Relevant News Research Articles

Komodifikasi Budaya dalam Perayaan Kultural Pasar Malam Tjap Toendjoengan Surabaya dalam Mempertahankan Warisan Kuliner Lokal

Tjap Toendjoengan Night Market in Surabaya is a cultural celebration that is not only a major highlight in the city but also a center of cultural, culinary and local economic activities. This research analyzes how the process of cultural commodification occurs in this night market, by considering the transformation of previous culinary practices into products that can be sold and commercialized. The method used is a systematic literature review with the help of the Perish or Publish application and the Google Scholar database using the keywords tjap toendjoengan night market and cultural commodification in 2014 - 2024 and obtained 12 relevant journals and news articles from 100 journals. The findings of this study show that while cultural commodification can provide economic benefits, it is also necessary to be aware of its potential negative impacts, such as a decline in original cultural values or damage to cultural integrity. Therefore, it is important for relevant parties, including night market managers and local governments, to take prudent measures in promoting and maintaining the sustainability of local culture without compromising the integrity of the actual culture. The Tjap Toendjoengan night market can thus serve as a good example for the preservation and sustainable utilization of local cultural heritage.

An Automated Daily News Reports Generating Application Involving Keyword-Based News Scraping, Summarization, and Sentimental Analysis Leveraging NLP Models

In the era of information overload, obtaining relevant, concise, and sentiment-analyzed news content is essential for effective decision-making. This paper introduces an automated daily news reporting system that streamlines the process of collecting, summarizing, and analyzing the sentiment of news articles fetched based on the user's keyword. By leveraging stateof-the-art Natural Language Processing (NLP) models like GPT-3.5 for two-level summarization and BERTweet for sentiment analysis, the system provides users with concise, sentiment-labeled reports, enhancing their understanding of news trends and emotional tone. The architecture integrates data scraping, text extraction, and sentiment classification within a cloud-based Python microservice, supported by Flask. The system incorporates user localization options, allowing users to customize news retrieval by region and time preferences. Finalized reports are formatted into HTML and delivered directly to authenticated user emails through Gmail API, ensuring seamless and secure distribution. This research underscores the significance of automated news summarization and sentiment analysis in modern information retrieval. It provides a scalable and personalized solution that enables real-time synthesis of large volumes of news content, making it accessible and relevant for diverse audiences worldwide.

The impact of smart speakers and podcasts on news media consumption

ABSTRACT The increasing diffusion of smart speakers results in substantial challenges for news publishers. Voice-based formats – especially news podcasts – gain significant importance via smart speakers, raising the question of displacement effects on other news media. We investigate the interplay of smart speakers and podcasts with news consumption data from 1,528 respondents collected in Germany. We control for selection effects using propensity score matching and find (I) a higher usage of news podcasts as well as (II) higher podcast news consumption intensity, both driven by smart speaker diffusion. Additionally, podcast usage increases the total cross-media news consumption intensity without displacing other media. We provide converging evidence that smart speakers are a relevant complementary news content channel to access podcasts, which subsequently contribute to strengthening news consumption through other media.

Implementing online discussion boards to enhance professional skills and news reading habits in accounting students

ABSTRACT This study investigates the impact of a teaching and learning activity using news articles on fostering professional skills (including critical thinking, collaboration, and openness to learning). In this activity, an Asynchronous Online Discussion Board (AODB) was used as a medium to facilitate learning. Implemented in two postgraduate accounting courses over two academic years, the AODB activity required students to engage with relevant business news, make a meaningful contribution, and interact with their peers. A mixed-method approach, involving a survey of 231 students and 22 semi-structured interviews, was used to assess the effectiveness of the activity. The results show that the activity significantly enhanced engagement, critical thinking, and collaborative learning, with critical thinking emerging as the predominant skill developed. Additionally, students demonstrated an increased frequency of reading business news, reflecting the integration of lifelong learning habits. This research contributes to the literature by offering a practical and scalable approach for accounting educators globally to develop both technical and non-technical skills, addressing the growing demands of the profession in a digitalised, global business environment. The study also emphasises the importance of innovative teaching methods that can be adapted to diverse educational contexts.

A text-based recommender system for recommending relevant news articles

Despite recent advances in the related fields and the growing popularity of AI-based tools, small businesses, and public institutions still face challenges when implementing recommendation systems to increase profits and provide personalised services. This paper provides a comprehensive overview of state-of-the-art systems and a case study of a recommender system for a small, low-budget project, battling with several constraints: the use of uncommon natural language, the use of raw, unlabelled textual data, thus using mainly techniques coming from the field of data mining and text mining rather than relying on supervised learning methods. Another constraint was the reliance on the smaller dataset and the limited resources available for the development. This led us to explore a range of content-based methods, including the utilisation of the similarity measures applied to word embeddings derived from the Word2Vec and Doc2Vec shallow neural network models, as well as the TF-–IDF method. Additionally a topic modelling approach utilising the Latent Dirichlet Allocation was used, as well as collaborative filtering methods, such as the algorithm using the Singular Value Decomposition method, and hybrid methods integrating the fuzzy inference and fuzzy expert systems. Some of the obstacles identified were subsequently demonstrated to be too challenging for the development of accurate recommendations. However, the item-to-item similarity solutions, which were primarily content-based, yielded satisfactory results when the threshold of 75% of the average precision of recommendations assessed by users was exceeded. One such solution was the one that used the Word2Vec-based model, which had been trained on the parameters obtained from the word similarities and analogies tests. Furthermore, an overview of alternative techniques and methodologies is provided.

The Role of the Church in Teaching Effective Parenting Methods to Protect the Alpha Generation from the Dangers of Gadget Addiction

This research explores the church's role in supporting parents of Generation Alpha and implementing effective parenting methods to prevent gadget addiction. This research collected and analyzed secondary data from relevant scientific journals, books, and news articles through a literature study approach. The results show that churches are important in providing education and resources for parents to face parenting challenges in the digital age. The research suggests that the integration of spiritual values and faith-based parenting guidelines can help parents in guiding their children to face the risks of device addiction. Furthermore, the study identified the need for educational programs specifically designed to equip parents with practical strategies in regulating their children's use of technology. The results of this study contribute to the understanding of the importance of synergy between church and family in educating children in the digital age.

Strengthening Business Collaborations in the Sidoarjo District MSME Community

Background: Effective communication is vital for group dynamics within communities, particularly in business networks. Specific Background: This study focuses on the communication practices of the Micro, Small, and Medium Enterprises (MSMEs) community. Knowledge Gap: While prior research exists, the dynamics of information sharing in such informal settings have not been thoroughly examined. Aims: The research aimed to analyze communication dynamics within an MSME community and identify factors affecting interaction. Results: Findings indicate that informal interactions strengthen relationships and enhance partnership potential, yet information sharing remains limited, with only a few members actively disseminating business-related news. Novelty: This study emphasizes the significance of informal communication in building relationships while highlighting the need for improved information dissemination. Implications: Recommendations include fostering a more flexible communication environment to enhance collaboration and information exchange within MSME communities. Highlights : Informal Interactions: Foster strong relationships among community members. Limited Information Sharing: Only a few members actively disseminate relevant business news. Need for Flexibility: A more inclusive communication environment is essential for collaboration. Keywords: communication, MSMEs, information sharing, community dynamics, partnerships

Debates over the role of Traditional Chinese Medicine on COVID-19: A computational comparison between professionals and laypersons in Chinese online knowledge community

Leveraging a large collection of textual data (N = 21,539) from a Chinese online community, we employed structural topic modeling to investigate the thematic disparities between professionals and laypersons, regarding the effectiveness of Traditional Chinese Medicine (TCM) on COVID-19. Findings reveal that laypersons are the dominant communicators in terms of discussion volume, who often focus on relevant news events, societal or political aspects of TCM. In contrast, professionals keep concentrating on issues related to medical expertise, and do not shift attentions as frequent as laypersons. Despite the dominant influence of professionals on laypersons’ agenda, two-way agenda interactions identified confirm that lay public is empowered to negotiate with elite professionals under certain topics. Our results provide novel insights into the dynamic nature of attentions, behaviors, and relations among prominent communication actors, and encourage future research to examine the individual-level and societal-level impacts of these constructs in the emerging online media landscape.

Urban Gentri: Serious Game on Learning about Gentrification

As a global phenomenon, gentrification has far-reaching social and economic implications. As civic engagement and public understanding are crucial for responding to gentrification, employing a game-based approach could be effective due to the games’ ludic attributes. However, existing gamified interventions often lack structured game design and empirical assessment of their learning effectiveness. As such, this study aims to develop a serious game (SG) and assess its effect on enhancing public understanding and awareness of gentrification using an Analysis, Design, Development, Implementation, and Evaluation framework. A prototype of a serious board game, Urban Gentri, was developed to immerse participants in the process and consequences of gentrification. A Mechanics-Dynamics-Aesthetics framework was used when developing the rules of the game. Then, the study employed a mixed-methods approach, with forty-five participants completing pre- and post-tests and semi-structured exit interviews. Quantitative analysis showed a statistically significant increase in cognitive learning scores, demonstrating an improvement in the understanding of gentrification. The qualitative analysis of the exit interviews revealed the SG’s effectiveness in fostering empathy and encouraging participants to explore solutions (e.g., communication and policy interventions) to mitigate gentrification’s negative implications. Participants also expressed their willingness to engage in civic actions, such as supporting local businesses or paying attention to relevant news articles after playing the game. This implies the role-playing aspect allowed participants to empathize with different stakeholders, leading to a deeper appreciation of the complexities involved in gentrification. The findings highlight SGs’ potential for promoting public understanding and awareness of urban planning issues, including gentrification. By leveraging SGs as educational tools, planners and educators can create engaging platforms to inform the public and promote informed decision-making on complex urban issues.

The government intervention effects on panic buying behavior based on online comment data mining: a case study of COVID-19 in Hubei Province, China

At the end of 2019, the world grappled with an unparalleled public health crisis due to the COVID-19 pandemic, which also precipitated a global economic downturn. Concurrently, material panic buying occurred frequently. To restore benign market order, the government instituted a series of interventions to stabilize the market. This scholarly exploration dives deep into evaluating the tangible impact of these governmental measures in Hubei, China, a region which found itself at the very epicenter of the epidemic in its onset phase. Existing papers often employ structured questionnaires and structural equation methods, with small samples and limited effective information. In contrast, we used a dataset of tens of thousands of entries and employed text analysis to maximize the extraction of valid information. Through a meticulous analysis of public feedback, our findings unveil several pivotal insights: (1) The news measures of materials have the best effect. Their effectiveness, in descending order, is ranked as: material sufficiency > authority effect > market supervision > appeal and guidance; (2) Government measures during the epidemic’s initial phase exhibited a delay. After the lockdown measures, the phenomenon of large-scale buying has been formed, and the relevant material news was released later; (3) A dual approach combining authority influence with material sufficiency yielded the most favorable results. In light of these findings, the paper concludes with tailored recommendations aimed at amplifying the efficacy of government-led public opinion interventions in future crises.

An announcement effect in reverse? Evidence from cash‐settled convertible bonds

AbstractWe analyze the trading activity of underlying shares at the maturity of convertible bonds with cash‐settlement provisions. Our findings indicate that arbitrageurs and option dealers engage in systematic trading that leads to an increase in trading volumes and changes in the level of short interest in the affected underlying equities. We find significantly positive abnormal returns for the affected stocks if convertible bond‐related trading is not contaminated by opposing option‐related flows. This finding supports the notion that arbitrageurs can create a substantial market impact on shares underlying convertible bonds even without any relevant news dissemination.

ИНФОРМАЦИОННОЕ ОБЕСПЕЧЕНИЕ МОРСКИХ НАУЧНЫХ ИССЛЕДОВАНИЙ

According to the decisions of the Maritime Board at the Government of the Russian Federation and the Interdepartmental National Oceanographic Commission, the information platform “Marine Scientific Research” is elaborating by order of the Ministry of Science and Higher Education of the Russian Federation. The platform aims to attract all stakeholders interested in marine scientific research to the integrated information platform, as well as to provide upto-date information on the studies, conservation and use of the World Ocean to a wide range of users, including non-specialists. It has three main thematic areas: marine expeditionary studies, the activities of the Russian Federation within the framework of the goals of the United Nations Decade of Ocean Science, as well as the work of the Interdepartmental National Oceanographic Commission, which also works as National Committee for the Decade. In addition, the platform contains the most relevant news in these areas, information about the main maritime organizations and their activities. A special section of the platform is dedicated to the ocean literacy in a playful way, in particular through “Sea-loving quizzes”. The article discusses the main stages of the platform’s development, the principles of data integration and data sources, as well as the preparation of information and analytical materials for content, the role in popularizing ocean science and the main directions for its further development and integration with similar information resources.

Hybridity and Paradox: The Cultural Bridge of Gen Z (A Critical Multimodal Discourse Analysis on the X Account @Aniesbubble)

Social media, such as Twitter, enable immediate and delayed interaction with a wide audience. Online personas of political figures, such as the X account @aniesbubble, are in the spotlight in the 2024 election campaign, especially in attracting the attention of potential young voters in Indonesia. This research aims to ana-lyze the critical multimodal discourse on X @aniesbubble account in the context of the 2024 election political campaign. The main focus is to understand how multimodal ele-ments are used to shape opinions and interactions with audiences, as well as the implica-tions of using this strategy in cultural and political contexts. This research uses a qualita-tive method with a multimodal critical discourse analysis approach. The main data was taken from the X @aniesbubble account in the time period before the general election until the determination of the election winner. The analysis was conducted based on Norman Fairclough's theory, which includes three levels of analysis: text, discursive practice and social praxis. Additional data was obtained from relevant books, news articles, and journal articles. The research found that the @aniesbubble X account succeeded in creating an authentic and attractive political persona for the younger generation through the use of glocalization and cultural hybridity strategies. The account also demonstrates the dynamic interaction between global and local influences, and is able to convert social capital into economic capital through the active participation of fans. The findings suggest that social media plays an important role in shaping political and cultural narratives.

Voice-Assisted News App using Natural Language Processing

In recent times, the advancements in Artificial Intelligence (AI) and natural language processing (NLP) have enabled the development of novel voice-assisted apps. This study introduces a cutting-edge Voice-Assisted News App that utilizes the capabilities of Alan M, a prominent AI platform that revolutionizes the way people access news. This research endeavor attempts to utilize Alan Al's capabilities to offer a personalized news experience and enhance clarity through voice interactions. Users of the Voice-Assisted News software can effortlessly request and select their preferred news topics, and the computer will generate the most relevant news updates. Through integration with diverse news sources and the application of sophisticated algorithms, the program generates an individualized news stream for every user, ensuring they receive the latest and most relevant information. Users can efficiently access story audio descriptions. The app's speech interface allows users to navigate across different news categories. The NLP algorithms developed by Alan AI offer precise understanding of user demands by ensuring smooth interaction and an authentic conversational experience

Event Identification for Supply Chain Risk Management Through News Analysis by Using Large Language Models

Event identification is important in many areas of the business world. In the supply chain risk management domain, the timely identification of risk events is vital to ensure the success of supply chain operations. One of the important sources of real-time information from across the world is news sources. However, the analysis of large amounts of daily news cannot be done manually by humans. On the other hand, extracting related news depends on the query or the keyword used in the search engine and the news content. Recent advancements in artificial intelligence have opened up opportunities to leverage intelligent techniques to automate this analysis. This paper introduces the LUEI framework, a lightweight framework that, with only the event’s name as input, can autonomously learn all the related phrases associated with that event. It then employs these phrases to search for relevant news and presents the search engine results with a label indicating their relevance. Hence, by conducting this analysis, the LUEI framework is able to identify the occurrence of the event in the real world. The framework’s novel contribution lies in its ability to identify those events (termed as the Contributing Events (CEs)) that contribute to the occurrence of a risk event, offering a proactive approach to risk management in supply chains. Pinpointing CEs from vast news data gives supply chain managers actionable insights to mitigate risks before they escalate.

“It Could Have Been Us”: media frames and the coverage of Ukrainian, Afghan and Syrian refugee crises

Russia’s invasion of Ukraine in February 2022 forced millions of Ukrainians to flee their homes and seek refuge in other countries, resulting in one of Europe’s largest refugee crises in decades. Many countries were quick to mobilise and provide the needed assistance to Ukrainians refugees. In contrast to the usual responses to asylum seekers coming from outside Europe, EU countries as well as the UK were uncharacteristically welcoming. Similarly, news reports of the Ukraine refugee situation have been marked by affectionate headlines and an outpouring of support and sympathy arguably not seen previously in the context of other refugee crises outside of Europe. This prompted questions and discussions about the alleged double standards and disparities in the treatments and portrayals of refugees from different countries. Inspired by such discussions and through a media frame analysis approach, this article examines a sample of relevant online UK news articles from the BBC and The Sun and compares their coverage of the Ukrainian refugee crisis to that of the Syrian and Afghan ones. The overarching aim is to identify how these distinct refugee crises are framed within the sampled articles and whether there are, in fact, discursive differences in the representation and framing of these crises, as often claimed in existing debates on the subject.

AI GENERATIVE CHATBOT IN THE MEDIA: JOURNALISTIC COVERAGE OF CHATGPT IN BOSNIA AND HERZEGOVINA

In diffusion research, journalistic coverage is acknowledged as a significant factor in spreading awareness and fostering knowledge about innovation, potentially accelerating or impeding the adoption process. With regards to AI-related innovations, this dynamic has largely been studied within the context of Western developed countries. There is far less understanding of how this process unfolds in the news ecosystem of post-communist countries, particularly those with lower democratic standards and weaker economic development, such as Bosnia and Herzegovina. With the intention of gaining preliminary insights, this study investigated how the journalistic organizations in Bosnia and Herzegovina covered the emergence and societal adoption of ChatGPT, a novel form of generative AI, during the initial six-month period following its widespread availability. The content analysis of relevant news messages (N=542) published by 40 legacy and digital- only news outlets was used to explore the key characteristics of journalistic coverage, the attention given to the issue over time and the media depictions of this innovative AI technology. Results indicate that a small group of news outlets, predominantly legacy news organizations, provided significantly more content on ChatGPT than others, particularly public broadcasting services. Findings highlight a tendency among news outlets to focus on either the risks or benefits of ChatGPT and similar AI-based products and amplify sources associated with the business sector and high-tech industry, overrepresented by male voices.

Trust in media and processing of health information during the Covid-19 pandemic

Based on a nationwide survey of 938 American adults in 2020, this study analyzed systematic and heuristic information processing related to Covid-19. Results showed mainstream media use correlated with systematic processing, while partisan and social media use correlated with heuristic processing. Trust in mainstream media predicted systematic processing, whereas trust in partisan and social media predicted heuristic processing. Perceived importance of health issues was positively associated with systematic processing and negatively with heuristic processing. Perceived ability to find relevant health news positively predicted systematic processing. The findings underscore the complex interplay among media trust, media use, and individual perceptions in information processing during public health crises.

Representation of Victim and Perpetrator in Adelina Lisao’s Abuse Case in Malaysian Online News Articles

This research analyses the representation of victim and perpetrator as social actors and its argumentation strategies from Malaysian online news articles surrounding the tragic case of Adelina Lisao, an Indonesian domestic worker who lost her life in Malaysia due to abuse by her employer. Employing a qualitative research approach, the data is systematically collected with keywords to identify relevant news articles pertinent to the case: "Adelina Lisao” and “Ambika Shan”. This research utilises the Social Actor’s Network (SAN) and the Critical Discourse Analysis (CDA) framework, specifically the Discourse-Historical Approach (DHA), to examine 39 online news articles about the case from the most popular Malaysian news website in 2023, Malaysiakini. The findings revealed that Adelina Lisao was mostly represented as a passive receiver of the abuse, while Ambika Shan’s role as a perpetrator was mostly backgrounded. The findings contribute to a better understanding of how social actors are portrayed on online websites and the argumentation strategies within the context of domestic worker abuse cases in Malaysia. Thus, it acknowledges the importance of language in online news discourse in shaping public perceptions, particularly regarding domestic workers in Malaysia as evidenced by the representation of victim-perpetrator dynamics in such cases.

SHARIA LAW AND COMMUNITY RESPONSE TO ROHINGYA REFUGEES IN ACEH

In Aceh, the implementation of Sharia law plays a significant role not only in social and religious regulation but also in responses to humanitarian issues, such as the case of Rohingya refugees. This study aims to discuss how Sharia law influences the perceptions and actions of the Acehnese community towards Rohingya refugees. The study employs a literature review as the primary approach to gather and analyze existing data. Data were collected through systematic searches in prominent academic databases, online archives, and digital libraries. The search focused on reputable journals, books, government and NGO reports, as well as relevant news articles pertaining to this area of study. The findings of this study indicate that the implementation of Sharia law in Aceh significantly influences the community's response to the arrival of Rohingya refugees. Sharia law in Aceh functions not only as a legal framework but also as a social ethos that shapes humanitarian actions towards the refugees. Sharia law has reinforced humanitarian and solidarity values within the community, as evidenced by the warm reception given to the refugees. However, the study also finds that economic and political factors can modify and even diminish these positive responses over time. The study concludes that in the context of Rohingya refugees, while Sharia law plays a role in shaping positive social norms and behaviors among the Acehnese community, it sometimes fails to adapt or is insufficiently responsive to changing economic and political pressures. These pressures can alter community perceptions and responses to refugees in the long term. Therefore, the study concludes that the complex dynamics between Sharia law and socio-economic conditions in the context of refugees are critical aspects that policymakers and humanitarian practitioners need to address.