News Platforms Research Articles

Relationship between news overload and news avoidance: A meta-analysis

In the era of digital news, news overload and news avoidance represent novel crises confronting the field of journalism. However, most relevant studies are limited to case studies, and a clear understanding of the relationship between news overload and news avoidance has yet to be established. To address this gap, a meta-analysis of 17 papers (with 13,143 participants) was conducted. The results suggest that news avoidance is positively linked to news overload. Compared to developing countries (r = 0.282), the correlation between news overload and news avoidance is more significant in developed countries (r = 0.330). The correlation between news overload and news avoidance is more readily observable when using specific news topics (r = 0.425) and specified news platforms (r = 0.378) than general news topics (r = 0.236) and online/social media news platforms (r = 0.228). Behavioral measurement tools (r = 0.366) reveal a stronger correlation between news overload and news avoidance than psychological measurement tools (r = 0.194). The findings enhance the theoretical framework for crisis management in the news industry and offer valuable strategies for effectively addressing issues of news overload and news avoidance by audiences.

The Media Image Construction of Chinese Female Athletes on the Weibo Platform from the Perspective of Self-shaping and Other-shaping

Abstract: The portrayal of public figures, especially female athletes, has undergone quite a bit of transformation due to social media and internet news platforms growing rapidly over the past years. This paper mainly analyzes the Weibo image construction of female Chinese athletes from the two perspectives: self-shaping and other-shaping. Three famous athletes are chosen for this study: Zhang Yufei, Wu Yanni, and Li Wenwen, who have each gained more than a million followers on Weibo. By doing text analysis and case studies, the focus here is placed on how athletes manage their public images and also how media and public reactions shape those images. The findings talk about the main reasons why self-shaping images and other-shaping images show differences, including things like cultural influence and gender roles, as well as the fewer female examples available in sports. This paper may offer some references for people who need to maintain their images in public.

Solidarity Building in Antagonistic Political Discourse on GhanaWeb

The advancement of technology has seen the growth of online news platforms where users can interact and share their views in the comments section. The comments section, therefore, has become a site for the expression of divergent views, leading to linguistic practices such as verbal attacks. This study sourced data from GhanaWeb, an online news platform that focuses on sociopolitical occurrences in Ghana, to demonstrate how users rely on insults, alongside other strategies like endorsement, irrelevancy claims, ill-wishes and unpalatable questions to create enmity with the out-group and build solidarity with the in-group. It was also found that there were cases where two participants or commenters who seem to share the same viewpoint were challenged by a third participant. This study makes a fair contribution to the emerging research on the linguistic practices on online communication platforms and highlights areas for further inquiry.

Analysis Sentiment & Topic Modelling using Latent Semantic Indexing

The proliferation of digital news platforms has transformed information consumption, making user experience (UX) a key success factor for Detik.com, a leading news application in Indonesia. This research aims to explore the relationship between UX and news through sentiment analysis and topic modelling. The methods include web scraping for data collection, descriptive analysis for sentiment, and Latent Semantic Indexing (LSI) for topic modelling. Out of 14,320 reviews analyzed, 45.6% were positive, focusing on the speed of updates and news quality. Conversely, 41.18% of reviews were negative, highlighting disruptive ads and login issues, while 13.76% were neutral. LSI identified eight main topics, including user experience and ad complaints. Visualizations through heatmaps, distance maps, and word clouds emphasized terms like "news" and "ads." This research recommends that Detik.com developers reduce ad disruptions, fix technical problems, and understand frequently discussed topics to enhance user satisfaction.

Improving the Quality of Individual-Level Web Tracking: Challenges of Existing Approaches and Introduction of a New Content and Long-Tail Sensitive Academic Solution

This article evaluates the quality of data collection in individual-level desktop web tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard for the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic web tracking solution, WebTrack, an open-source tracking tool maintained by a major European research institution, GESIS. The design logic, the interfaces, and the backend requirements for WebTrack are discussed, followed by a detailed examination of the strengths and weaknesses of the tool. Finally, using data from 1,185 participants, the article empirically illustrates how an improvement in data collection through WebTrack leads to innovative shifts in the use of tracking data. As WebTrack allows for collecting the content people are exposed to beyond the classical news platforms, it can greatly improve the detection of politics-related information consumption in tracking data through automated content analysis compared to traditional approaches that rely on the source-level analysis.

When echoes surpass voices: Market reaction to forwarded news

We examine the impacts of two types of news, forwarded news and novel news, on market reactions and investor attention in the Chinese stock market. Surprisingly, we find that forwarded news evokes a stronger market reaction than novel news. To understand this phenomenon, we investigate and find three mechanisms through which forwarded news influences investor behavior: filtering, verification, and amplification effects. The filtering effect shows that forwarded news, having been selected for resharing, contains information content that has persistent effects on stock return. The verification effect indicates that forwarded news, particularly following scandals involving news platforms, is perceived as more credible, thus eliciting a stronger market reaction. The amplification effect shows that the redistribution of news magnifies its reach and captures investor attention, leading to increased investor search and trading on the stocks. Moreover, the impact of forwarded news on investor attention is more pronounced for firms with greater information opacity. Our results show the distinctive roles of forwarded news and novel news in the financial market, highlighting the importance of considering the heterogeneity of news information.

The Ladder of Data Citizen Participation: A Sociotechnical Lens for Designing Democratic Digital Services in the Data Economy

Abstract In the data economy digital services of democratic value such as news platforms are embedded into unjust power imbalances and the intertwining of economic and civic objectives. The authors argue, to design proficient democratic digital services, power structures of the data economy and empowerment stages of data citizenship need to be made visible initially. To do so, a combination of Arnstein’s ‘Ladder of Citizen Participation’ and Rocha’s ‘Ladder of Empowerment’ is translated into the field of the data economy. A small interview study with niche actors from the field of online journalism is conducted to identify phenomena of their embedding into the sociotechnical landscape of the data economy. As a result, the Ladder of Data Citizen Participation is presented. Eventually, this article closes with implications for the sociotechnical design of democratic digital services and suggestions for future research.

Critical Discourse Analysis of Brunei's English Mainstream Media Perceptions on the Laos-China Railway

This study aims to comprehensively analyze the coverage of the Laos-China Railway project by mainstream media in Brunei and its impact on public perception. Utilizing web scraping tools such as Octoparse and Heritrix, this paper systematically collected relevant reporting data from two major English news platforms in Brunei, the Borneo Bulletin and Brunei News. Quantitative tools such as AntConc and Excel were employed to process and analyze the data, revealing the frequency, temporal distribution, and emotional orientation of the reports. The qualitative component utilized Critical Discourse Analysis (CDA) to delve into the language use, discourse structure, and implicit meanings within the reports, further exploring how the media shapes the social reality and public cognition of the Laos-China Railway topic.The findings indicate that Brunei media not only frequently and comprehensively report on the Laos-China Railway project but also cover the project's political, economic, environmental, and social implications from multiple perspectives. Additionally, the media coverage reflects a keen awareness of the project's potential risks and a positive appraisal of its developmental benefits. The novelty of this research lies in its mixed-methodological approach and unique perspective on public opinion cognition, providing insights for policymakers on public opinion guidance and project promotion, as well as offering a rich theoretical and methodological reference for the academic community.

Indonesian GDP movement detection using online news classification

Gross Domestic Product (GDP) stands as a pivotal indicator, offering strategic insights into economic dynamics. Recent technological advancements, particularly in real-time information dissemination through online economic news platforms, provide an accessible and alternative data source for analyzing GDP movements. This study employs online news classification to identify patterns in the movement and growth rate of Indonesia’s GDP. Utilizing a web scraping technique, we collected data for analysis. The classification models employed include transfer learning from pre-trained language model transformers, with classical machine learning methods serving as baseline models. The results indicate superior performance by the pre-trained language model transformers, achieving the highest accuracy of 0.8880 and 0.7899. In comparison, hyperparameter-tuned classical machine learning models also demonstrated commendable results, with the best accuracy reaching 0.845 and 0.7811. This research underscores the efficacy of leveraging online news classification, particularly through advanced language models. The findings contribute to a nuanced understanding of economic dynamics, aligning with the contemporary landscape of information accessibility and technological progress.

24/7 news network: A paradigm shift in the media industry

This research aims to investigate the role of 24/7 news networks in transforming how the media industry presents, consumes, and interacts with news. To achieve this goal, the study conducts a literature review on the development and evolution of 24/7 news networks and their impact on society. The analysis results show that 24/7 news networks have significantly changed the media landscape. Firstly, their ability to deliver news instantly and in real-time has shifted traditional news reporting models that relied on print editions or scheduled broadcasts. Second, the adoption of multimedia formats in 24/7 news network platforms has enhanced interactivity and reader engagement in the news process. However, the media industry faces challenges in dealing with this phenomenon. The demand to provide non-stop news puts pressure on journalists and editors to remain relevant and accurate in delivering information. Additionally, the emergence of social news platforms has brought new issues related to the validity and credibility of news. This article also highlights the social impact of 24/7 news networks. Easy access to news anytime and anywhere has influenced how society consumes news and interacts with information. Moreover, ethical questions arise on how to manage the information overload presented by 24/7 news networks without compromising the reliability of sources.

Peran media sosial bagi advokat muda

In the utilization of social media, there are notable advantages and disadvantages associated with the dissemination of legal information. Unreliable online news platforms have the potential to propagate misinformation, posing harm to society. Nonetheless, legal practitioners can leverage social media as a conduit for dependable legal information by implementing strategic measures. The evolution of information and communication technology, particularly the internet, has transformed communication into a digital format. The ubiquity of smartphones allows for diverse forms of communication, including engagement through social media. Applications like TikTok, favored among the younger demographic, serve as tools for legal professionals to construct information networks and convey legal knowledge in innovative ways. It is crucial to bear in mind that social media can have adverse effects concerning the spread of inaccurate legal information. Advocates can mitigate this impact by utilizing reputable platforms, delivering pertinent information, and collaborating with fellow legal experts to generate informative and reliable content. In the execution of their responsibilities, advocates are obligated to uphold the integrity and esteem of the profession, exercise prudence and ethical conduct in using social media to cultivate a professional image, and disseminate precise and beneficial legal information.

Balancing Needs and Values: A Multi-Stakeholder Examination of Algorithmic News Recommenders in the Netherlands

ABSTRACT This paper aims to deepen understanding of the negotiation process underlying the values embedded in algorithmic news recommenders. The focus is on examining the perceptions and aspirations of different stakeholders and what values are ultimately incorporated in the design of a recommender system. Specifically, it investigates the development of value-driven recommendations at a leading Dutch online news platform, employing a combination of aspects of participatory action research and a multi-stakeholder framework. This is achieved through workshops and interviews with practitioners, critically examining the constraints and value tradeoffs that emerge among key internal stakeholders: journalists, editors, chief editors, and the technical team. The paper reveals how there is a tendency to prioritize technical aspects that align with immediate business goals. This does not stem from ill-intent or an unwillingness to explore other values but has practical reasons. Additionally, the study uncovers reservations and misconceptions about recommender systems by certain stakeholders, highlighting the need for improved understanding and dialogue among stakeholders.

DiversiNews: Enriching News Consumption with Relevant Yet Diverse News Articles Retrieval

In the digital age, where echo chambers on social media and news platforms increasingly shape public opinion, there is a growing need for tools that present news consumers with a broad spectrum of perspectives. To this end, we introduce DiversiNews, a novel system designed to diversify news consumption by providing readers with articles that are not only relevant to their interests but also offer a variety of viewpoints. DiversiNews leverages state-of-the-art semantic text encoding techniques and implements advanced Diversity-aware k -Maximum Inner Product Search (D k MIPS) algorithms. Our demonstration highlights the potential of DiversiNews to broaden users' exposure to different viewpoints, thereby countering the polarizing effect of digital echo chambers. We showcase how DiversiNews can enrich the news reading experience, supporting the development of a more informed and balanced public discourse in digital news consumption applications.

A Depiction of Rohingya Refugees in India’s Online News Platforms Following the Shift in the Indian Government’s Stance in 2017

A Depiction of Rohingya Refugees in India’s Online News Platforms Following the Shift in the Indian Government’s Stance in 2017

Evaluating User Engagement in Online News: A Deep Learning Approach Based on Attractiveness and Multiple Features

Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.

From news websites to social media: Unpacking the influence of online channels on presumed influence and responses to misinformation

This study employs the theoretical model of the influence of presumed influence to examine the mechanisms that drive online users to engage in information authentication and social correction, which are critical for countering misinformation. In particular, this study examines how the perceived prevalence and presumed influence of misinformation affect the intention to verify and correct misinformation, and how the use of different online platforms (i.e., social networking sites, video platforms, instant messaging applications, and news websites) for political news shapes these perceptions. This research draws on survey data from 2,589 respondents in Taiwan. The findings indicate that perceived prevalence has a positive relationship with presumed influence, which in turn, leads to authentications and corrections. Moreover, using social networking sites and video platforms for political news is positively related to perceived prevalence, whereas instant messaging applications have a negative association.

Coping Responses to the Stress of Using News Platforms’ Recommendation Algorithms

Drawing on the stressor–strain–outcome (SSO) framework, this study examined how information-related stressors (perceived information overload, information quality deterioration, and information narrowing) and privacy-related stressor (perceived privacy risk) in using news platforms’ recommendation algorithms trigger users’ psychological reactance, which leads to their active coping, expressive coping, or avoidance coping with their stress in using such recommendation algorithms. Using a sample of 352 valid research responses, this study found that users’ psychological reactance is positively influenced by perceived information quality deterioration, followed by perceived information overload and perceived privacy risk. Meanwhile, perceived information narrowing has no significant impact on psychological reactance. In turn, psychological reactance exerts a stronger impact on expressive coping and avoidance coping responses than on active coping response. This study enriches the literature on the stress in algorithm usage at the individual level and provides practical insights for the providers of news platforms which have applied recommendation algorithms.

Bonding over bashing: Discussing LGBTI topics in far-right alternative news media comments sections

Abstract This study investigates virtual community-building practices and discriminatory views in PAL NWS, a Dutch-speaking Belgian far-right alternative news medium, by examining discussions in the comments sections. Thematic analysis was applied to a total of 1,127 comments by 343 users in response to 50 articles about LGBTI topics. The findings show that far-right alternative news sites can function as virtual communities that facilitate polarization. The comments exhibited a high level of hostility towards LGBTI individuals, particularly toward transgender people and public displays of LGBTI inclusion. Furthermore, Muslims were homogeneously perceived as homophobes, and LGBTI topics were understood as (woke) ideological suppression by a dogmatic minority. The results of this study have implications for understanding the dynamics of comments sections on alternative media and the potential for far-right news platforms to facilitate the spread of intolerance.

A Hybrid Deep Learning Based Fake News Detection System Using Temporal Features

Detecting fake news and missing information is gaining popularity, especially after social media and online news platforms advancements. Social media is the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to determine whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use different models such as LSTM, BERT, and CNN- BiLSTM to investigate, and it is concluded that from BERT, we get better results, and 70% of true news is recurring, and the rest of 30% is non-recurring.

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>