News Information Research Articles

Tantangan Etika dalam Jurnalisme Digital: Studi Kasus Peran Media Sosial dalam Penyebaran Berita

This research aims to describe the ethical challenges of digital journalism, regarding the role of social media in news dissemination. This research is a qualitative descriptive study. The research results show that the ethical challenges of digital journalism can be seen from the development of media for reporting, starting from print media, mass media, and the current era of digital media where everyone can easily access information (reporting) from various social media today. The ethics of digital journalism can be seen from the non objective reporting that online journalism conveys news instantly and superficially. In fact, this challenge is the news that is conveyed now, it is difficult to know the facts of the news that is uploaded on various social media. One of them is fake news (hoax) which occurs due to a lack of reporting accuracy and journalists' instantaneousness in conveying news/information. The issue of accuracy is now a dilemma for online media which is pursuing speed in publishing information in real time, demanding it be fast but at the same time demanding it be accurate. These many demands often give rise to new problems. Not a few online media take shortcuts, namely by prioritizing speed over accuracy. The practice of self-plagiarism is widespread by repeating sentences in previous reports to recycle them as if they were new news. In certain cases, real-time online media began to emerge which reported quickly on an event, even though he and his team never went to the field to collect the news. Then create news titles or information that are clickbait (excessive titles or titles that sometimes do not match the existing news). These are the challenges faced by journalists in the advancement of technology and social media, sometimes resulting in news producers or readers consuming practical news that lacks accuracy.

Aplikasi Laporan Berita Jurnalis Pada Surat Kabar Harian Sumut Pos Medan Menggunakan Kodular

Various new print media have emerged in Indonesia with the same aim of providing complete information to the public. Current technology makes it easy for the journalism sector to use it as a means of conveying news information easily. Journalists as news content producers. Recording each member's journalistic activities is one of the journalist's duties in making news. Journalistic activity records are one of the main materials in consideration for making news. There are several stages of the waterfall model, namely: Requirements analysis, system design, coding, application testing, program implementation. The purpose of data analysis is to make it easier to design information in a system. There is some data used in creating an information system for journalists' daily reports in the North Sumatra Post daily newspaper, such as data analysis. This application can also make it easier for the North Sumatra Medan Post Office to monitor and manage journalists' news data. Equipped with a login application, security can be guaranteed from use by unauthorized people

Optimization of news dissemination push mode by intelligent edge computing technology for deep learning

The Internet era is an era of information explosion. By 2022, the global Internet users have reached more than 4 billion, and the social media users have exceeded 3 billion. People face a lot of news content every day, and it is almost impossible to get interesting information by browsing all the news content. Under this background, personalized news recommendation technology has been widely used, but it still needs to be further optimized and improved. In order to better push the news content of interest to different readers, users' satisfaction with major news websites should be further improved. This study proposes a new recommendation algorithm based on deep learning and reinforcement learning. Firstly, the RL algorithm is introduced based on deep learning. Deep learning is excellent in processing large-scale data and complex pattern recognition, but it often faces the challenge of low sample efficiency when it comes to complex decision-making and sequential tasks. While reinforcement learning (RL) emphasizes learning optimization strategies through continuous trial and error through interactive learning with the environment. Compared with deep learning, RL is more suitable for scenes that need long-term decision-making and trial-and-error learning. By feeding back the reward signal of the action, the system can better adapt to the unknown environment and complex tasks, which makes up for the relative shortcomings of deep learning in these aspects. A scenario is applied to an action to solve the sequential decision problem in the news dissemination process. In order to enable the news recommendation system to consider the dynamic changes in users' interest in news content, the Deep Deterministic Policy Gradient algorithm is applied to the news recommendation scenario. Opposing learning complements and combines Deep Q-network with the strategic network. On the basis of fully summarizing and thinking, this paper puts forward the mode of intelligent news dissemination and push. The push process of news communication information based on edge computing technology is proposed. Finally, based on Area Under Curve a Q-Leaning Area Under Curve for RL models is proposed. This indicator can measure the strengths and weaknesses of RL models efficiently and facilitates comparing models and evaluating offline experiments. The results show that the DDPG algorithm improves the click-through rate by 2.586% compared with the conventional recommendation algorithm. It shows that the algorithm designed in this paper has more obvious advantages in accurate recommendation by users. This paper effectively improves the efficiency of news dissemination by optimizing the push mode of intelligent news dissemination. In addition, the paper also deeply studies the innovative application of intelligent edge technology in news communication, which brings new ideas and practices to promote the development of news communication methods. Optimizing the push mode of intelligent news dissemination not only improves the user experience, but also provides strong support for the application of intelligent edge technology in this field, which has important practical application prospects.

Examining belief adjusment model and framing effect on the audit materiality level decision making

The purpose of this study is to examine whether or not there is a difference in the decision-making of external auditors in determining the level of audit materiality between participants who get good news followed by bad news and those who get bad news information sequences (bad news) followed by good news (good news) with a step-by-step and end-of-sequence information presentation pattern in the positive frame or negative frame. The research method used in the research is the mixed design experiment method (between and within the subject) which manipulates the independent variables of the order of evidence (good news followed by bad news and bad news followed by good news) and framing effect (positive frame or negative frame) in the presentation pattern Step by Step and End of Sequence. Participants in this study were 150 students of the Bachelor of Accounting study program at Hayam Wuruk Perbanas Surabaya University with 300 experiment data. This study uses the normality test and the Kruskall-Wallis H test. The results of this study indicate that the Step-by-step presentation pattern can cause a recency effect when receiving information with a sequence of evidence of good news followed by bad news and bad news followed by good news both in the series, with the positive or negative frame, and the results obtained if the information is presented with an End of Sequence presentation pattern with information and a sequence of evidence of good news followed by bad news or bad news followed by good news there is no difference (no order effect) either in the framing effect (positive frame and negative frame).

Victim blaming and character assassination: Media framing of controversial issues of Bangladesh

News framing plays a significant role in creating and diverting perceptions on controversial issues in Bangladesh. The study examines three selected occurrences involving renowned personalities to discover the factors that contribute to the sensationalization of news information. The research posits theme-based criteria that influence news framing and investigates how controversy spreads via conventional and internet media. According to the study, online news portals participate in more news framing than conventional media. Victim blaming and character assassination are frequent framing strategies utilized in the online domain. The framing factors include misleading headlines, improper wording, and distortion of context. Thematic analysis of the study finds nine variables responsible for framing disagreement in the media, three of which constitute a basic pattern for framing disputes. The study emphasizes the possibility of news framing interfering with an individual's personal and professional life, as well as the importance of responsible journalism that promotes factual reporting while avoiding framing elements that can lead to victim blaming, character assassination, and sensationalism. Overall, the study adds to the current literature on news framing by offering insights into the thematic factors that can greatly influence framing debate. The study's findings might be useful for media practitioners and policymakers in Bangladesh and abroad since they give a better knowledge of the mechanics of framing disputes in the media. The report also recommends more research on news framing and its influence on public perceptions and attitudes toward contentious subjects.

An efficient fake news classification model based on ensemble deep learning techniques

The availability and expansion of social media has made it difficult to distinguish between fake and real news. Information falsification has exponentially increased as a result of how simple it is to spread information through sharing. Social media dependability is also under jeopardy due to the extensive dissemination of false information. Therefore, it has become a research problem to automatically validate information, specifically source, content, and publisher, to identify it as true or false. Despite its limitations, machine learning (ML) has been crucial in the categorization of information. Previous studies suggested three-step methods for categorising false information on social media. In the first step of the process, the data set is subjected to a number of pre-processing processes in order to transform unstructured data sets into structured data sets. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by WOA (Whale Optimization Algorithm) for weight value to tune the classification part. Finally, a Hybrid Classification model that is hybrid with a fuzzy based Convolutional Neural Network and kernel based support vector machine is constructed in order to identify the data pertaining to bogus news. However using single classifier for fake news detection produces the insufficient accuracy. To overcome this issue in this work introduced an improved model for fake news classification. To turn unstructured data sets into structured data sets, a variety of pre-processing operations are used on the data set in the initial phase of the procedure. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by COA (Coati Optimization Algorithm) for weight value to tune the classification part. Finally, an ensemble of RNN (Recurrent Neural Networks), VGG-16 and ResNet50.A classification model was developed to recognise bogus news information. Evaluate each fake news analysis' performance in terms of accuracy, precision, recall, and F1 score. The suggested model, out of all the methodologies taken into consideration in this study, provides the highest outcomes, according to experimental findings

A Mediating Effect of News Information on the Driver of Green Investment and Herding Behaviour in China's A-Share Market

In recent years, the surge in interest and investment towards environmentally sustainable initiatives, particularly in emerging economies like China, has garnered significant attention. This paper investigates the intricate dynamics between green investment drivers, herd behaviour, and the mediating role of news information in China's A-share market. Drawing upon a comprehensive dataset spanning the recent years, this study employs advanced statistical techniques, including structural equation modelling, to analyse the relationships between the aforementioned variables. Firstly, it examines the primary drivers behind green investment in China, considering factors such as regulatory policies, corporate social responsibility initiatives, and market demand for sustainable products and services. Subsequently, the paper delves into the phenomenon of herd behaviour within the A-share market, exploring how investor decisions are influenced by the actions of others rather than fundamental market indicators. This aspect is particularly pertinent in the context of green investment, where social proof and conformity may play a significant role in shaping investment decisions. Moreover, the study elucidates the mediating effect of news information in this relationship. News media serves as a critical intermediary, disseminating information about regulatory changes, corporate sustainability initiatives, and market trends, which in turn influence investor sentiment and decision-making processes. By uncovering the mediating role of news information, this research provides valuable insights into the mechanisms driving green investment and herd behaviour in China's A-share market. Understanding these dynamics is crucial for policymakers, market participants, and sustainability advocates alike, as they seek to promote environmentally responsible investment practices and mitigate the adverse effects of herd behaviour on market stability and efficiency. Overall, this paper contributes to the growing body of literature on sustainable finance and behavioural economics by shedding light on the complex interplay between green investment drivers, herd behaviour, and the role of news information in shaping investor decisions in China's rapidly evolving financial landscape.

Open Science News Informing System for Scientific Libraries

The purpose of the study is to theoretically and practically substantiate the need to introduce an online news information system into library practice as part of the information support of scientific activities (on the example of Open Science). The work theoretically substantiates that improving the news information system in libraries is necessary in connection with the development of technology, open science, library activities and the rapid growth of disparate sources of information. The experience analysis of 130 libraries in presenting various news information products has led to the conclusion that the implementation of news information requires appropriate technology that would allow us to model the sequence of processes and operations for generating news library and information products.As a result the model of a news information system on open science is developed, which allows searching, collecting, processing, and synthesizing news information on open science, as well as creating, posting and distributing information products, their qualitative and quantitative assessment of effectiveness to be made. It has been implemented on the web platform of the State Public Library for Science and Technology of the Siberian Branch of RAS “Library for Open Science” in January 2023 and demonstrates a gradual increase in the number of all web analytical indicators of news information products created on its basis.

A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.

Fake news text detection based on convolutional neural network

The swift evolution of mobile devices and multimedia technology has made the Internet one of the primary means of learning new information today. However, the huge amount of news information is often mixed with erroneous fake news, which can cause bad news events to spread and trigger people's bad emotions, putting the healthy development of society and economy at risk. Addressing the real-world application problem of swiftly and accurately detecting fake news is imperative. To mitigate the aforementioned challenges, we propose a method that uses deep learning to detect fake news and validate it through empirical studies. We begin by collecting a sizeable fake news dataset from domestic social media platforms and use a pre-trained deep learning model to extract textual features. Furthermore, we amalgamate convolutional neural networks and deep learning models to effectively glean and encompass the patterns and attributes of disinformation through an analysis of the text's semantic and structural characteristics. Finally, we experimentally evaluate the effectiveness of the method. The experimental findings demonstrate that the suggested approach exhibits commendable performance in the task of detecting fake news, effectively discerning between authentic and fabricated information. Our deep learning-based approach proves to be both efficient and highly impactful in addressing the issue of fake news within the realm of social media.

A deep learning approach for robust traffic accident information extraction from online chinese news

AbstractRoad traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.

Most of the Circulation Figures of Print Newspapers in India Illustrate the Strong Hold of Indian Languages

Abstract: With the advent of new media, print newspapers have also made a presence on the web. Through the findings of this study, researcher has been able to understand the future of print newspapers and e-newspapers in India. Since literacy rate is still growing and internet penetration is taking place but is slow, there is still time for e-newspapers to be fully accepted but gradually with increase in use of technology, print newspapers would meet the fate of their western counterparts. According to Robert Logan, new media possesses of features like interactivity, mutual form of communication and performs some metrics. Most of the traditionally existing media has been found to be mass media. New media technology differ from traditional existing media in terms of greater focus on interactivity. New media technology have made people greatly participative and they generate news content and information, through various available options. Information on new media is easily accessible and can stored as well. It is also hyperlinked so that reader can get more details. It is usually available through the search engines also.

Linear Kernel Optimization of Support Vector Machine Algorithm on Online Marketplace Sentiment Analysis

Twitter is a short message platform commonly used as a means of news information, commentary, and social interaction. One of the utilization of twitter is to analyze the sentiment of the online marketplace which can be used to determine the service, quality of goods, and delivery of goods on a product, service or application. This research aims to categorize the reviews or responses of the Indonesian people, especially to the online marketplace using the linear Support Vector Machine (SVM) algorithm. In order to make continuous improvements to the role of the Indonesian online marketplace in the future, sentiment analysis is needed. The analysis research tweets used were 4165 datasets using the python programming language. Sentiment analysis research stages include data collection, preprocessing, labeling, tf-idf weighting, split data, SVM model analysis and result evaluation. The data is then divided into 80% training data and 20% testing data, 50% training data and 50% testing data, 20% training data and 80% testing data. The results of the svm algorithm testing scenario obtained the highest optimization with an accuracy value of 97%, F1-score value on positive labels 88% and negative 98%, also obtained a positive recall value of 80% and negative 100% precision value on positive labels 98% and negative 97%, on 80% training data and 20% testing. It can be concluded that in this case, the linear svm algorithm is able to work to recognize models with a high level of accuracy so that in the future it can be used in similar cases.

Social Media as Commodifier or Homogenizer? Journalists’ Social Media Use in Individualistic and Collectivist Cultures and Its Implications for Epistemologies of News Production

This study seeks to address two research needs in journalism studies. First, there is a need for research that situates political reporters’ social media engagement as part of a broader investigation into journalistic epistemology, or what it means for these reporters to produce and validate information as authorized “knowers.” Second, there is a need for cross-national comparative research that looks beyond Western contexts alone and does so using the strengths of qualitative research methods. This study thus explores the use of social media by political reporters in the United States and South Korea. Through interviews with 35 journalists, we show that social media practices create a different sense of knowing, which operates according to a work atmosphere that is culturally conditioned. We find that U.S. journalism’s emphasis on exclusivity and individuality motivates reporters to orient social media use around producing unique news that no other reporter knows. Such emphasis on distinctiveness opens the door for honing their personal branding skills, ultimately leading to a self-commodification strategy. By contrast, in South Korea, with its long tradition of a collectivist-oriented Confucian culture, reporters treat social media as a more communal digital workspace that distributes news sources and information in an equitable manner.

Exploring Generation Z's Perspective on Twitter as a News Discovery Platform: Insights from @CNNIndonesia Followers

This qualitative descriptive research aims to explore how Generation Z perceives the use of Twitter, particularly the @CNNIndonesia news account, as a platform for news discovery. Employing purposive sampling, in-depth interviews, and observations, the study reveals that followers retain knowledge obtained from @CNNIndonesia, emphasizing the critical role of perception indicators in influencing their attitudes. The informants express a preference for @CNNIndonesia, citing its reliability and alignment with their information needs. The results underscore the significance of perception in shaping user behavior on Twitter as a news source. Highlights : Digital Information Consumption: Explore how Generation Z consumes and retains news information through the Twitter platform, specifically focusing on the @CNNIndonesia account. Perception Indicators: Investigate the key indicators shaping the perceptions of Generation Z followers towards @CNNIndonesia as a reliable and preferred news source. User Behavior Influence: Highlight the significance of perception in influencing the behavior of Generation Z users on Twitter as they seek and engage with news content. Keywords: Generation Z, Twitter, News Discovery, @CNNIndonesia, Perceptions

Communication and Social Sustainability: Information Disorder and the Risk of Pollution in the Italian Hybrid Media Ecosystem

Over the past three decades, a social dimension of sustainability has been established. Much like the environment, society also suffers from forms of pollution that pose a threat to community development. The phenomenon of information disorders, commonly referred to as fake news (FNs), represents an emblematic case of pollution of public discourse. This is particularly evident in the hybrid media ecosystem, where individuals construct their own information pathways in response to a process of deep mediatization. Considering this scenario, especially in the Italian context, research was conducted to investigate the impact of fake news on social sustainability. The survey was constructed around four research areas: sociodemographic factors, news consumption patterns, information disorders, and personal values. A non-probabilistic sample of the Italian population was drawn, totaling n = 399 individuals distributed throughout the country. The responses were analyzed using both single-variate and multivariate analyses. Specifically, a Social Sustainability Index (SSI) was synthesized from the analysis of sustainability indicators. This index revealed four levels of different behaviors concerning media use, political orientations, and especially, belief in fake news. Thus, a link seems to emerge between sustainability and the ability to inhabit the communication ecosystem properly.

Fake News Detection Based on Knowledge-Guided Semantic Analysis

Recently, fake news, such as low-quality news with intentionally false information, has threatened the authenticity of news information. However, existing detection methods are inefficient in modeling complicated data and leveraging external knowledge. To address these limitations, we propose a fake news detection framework based on knowledge-guided semantic analysis, which compares the news to external knowledge through triplets for fake news detection. Considering that equivalent elements of triplets may be presented in different forms, a triplet alignment method is designed to construct the bridge between news documents and knowledge graphs. Then, a dual-branch network is developed to conduct interaction and comparison between text and knowledge embeddings. Specifically, text semantics is analyzed with the guidance generated by a triplet aggregation module to capture the inconsistency between news content and external knowledge. In addition, a triplet scoring module is designed to measure rationality in view of general knowledge as a complementary clue. Finally, an interaction module is proposed to fuse rationality scores in aspects of text semantics and external knowledge to obtain detection results. Extensive experiments are conducted on publicly available datasets and several state-of-the-art methods are considered for comparison. The results verify the superiority of the proposed method in achieving more reliable detection results of fake news.

Role Orientation, Development and Transformation of News Editors in the New Media Era

With the rapid development of modern economy and society and the arrival of the information age, the new media have profoundly influenced the daily lives of people. News editors, as disseminators and advocates of news information, play an important role in the dissemination of news information. In the new media era, the news dissemination of new media relies on the degree of strengthening but also puts forward new development requirements for the development of news editors. This paper is based on the role of news editing and development transformation to analyse and solve the key problems in the current stage of news dissemination. In the new media era for the news editor, the new requirements listed in the new media era for the news editor include role positioning analysis and development transformation measures. The changing role of news editors in the new media era and their need to adapt to digital platforms and audience participation, utilise audience data, and develop multimedia narrative techniques Subvert the traditional communication mode of paper media, gradually adapt to and turn to the development of multimedia news reporting channels, and transform the skills and strategies of journalists to adapt to the progress of this era.

Media Policy, Structural Power, and Political Development at the US State-Corporate Interface

This article illuminates an underappreciated political dimension of capitalism by elaborating media policy as a site for the dynamic interaction of corporate power and the state. Synthesizing Marxian analyses of monopoly capital, American political development (APD) perspectives on public policymaking, and communication studies conceptions of media systems as fields of democratic struggle, I trace the institutional mechanisms that enable corporate media interests to reinforce systemic imperatives and maintain political-economic dominance. I illustrate my argument with examples from the Telecommunications Act of 1996. My analysis sharpens APD’s critical edge by defining path dependency, policy feedback, and policy drift as processes through which capital leverages structural power to fend off political challenges under changing economic, social, and technological conditions. State-sanctioned ownership and control of news outlets, communication platforms, and information networks furnish a unique mechanism for influencing the structural terms of public discourse on all issues, including media policy. This implicates corporate media power in the ongoing US political communication crisis and as a central force in the entrenchment of inegalitarian and undemocratic social relations. Lending greater theoretical coherence and empirical grounding to these material and ideological dynamics historicizes public communication and highlights contingent political openings for radical engagement with media policy.

Ab. No. 147 Pelvic Floor Health Literacy in Ahmedabad- A Pilot Study

Introduction: The purpose of the study is to investigate, beliefs and mentalities prevalent in community related to pelvic floor problems. Pelvic floor problems are not discussed because of certain barriers and healthcare personnel remain uninformed about the condition. Literature also mentions that, there are barriers in community. A Physiotherapist, working with diverse population needs knowledge about health literacy levels in community so as to educate, prevent and approach community with acceptable solutions. Methods: Both genders, 18-65 years were included from the community of Ahmedabad using convenience sampling was used. Participants from different socio-cultural, economic, educational backgrounds with/without history of pelvic floor issues were included. Participants belonging to medical background, and not willing to participate were excluded. Close-ended in English language questionnaire, validated by expert panel was prepared to investigate health literacy. A google form was filled by investigator in front of the participant after obtaining verbal consent. Descriptive analysis of 37(25 females,12 males) participants was done using Microsoft excel 2019 and Google spreadsheet. Result: Data collection is on-going. Mean age of participants was (38.41 + 12.06) years. More than 50% participants do not know the term “Pelvic Floor”. 60% participants do not know whom to consult, around 60% participants rely on family and/or friends’ advices. 56% of participants do not want to share these problems. Large number of participants feel cultural/societal barriers in sharing these problems, do not find information in news or social media and do not have an access to sources providing reliable information. Conclusion: Pelvic floor health literacy is low in community. Unawareness about “Pelvic Floor,” not having reliable sources, cultural and societal barriers about these problems keeps health care providers uninformed about the problem. Implication: Findings may help Physiotherapist build a culturally sensitive protocol, develop educational material, improve public awareness, plan early intervention and prevention.