology with a semiotic approach by Roland Barthes regarding denotation and connotation meanings. The results of the analysis conducted on the photo illustrations of Ferdi Sambo in online media reporting indicate that some objectives and interests can be concluded as resistance or defense conducted through body gestures and fashion to address the case being faced.

Linguistic peculiarities of fake publications about the COVID-19 pandemic (on the example of Russian and English-language new media)

The aim of the study is to identify the linguistic features of fake publications about the COVID-19 pandemic on the example of Russian and English-language new media in order for a modern person to be able to recognize such types of news among the published information content. The scientific novelty of the study lies in the identification and study of similarities and differences in the use of the language of fake news about COVID-19 in Russian and English languages. The work included a linguistic assessment of the ability of authors of fake publications in Russian and English-language new media to operate a variety of stylistic techniques in order to expand the emotional range of published media materials, thereby contributing to a stronger emotional impact on the audience of readers (viewers, listeners). As a result of the study, it was found that Russian and English-language fake news about the pandemic have a number of linguistic patterns, knowing which one can predict the truth or falsity of news content of this type in the information space and, accordingly, prevent its dissemination. In particular, such principles include the use of imperative constructions, stylistic means of expression in news texts (e.g., metaphors); the presence of a trigger in informational headlines, as well as the generation of short text messages accompanying memes that can influence the affective actions of media audiences in their interaction with Russian and English-language media resources. Further prospects for the study of fake news texts about COVID-19 and epidemics in general are identified to explore the phenomenon of disinformation and post-truth and their impact on media reality.

Social Networking and Misinformation Challenges: Moroccan Students in Tertirary Education as a Case Study

This paper focuses on examining how Moroccan university students approach the various social networking sites, including their self-perceived capacities to critically analyze and evaluate digital content in general and online news and information more specifically. The paper poses several research questions that all aim to investigate the issue under examination. This empirical endeavor used the survey as a research instrument to gather data, and presented a number of conclusions and recommendations for the Moroccan policy makers, stakeholders, and all concerned parties. The study further reported that most respondents advance by critically evaluating social media content and largely deploying various efficient verification measures and techniques. In the same vein, the majority of respondents highly rated their digital media perceived self-efficacy. Almost two thirds of the participants postulate that higher educational institutions should incorporate digital literacy skills in their curricula and syllabi.

Bullying is Never Ending: Construction of Reporting on Cases of Bullying in Schools

The case of bullying that occurred at SMPN 16 Malang and affected MS became a topic of discussion in various media, including online media. In reporting a case, media is a reality construct. A number of pros and cons become interesting perspectives raised in the media, including radarmalang.jawapos.com. This study aims to find out how the reporting construction is on news content related to the bullying case that occurred on MS student at SMP 16 Surabaya using the framing analysis method according to Zhongdan Pan & Konsicki. The research results mention that the news construction made by radarmalang.jawapos.com tends to take the side of the government and related institutions and the media tends to bully the school. This is proven by the five news analyzed, none of which highlights the clarification or defense from the school. Radar Malang tends to raise topics that blame the school and the authorities (government, education, culture and arts department, KPAI) framed as if washing their hands of this case. The images selected to accompany the journalism news also tend to put the school in a corner. Radar Malang also tends to overly protect the perpetrators of bullying acts with more subtle diction choices.

Analisis Framing pada Pemberitaan Kasus Penipuan Trading Online Indra Kenz di Kompas.com dan Detik.com

This research discusses two quite large media, namely Kompas.com and Detik.com, regarding the choice of point of view and framing of news regarding the suspect in the online trading fraud case committed by Indra Kenz. Researchers used two news samples each in the media Kompas.com and Detik.com which were presented on February 25 2022. This research discusses framing analysis, the aim of the research is to understand the framing in the reporting of the Indra Kenz online trading fraud case in Kompas. com and detik.com, using the framing analysis model from Pan & Kosicki. The research approach used is qualitative with Pan & Kosicki framing analysis, where the data used for this research is primary data with non-participant observation techniques obtained from the news portals Kompas.com and Detik.com. The research results obtained show that there are differences in the framing of news carried out by the online media Kompas.com and Detik.com. If you look at the news content on kompas.com and detik.com, they have their own ways of constructing news that is presented to the reading public.

NSEP: Early fake news detection via news semantic environment perception

The abundance of heavy data on social media enables users to share opinions freely, leading to the rapid spread of misleading content. However, existing fake news detection methods exaggerate the influence of public opinions, making it challenging to combat misinformation since its early spreading state. To tackle this issue, we propose a novel fake news detection framework through news semantic environment perception (NSEP) to identify fake news content. The NSEP framework consists of three major steps. First, NSEP divides the news semantic environment with time-constrained intervals into macro and micro semantic environments using an in-depth distinguisher module. Second, graph convolutional networks are applied to perceive the semantic inconsistencies between intrinsic news content and extrinsic post tokens in the macro semantic environment. Third, a micro semantic detection module guided by multihead attention and sparse attention is utilized to capture the semantic contradictions between news content and posts in the micro semantic environment, providing explicit evidence for determining the authenticity of fake news candidates. Empirical experiments conducted on real-world Chinese and English datasets show that the NSEP framework on Chinese datasets achieved as high as 86.8% accuracy, performing at most 14.1% higher accuracy than that of other state-of-the-art baseline methods and confirming that detecting news content through both micro and macro semantic environments is an effective methodology for alleviating early propagation of fake news. The findings also comprehensively indicate that both news items and posts are critical for the early debunking of fake news and in theories concerning information science.

TRAINING ON BEACH TOURISM THROUGH SOCIAL MEDIA: CITIZEN JOURNALISM NEWS CONTENT PRODUCTION IN SAWARNA

In the information age like today, we are presented with a lot of news, both from within the country and abroad. Now, in the information age, we get news apart from the media but also through the internet. The rapid development of communication and information technology has produced a variety of information and communication technology equipment that makes it easier for us to access news easily. Now, we can get a variety of news through mass media, social media, news portals, and others. In producing news broadcasts, it is necessary to first understand what news value is. Because the viewers should not be treated to news that is not at all useful as information. This is necessary to find out what information they need. The bottom line: must first examine and examine what events or incidents should be made as news. There are many interesting news materials to be presented to the public, one of which is the tourism midwife. Indonesia is one of the 204 countries in the world. Indonesia is a maritime country, which is a country that has many islands. This causes Indonesia to be rich in natural resources. These abundant natural resources consist of biological, energy, material, space and many other kinds. Because of the potential in these natural resources. Indonesia is a maritime country, which is a country that has many islands. This causes Indonesia to be rich in natural resources. These abundant natural resources consist of biological, energy, material, space and many other kinds. Because of the potential in these natural resources. One of the interesting destinations to be covered as news material is Sawarna. From the results of the training, participants need ongoing training to create positive news content by promoting interesting tourism in Sawarna on social media through citizen journalism, but many participants are satisfied with what the presenters are presenting and get a basic overview of the practice process of producing news content in Sawarna on social media. through citizen journalism. In addition to doing role playing, the presenters provide games or games by asking questions about the material that has been presented.

Ethical and safety considerations in automated fake news detection

ABSTRACT This paper highlights ethical issues in automated fake news detection and calls for caution when deploying tools to automatically detect mis/disinformation in real-life settings. We argue that the potential harm to information consumers caused by an automated tool making a mistake requires us to better understand the mistakes that can be made. We implement three proposed detection models from the literature that were trained on over 381,000 news articles published over six months. We test each of these models using a test dataset constructed from over 140,000 news articles published a month after each model’s training data. Articles in the test dataset could come from any outlet, no matter if that outlet was labelled during training or never used during training. We used these data to explore and understand two specific problems with algorithmic fake news detection, namely Bias and Generalisability. These problems arise from the models’ training, design, and the inherent unpredictability of news content. Based on our analysis, we discuss the importance of understanding how ground truth is determined, how operationalisation may perpetuate bias, and how the simplification of models may impact the validity of predictions. We offer avenues for future research.

Automated Detection of Persuasive Content in Electronic News

Persuasive content in online news contains elements that aim to persuade its readers and may not necessarily include factual information. Since a news article only has some sentences that indicate persuasiveness, it would be quite challenging to differentiate news with or without the persuasive content. Recognizing persuasive sentences with a text summarization and classification approach is important to understand persuasive messages effectively. Text summarization identifies arguments and key points, while classification separates persuasive sentences based on the linguistic and semantic features used. Our proposed architecture includes text summarization approaches to shorten sentences without persuasive content and then using classifiers model to detect those with persuasive indication. In this paper, we compare the performance of latent semantic analysis (LSA) and TextRank in text summarization methods, the latter of which has outperformed in all trials, and also two classifiers of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). We have prepared a dataset (±1700 data and manually persuasiveness-labeled) consisting of news articles written in the Indonesian language collected from a nationwide electronic news portal. Comparative studies in our experimental results show that the TextRank–BERT–BiLSTM model achieved the highest accuracy of 95% in detecting persuasive news. The text summarization methods were able to generate detailed and precise summaries of the news articles and the deep learning models were able to effectively differentiate between persuasive news and real news.

Hindu-Nationalism and Media: Anti-Press Sentiments by Right-Wing Media in India

This monograph investigates the expression of anti-media populist sentiments by right-wing news media in India and the socio-technological, historical, economic, legal, and political factors that aid right-wing efforts to delegitimize professional journalism. Thematic analysis of 545 articles from Swarajya.com and OpIndia.com, a qualitative content analysis of Republic TV debates, and interviews with 24 Hindu nationalists on Twitter reveal the discursive strategies they use to discredit journalism. Themes in media criticism include claims that the mainstream media are corrupt and partisan, suppress Hindu voices, favor Muslim and Christian minorities, undermine national interests, and spread false narratives. These expressions aim to undermine journalists’ credibility and position right-wing actors as challengers to the established media’s hegemony. Meanwhile, the Modi government employs coercive measures to co-opt the media into providing favorable coverage to the BJP. These endeavors cause journalists to self-censor to safeguard personal safety and livelihood. Consequently, mainstream news content is increasingly homogeneous, eroding diverse perspectives. This shift weakens the media’s watchdog role and shrinks the space for open debate and public deliberation in India.

How does extreme weather impact the climate change discourse? Insights from the Twitter discussion on hurricanes

The public understanding of climate change plays a critical role in translating climate science into climate action. In the public discourse, climate impacts are often discussed in the context of extreme weather events. Here, we analyse 65 million Twitter posts and 240 thousand news media articles related to 18 major hurricanes from 2010 to 2022 to clarify how hurricanes impact the public discussion around climate change. First, we analyse news content and show that climate change is the most prominent non hurricane-specific topic discussed by the news media in relation to hurricanes. Second, we perform a comparative analysis between reliable and questionable news media outlets, finding that unreliable outlets frequently refer to climate-related conspiracies and preferentially use the term “global warming” over “climate change”. Finally, using geolocated data, we show that accounts in regions affected by hurricanes discuss climate change at a significantly higher rate than accounts in unaffected areas, with references to climate change increasing by, on average, 80% after impact, and up to 200% for the largest hurricanes. Our findings demonstrate how hurricanes have a key impact on the public awareness of climate change.

A comprehensive survey on machine learning approaches for fake news detection

AbstractThe proliferation of fake news on social media platforms poses significant challenges to society and individuals, leading to negative impacts. As the tactics employed by purveyors of fake news continue to evolve, there is an urgent need for automatic fake news detection (FND) to mitigate its adverse social consequences. Machine learning (ML) and deep learning (DL) techniques have emerged as promising approaches for characterising and identifying fake news content. This paper presents an extensive review of previous studies aiming to understand and combat the dissemination of fake news. The review begins by exploring the definitions of fake news proposed in the literature and delves into related terms and psychological and scientific theories that shed light on why people believe and disseminate fake news. Subsequently, advanced ML and DL techniques for FND are dicussed in detail, focusing on three main feature categories: content-based, context-based, and hybrid-based features. Additionally, the review summarises the characteristics of fake news, commonly used datasets, and the methodologies employed in existing studies. Furthermore, the review identifies the challenges current FND studies encounter and highlights areas that require further investigation in future research. By offering a comprehensive overview of the field, this survey aims to serve as a guide for researchers working on FND, providing valuable insights for developing effective FND mechanisms in the era of technological advancements.

News Recommendation Systems: Impediments and Future Prospects

News publishers have reduced their usage of traditional newspapers in favour of digital various options such as webpages and specially constructed cell phone applications. In the present world, the internet has recreated information borders and, in addition to smartphone internet access, makes it the most vital medium content for everyone on earth. Users want personalized news recommendations in order to locate relevant news content and avoid information overload. With the growing number of media reports available on social media, tailored news suggestions have become more vital in assisting end users inside discovering relevant and engaging news stories. Traditional recommender systems, on the other hand, frequently fail to account for the constantly changing nature the users' preferences in addition to shifting patterns in news items. To solve this issue, this study offers a context-aware customized data system of recommendations that utilizes contextual information to improve news suggestion individualism. The method entails gathering, extracting, investigating, scrubbing, and arranging a big dataset from 22,657 English publications from 19 individual news outlets online. Four separate recommender frameworks were thought up using different methodologies, including content-based strategies like the TF-IDF Bag-of-Words, and Word2Vec, as well as a related not entirely set in stone by click demeanours. Since it’s been thoroughly explored over decades with noteworthy progress in enhancing user interaction, there encore many concerns and obstacles waiting to be researched further.

The use of thematic context-based deep learning in discourse expression of sports news

Sports news is a type of discourse that is characterized by a specific vocabulary, style, and tone, and it is typically focused on conveying information about sporting events, athletes, and teams. Thematic context-based deep learning is a powerful approach that can be used to analyze and interpret various forms of natural language, including the discourse expression of sports news. An application model of sign language and lip language recognition based on deep learning is proposed to facilitate people with hearing impairment to easily obtain sports news content. First, the lip language recognition system is constructed; next, MobileNet lightweight network combined with Long-Short Term Memory (LSTM) is used to extract lip reading features. ResNet-50 residual network structure isadopted to extract the features of sign language; finally, the convergence, accuracy, precision and recall of the model are verified respectively. The results show that the loss of training set and test set converges gradually with the increase of iteration times; the lip language recognition model and the sign language recognition model basically tend to be stable after 14 iterations and 12 iterations, respectively, suggesting a better convergence effect of sign language recognition. The accuracy of sign language recognition and lip language recognition is 98.9% and 87.7%, respectively. In sign language recognition, the recognition accuracy of numbers 1, 2, 4, 6 and 8 can reach 100%. In lip language recognition, the recognition accuracy of numbers 2, 3 and 9 is relatively higher. This exploration can facilitate hearing-impaired people to quickly obtain the relevant content in sports news videos, and also provide help for their communication.

Problematising Simplicity: Addressing Comprehension Gaps of RFI’s ‘Le Journal en Français Facile’

This paper evaluates Malawian learners’ French language comprehension abilities concerning the French radio program ‘Le Journal en français facile’ (The News in Simple French), broadcast by Radio France Internationale. Specifically, it assesses learners’ levels of aural comprehension of this Francophone news content designed for non-native audiences. The paper examines whether the universal applicability of the ‘français facile’ (simple French) concept across heterogeneous learners can be assumed, considering linguistic aspects deemed elementary from a Francophone perspective may not prove equivalently rudimentary for anglophone learners of French. Methodologically, the paper is grounded in the Bottom-up and Top-down listening comprehension model, which emphasises integrating linguistic and prior sociocultural knowledge to facilitate aural meaningmaking. Through French listening exercises and teacher interviews with a sample of Malawian college students, the study highlights limitations in comprehension, with listeners restricted to grasping essential lexical news components like numbers, names and locations while failing to parse complex news concepts —attributed to newscaster accent, pronunciation, speech rate, unfamiliar vocabulary, and insufficient prior knowledge activation. This paper argues the notion of “simple” French may be misconstrued when applied uniformly across diverse global learners engaging with RFI broadcasts, evidenced by difficulty comprehending the news program among numerous Malawian learners despite its simplified linguistic profile. Mastery of these broadcasts, it is contended, necessitates more advanced proficiency.