News Content: from Event to Viewer

The purpose of the research is to analyze the way of news information from the event to the screen, to identify the extent to which the use of new technologies affects the audience, to determine the role of a journalist, an editor, a video editor and a sound engineer in this chain, to prove the importance of verifying information. The research methodology is based on a comprehensive theoretical analysis and descriptive-analytical approach, which combines methods of observation, comparison and generalization. The method of theoretical analysis of television stories, scientific publications, as well as determining the role of each employee of the channel, who works on the release of information on the air. Scientific novelty is a detailed analysis of news content on television, determining aspects of interdependence, efficiency and reliability of information when submitting news. Conclusions. Trends in the dynamic development of television have led to significant transformation processes and the use of the latest technologies to influence the audience in news content. The article analyzes the work with information at different levels, elaborates on each stage of news verification, summarizes news factors, approach to writing news stories.

Terrorism And Fake News Detection

Fake news dissemination is a critical issue in today’s fast-changing network environment. The issues of online fake news have attained an increasing eminence in the diffusion of shaping news stories online. This paper deals with the categorical cyber terrorism threats on social media and preventive approach to minimize their issues. Misleading or unreliable information in form of videos, posts, articles, URLs are extensively disseminated through popular social media platforms such as Facebook, Twitter, etc. As a result, editors and journalists are in need of new tools that can help them to pace up the verification process for the content that has been originated from social media. existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate. This study proposed a model that can accurately identify and classify deceptive news articles content infused on social media by malicious users. The news content, social-context features and the respective classification of reported news was extracted from the PHEME dataset using entropy-based feature selection. The selected features were normalized using Min-Max Normalization techniques. The model was simulated and its performance was evaluated by benchmarking with an existing model using detection accuracy, sensitivity, and precision as metrics. The result of the evaluation showed a higher 17.25% detection accuracy, 15.78% sensitivity, but lesser 0.2% precision than the existing model, Thus, the proposed model detects more fake news instances accurately based on news content and social content perspectives. This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.

Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm

The development of the Internet has completely changed the way of recommending and disseminating news content. Traditional media forms of news dissemination effectiveness evaluation methods are no longer fully suitable for the evolving needs of new media news dissemination. Therefore, it is necessary to innovate methods for evaluating the effects of new media news dissemination. This article mainly combines personalized recommendation algorithms to evaluate the effectiveness of news dissemination. Currently, popular personalized recommendation algorithms include content-based recommendation algorithms, collaborative filtering recommendation algorithms, knowledge-based recommendation algorithms, and associated recommendation algorithms. These recommendation algorithms are effective. This promotes the dissemination of news, which also recommends news content that is more relevant to user preferences for most users. In addition, the quality of news content is further evaluated through the news rating system, thereby effectively improving the quality of news content.

Fake News Detection in Aging During the Era of Infodemic

Increasing misinformation spread, including news about COVID-19, poses a threat to older adults but there is little empirical research on this population within the fake news literature. Embedded in the Changes in Integration for Social Decisions in Aging (CISDA) model, this study examined the role of (i) analytical reasoning; (ii) affect; and (iii) news consumption frequency, and their interplay with (iv) news content, in determining fake news detection in aging during the COVID-19 pandemic. Young (age range 18-35 years, M = 20.24, SD = 1.88) and older (age range 61-87 years, M = 70.51, SD = 5.88) adults were randomly assigned to view COVID or non-COVID news articles, followed by measures of analytical reasoning, affect, and news consumption frequency. Comparable across young and older adults, fake news detection accuracy was higher for news unrelated to COVID, and non-COVID fake news detection was predicted by individual differences in analytic reasoning. Examination of chronological age effects further revealed that detection of fake news among older adults aged over 70 years depended on interactions between individual CISDA components and news content. Collectively, these findings suggest that age-related susceptibility to fake news may only be apparent in later stages of older adulthood, but vulnerabilities are context dependent. Our findings advance understanding of psychological mechanisms in fake news evaluation and empirically support CISDA in its application to fake news detection in aging.

An understanding of Nigerian television mobile news applications and associate news locations

Mobile applications are already part of the contemporary news experience, which increasingly includes the convergent mobile application news content of conventional media, such as television stations. Impressively, the experience of such convergences has spread to include developing nations, including Nigeria. However, despite the enormous digital device and media penetrations in the country, little research has been done in order to understand the nature of such television stations’ mobile application news. With regards to the foregoing, and the importance of associated news locations, this content analysis research has been conducted and has found serious correlation between ownership type and television application news content. While Africa is the dominant proximate news locations of the prominent Nigerian television mobile application news studied, the news spread categories are largely similar across all mobile application news content.

Pengembangan Sistem Pemantauan Sentimen Berita Berbahasa Indonesia Berdasarkan Konten dengan Long-Short-Term Memory

<p class="Abstrak">Kementerian Komunikasi dan Informatika (Kemkominfo) memiliki tugas salah satunya untuk mengawasi konten berita yang beredar di media digital. Dengan terus bertambahnya berita online di internet, Kemkominfo dihadapkan pada permasalahan pengklasifikasian sentimen berita yang masih dilakukan secara manual dengan membaca konten berita satu persatu lalu menangkap sentimen dari berita, yaitu sentimen positif, negatif, atau netral. Hal ini sangat melelahkan dan memakan waktu mengingat volume dan kecepatan pertumbuhan berita setiap harinya semakin masif. Untuk itu diperlukan pengembangan sistem pengklasifikasi sentimen berita daring secara otomatis untuk pemantauan berita berbahasa Indonesia. Sistem pengklasifikasi secara otomatis berbasis <em>machine learning</em> dilakukan dengan membangun model pembelajaran dari korpus berita yang berasal dari situs berita daring. Korpus data tersebut kemudian diproses menggunakan algoritma <em>Long Short-Term Memory (</em>LSTM). LSTM biasa digunakan untuk menangani kasus klasifikasi dalam berbagai bidang khususnya dengan input berupa teks sekuensial. Model LSTM diimplementasikan ke dalam aplikasi berbasis web untuk menentukan jenis dari sentimen berita. Berdasarkan hasil pengujian yang dilakukan, model LSTM yang dibuat memiliki tingkat akurasi sebesar 86%. Dengan demikian implementasi LSTM mampu menjadi suatu solusi untuk mengatasi masalah pengklasifikasian sentimen berita daring secara otomatis untuk pemantauan sentimen berita di Kemkominfo.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Asbtract</strong></em></p><p class="Judul2"><em>The Ministry of Communication and Informatics (Kemkominfo) has one duty to monitor news content circulating in digital media. With the increasing number of online news in the internet, Kemkominfo is facing the problem of classifying news </em><em>sentiment </em><em>which is still done manually by reading the contents of the news one by one</em><em>, and then capturing the sentiment of the news; either positive, negative, or neutral</em><em>. This is very exhausting and time consuming considering the volume and speed of growth of news every day is getting massive. This requires the development of an automatic </em><em>online</em><em> news </em><em>sentiment </em><em>classification system for monitoring Indonesian news. Machine learning-based automatic classification systems are carried out by building a learning model from a news corpus originating from news sites. The data is then processed using the Long Short Term Memory (LSTM) algorithm. LSTM is commonly used to handle classification in various fields</em><em> especially in a sequential input</em><em>. The LSTM model is implemented into a web-based application to determine the </em><em>types of news sentiment</em><em>. Based on the results of the tests carried out, the LSTM model created has an accuracy rate of 86%. Thus, the implementation of LSTM is potentially become a solution to overcome the problem of automatic online news</em><em> sentiment</em><em> classification for the news content monitoring system at the Ministry of Communication and Information.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>