Web News Research Articles

Fiqh dan Negara: Dinamika Penerapan Sanksi Hukuman Zina di Aceh

Fiqh is the most important element in the legal tradition of the people of Aceh. All kinds of problems are resolved through the provisions of fiqh, and the power of fiqh can even override state law. In its application, the legal sanction of zina described in the fiqh book differs from the legal provisions of zina in Qanun Jinayat. This paper is based on a doctrinal study with a historical and legal approach. In order to strengthen the data of this study, some important research results in connection with this study are used in this study. In addition, this research also uses data obtained from online and web news directly from reliable sources. Data analysis was carried out using content analysis of the legal materials obtained and finally in-depth analysis was carried out. in-depth analysis. This research argues that the punishments for perpetrators are not fully taken from the existing law in the book of fiqh jinayat, in the book of fiqh the punishment for adultery is divided into two classifications and the punishment is lashing and stoning. However, in the Qanun Jinayat, the punishment for the perpetrator of adultery is only 100 lashes. However, in the Qanun Jinayat the sanction for the perpetrator of adultery is only 100 lashes, there is no legal sanction of stoning for the perpetrator of adultery muhsan as stipulated in the Fiqh Book.

Stock Market Prediction Using Machine Learning Techniques

The prediction of stock market prices holds significant importance within the contemporary economic landscape. Consequently, there has been a notable surge in scholarly interest directed towards exploring novel avenues for enhancing stock market prediction capabilities. Recent research endeavors have illuminated the potential predictive value inherent in various data streams, including historical stock data and user-generated content sourced from platforms such as Twitter and web news. These investigations have revealed a discernible relationship between social mood, as reflected in online discourse, and future stock price movements. However, prior studies have often overlooked the incorporation of such sentiment-derived information, thus presenting an information gap. In the present study, we address this gap by proposing an effective methodology for the integration and analysis of multiple information sources to facilitate more accurate stock price predictions. Leveraging Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models, we conduct a comprehensive analysis of disparate data sources. Furthermore, we employ an ensemble approach, incorporating Weighted Average and Differential Evolution techniques, to refine the predictive accuracy of our models. Our findings demonstrate the efficacy of the proposed methodology in generating highly accurate stock price predictions across varying future time horizons, including one-day, seven-day, 15-day, and 30-day intervals. These predictions offer valuable insights for investors seeking to make informed decisions regarding their investment strategies and enable companies to gauge their anticipated performance within the stock market landscape.

Using X Social Networks (formerly Twitter) and web news mining to predict the measles outbreak

Using X Social Networks (formerly Twitter) and web news mining to predict the measles outbreak

The Sectoral and Regional Peer Influences on Heavy-Pollution Corporate Environmental, Social, and Governance Performance

The conception of environmental, social, and governance (ESG) performance has been widely implemented and has become an important indicator of firms’ eco-friendly transformation in heavy-pollution industries. The sectoral and regional peer influences of corporate ESG performance can effectively promote firms’ green sustainable development within an entire industry, district, and market. In this study, our main hypothesis is that corporate ESG performance has a significantly positive peer effect among heavy-pollution industry firms within the same province, industry, and product market. Therefore, by employing novel spatial econometric techniques, we investigate the peer effect of corporate ESG performance among 681 of China’s A-share listed firms within 20 heavy-pollution industries from 2012 to 2021 and explore the impacts from peer indirect effect views, such as public media attention, regulatory pressure, and green innovation. Further, we detect the sectoral and regional peer pulling and dragging effects under the two statuses of firms’ ESG rating changes. The main findings are as follows. First, corporate ESG performance has a significantly positive peer effect, which is the highest among firms within the same industry. Second, the mechanism analysis presents that the increase in other firms’ negative web news, environment-related penalties, and green patents has different peer indirect effects on corporate ESG performance within the same province, industry, and product market. Third, corporate ESG performance has a significantly positive peer-pulling effect among firms when other firms’ ESG levels increase, yet a significantly positive peer-dragging effect only within the same region and industry when other firms’ ESG levels decrease. This study gives empirical contributions that firms can take advantage of the positive peer effect of corporate ESG performance to improve their own ESG practice level and employ it as a competitive strategy for pursuing long-term value, and governments should maintain sustainable supervision measures and an orderly competitive market environment to cultivate a consensus on corporate ESG development in heavy-pollution industries.

The Impact of Climate Change on Media Coverage of Sponge City Programs: A Text Mining and Machine Learning Analysis

ABSTRACT Sponge city programs (SCPs) are essential for improving urban resilience and sustainability, while media framing can imperceptibly influence public attitudes and thereby influence the smooth implementation of SCPs. Due to socio-political landscapes, media discourse on official and non-official media is different, changing and evolving as emergency events occur. However, few studies have focused on media disclosure differences or investigated the transformation of SCP-related media discourse before and after emergencies. Therefore, based on text mining methods, this study conducts a longitudinal big-data discourse analysis to compare framing differences in state-oriented newspapers and market-oriented web news. A total of 6,413 news articles from July 17th, 2020 to April 17th, 2022 are crawled. The results demonstrate that SCP publicity involves different narratives covering macro-level and micro-level content. After the disaster, attitudes of web news towards SCP are less supportive than those of state-oriented newspapers. Meanwhile, the most salient differences in SCP benefit coverage are social and economic benefits, and the most salient difference in SCP risk coverage is economic risks. Findings provide a retrospective assessment and present valuable practical implications for evidence-based SCP advocacy.

Chinese News Text Classification Method via Key Feature Enhancement

(1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. It is noted that the existing deep learning methods rely on a large-scale tagged corpus for news text classification tasks and this model is poorly interpretable because the size is large. (2) Methods: To solve the above problems, this paper proposes a Chinese news text classification method based on key feature enhancement named KFE-CNN. It can effectively expand the semantic information of key features to enhance sample data and then combine the zero–one binary vector representation to transform text features into binary vectors and input them into CNN model for training and implementation, thus improving the interpretability of the model and effectively compressing the size of the model. (3) Results: The experimental results show that our method can significantly improve the overall performance of the model and the average accuracy and F1-score of the THUCNews subset of the public dataset reached 97.84% and 98%. (4) Conclusions: this fully proved the effectiveness of the KFE-CNN method for the Chinese news text classification task and it also fully demonstrates that key feature enhancement can improve classification performance.

Innovation signals: leveraging machine learning to separate noise from news

The early detection of and an adequate response to meaningful signals of change have a defining impact on the competitive vitality and the competitive advantage of companies. For this strategically important task, companies apply corporate foresight, aiming to enable superior company performance. With the growing dynamics of global markets, the amount of data to be analyzed for this purpose is constantly increasing. As a result, these analyses are often performed with an unreasonably high investment of financial and human resources, or are even not performed at all. To address this challenge, this paper presents a machine-learning-based approach to help companies identify early signals of change with a higher level of automation than before. For this, we combine a newly-proposed quantitative approach with the existing qualitative approaches by Cooper (stage-gate model) and by Rohrbeck (corporate foresight process). After a search field of interest has been defined, the related data is collected from web news sites, early signals are identified and selected automatically, and domain experts then assess these signals with respect to their relevance and novelty. Once it has been set up, the approach can be executed iteratively at regular time intervals in order to continuously scan for new signals of change. By means of three case studies supported by domain experts we demonstrate the effectiveness of our approach. After presenting our findings and discussing possible limitations of the approach, we suggest future research opportunities to further advance this field.

THE USE OF NATURAL DISASTER NEWS TEXT IN MODELLING STAGE TO COMPOSE NEWS ITEM TEXT: AUTHENTIC MATERIAL IN THE SECOND STAGE OF GENRE-BASED APPROACH

This study aims to investigate how the news item texts composed by students are based on a natural disaster news text, and how they respond to the use of natural disaster news text as a learning media to compose news item text. Based on these aims, this study uses a qualitative study by means of observation, document analysis, and questionnaire. Data collection was carried out on students twelfth grade from one of the Vocational High Schools in Garut, West Java. The results of the observation showed that under the direction of the teacher there are four steps that students took in composing news item text based on a natural disaster news text: 1) students understand the social function, linguistic elements, and structure of the news item text, 2) students open a web news link about natural disaster, 3) students write down the information they get from reading news on the web, 4) students compose a news item text based on the information. Based on the results of document analysis, it showed that the news item text composed by students fulfills the aspects of writing news item text. In addition, based on the results of the questionnaire, it showed that students gave positive responses to the use of authentic material in the form of natural disaster news texts in learning to compose news item texts. Therefore, as an effort for students to achieve the goals of learning English successfully, it is hoped that teachers can use authentic materials as learning media in the classroom.

The Middle-class Card: A Corpus-based Study of the Characteristics of the Middle Class in US News

The ups and downs of the American middle class led several social scientists to rethink and reflect on their conditions, yet the characteristics of how the middle class are represented in the news have been hardly examined from a linguistic perspective. This study used a synergy of tools of Corpus Linguistics and Critical Discourse Analysis, guided by Norman Fairclough’s 3-D model (1989), to investigate the collocation profile of the middle class and the ideology underpinning its representation in US web news discourse from 2010 until 2022. The study relied on a specialized news corpus that had 1,253,678 tokens, including middle class (30,975 times) and middle-class (23,587 times). The findings showed that the US web news was consistent in constructing the characteristics of the middle class during the past twelve years. Under neoliberal policies, the idea of classism is an intrinsic dividing system in American society, and the notion that the middle-class family is an economic unit seems to be ubiquitous. Also, the middle class are depicted as the economic unit that establishes stability and the political card that politicians use in their agendas to win the majority of votes. In spite of the political and economic significance of the middle class, they are mostly passivated by minimizing their human agency and downplaying their roles as doers of social actions. The study can be a part of a branch of Applied Linguistics that focuses on the relationship between the science of economics and the science of language.

국내 산학협력 양상 연구

Objectives This study aims to review the aspects of domestic industry-academic cooperation by keyword network analysis of web data from 2018 to 2021, focusing on impacts of each performer-government, university, industry-on the industry-academia cooperation(IAC) system before and after the COVID-19 phenomenon.
 Methods Keyword network, keyword centrality, ego network, and topic modelling analysis were conducted to examine 1) each influence from the government, university, industry on IAC system, 2) changes its influence after the COVID-19 phenomenon, and 3) the degree to IAC policies were reflected in the aspects of IAC, analysing 5,000 documents collected from web news and blogs.
 Results As a result of keyword network analysis, university, industry, and support project were found to be the top frequent keywords and also at the center of keywords, showing the characteristics of IAC that industry and universities focus on jointly designing and operating government-supported IAC programs. Analysis of ego networks shows that since COVID-19, industry and government networks have grown in size, while recruitment and field practice networks have decreased in size. Keywords such as job creation, SME support, and new industrial talent training, which are major government industry-academic cooperation policies during the text collection period, were partially derived as major areas in topic modeling analysis, but showed different patterns before and after COVID-19.
 Conclusions Government, university, industry were found to be the key players in determining the aspects of the IAC system, and the most influential subject was university over the past four years, actively cooperating with companies in the educational field through government-led development projects. The government's IAC policy for job creation was also reflected in the IAC aspect, but it seems to be limited by the COVID-19 factor. Implications for the role of government, university, and industry in sustaining IAC are discussed.

Data Analysis of the Web News Headlines based on Natural Language Processing

Data Analysis of the Web News Headlines based on Natural Language Processing

OP54 The Early Detection And Warning System ‘SINTESIS-New Technologies’: A Horizon Scanning Experience In Spain

IntroductionSINTESIS-new technologies is the early warning system for new and emerging technologies of the Agencia de Evaluación de Tecnologías Sanitarias, Instituto de Salud Carlos III. SINTESIS is part of the Action Plan for the Early Detection of New and Emerging Technologies of the Spanish Network of Health Technology Assessment Agencies (RedETS). In RedETS, four agencies are responsible for the identification of innovative technologies. These agencies have been collaborating since 2016 according to the early awareness methods contained in the EuroScan Methods Toolkit. SINTESIS focuses on secondary information sources (i.e., experts and literature). This study describes the experience of SINTESIS in identifying and filtering new technologies in recent years.MethodsRetrospective analysis of all new and emerging technologies notified by SINTESIS to RedETS since 2018. Technologies were analyzed on a year-by-year basis for their source of information, the clinical specialties involved, and whether technologies found in the identification phase were selected for further assessment.ResultsBetween 2018–2020, SINTESIS identified 69 emerging and new technologies. Most of the information came from medical press news (35%), and medical web news (22%); other sources included experts (15%), licensing news search (12%), general press (12%), and scientific websites (6%). Almost 37 technologies (54%) were selected for further analysis. Reasons for exclusion included too early identification of technologies/prototypes without enough evidence (52%), technologies already implemented (28%), overlapping technologies between agencies (17%), and not being medical technologies (3%).ConclusionsExperience suggests that news sections of general and medical journals, websites, and expert consultation are useful sources to identify new and emerging health technologies. The main limitation is that the technologies identified are often at too early a stage of development for further assessment. SINTESIS contributes, within a national horizon scanning system with other agencies, to broaden the information sources and provide useful data on early awareness of innovative technologies. Further studies are needed to assess the impact of emerging technologies detection on healthcare delivery.

Tobacco Tax Increases: A Discourse Analysis of the French Print and Web News Media from 2000 to 2020.

Lobbying led by the tobacco industry and tobacconists is a barrier to reducing smoking prevalence in France. Here, we analyze the discourse of the tobacco industry and other key actors (public health agencies, politicians, etc.) in the French general-audience news media from 2000 to 2020 around tobacco tax increases, which is one of the most effective tobacco control measures, especially amongst youth. We queried Europresse (a European news media and specialized press database) using the keywords "increase", "price or taxes", and "tobacco or cigarettes", and found 5409 topic-relevant articles, from which we extracted 8015 arguments for or against the measure. In total,64.3% were against the measure (mostly on grounds of "ufueling the black market"), 32.1% were for the measure (mostly claiming it is "effective at reducing smoking prevalence"), and 1.8% proposed alternative measures. Tobacconists, the primary source of media content on the topic, led a discourse that was strongly opposed to tax increases. Public health agencies, which attracted only half as much media attention, were strongly supportive of the measure. Analysis of discourses relayed in the French general-audience press revealed overwhelming opposition to tobacco tax increases, and this discourse was widely advanced by tobacconists. The results were congruent with international literature that had highlighted a similar set of arguments to those found in the French general press that were broadcasted by the tobacco industry and its allies (tobacconists in France) in an effort to block this evidence-based public health measure.

Political ideology and information technology in government online communication

Government agencies are directed to communicate objective and scientific information to the public, but studies show that political ideology may play a role in how much information governments provide. In this paper I argue that the ideology of a head administrator, and its alignment with agency mission, can restrict the kinds of information that government agencies provide, which may or may not amount to a type of regulatory capture. This impact may also be moderated by the specific media in which the communication takes place. I explore this theory via a case study of the United States Environmental Protection Agency online communication over a period of 32 months, during the years of 2013–2014, under the Democratic Administrator Gina McCarthy, and 2017–2018, under the Republican Administrator Scott Pruitt, via topical terms and document analyses of Twitter posts and web news releases. The information—topics, policies and issues—remain largely consistent across administrations and media channels, but notable distinctions are observed that point to the political ideologies of administrators in office, including a restriction of relevant scientific information on climate change during the misaligned administrator. Moreover, results show differences across media types which may reflect speed and popularity affordances of Twitter compared to website communication. I conclude by discussing the importance of policies to protect against ideological partisanship, and how social media may be better used as tools in government information policy and online communication.

Spatio-Temporal Analysis of Archived Web News for Precise Political Event Detection and Impact Analysis in India's Southern States

Spatio-Temporal Analysis of Archived Web News for Precise Political Event Detection and Impact Analysis in India's Southern States

Professional Social Work and Public Libraries in the United States: A Scoping Review

This study aims to investigate how professional social work is practiced in U.S. public libraries using a scoping review method. Following Arksey and O'Malley's framework for conducting a scoping review, the research team undertook a scoping review of academic literature, such as peer-reviewed studies, academic conference presentations, and dissertations/theses, as well as so-called gray literature, including news articles and web search results. After every author's review, a total of 53 materials were included in the review. The findings from this study provide evidence that collaborations between social work and libraries are documented in the academic and gray literatures, and the collaborations have been rapidly increasing since 2015. The findings also show that these collaborations tend to operate in three main ways: (1) professional social workers and social work interns directly working with library patrons, (2) professional social workers providing consultation with or training to library staff, and (3) social workers providing linkages between library staff and community-based social services agencies.

Using twitter and web news mining to predict the monkeypox outbreak

Using twitter and web news mining to predict the monkeypox outbreak

The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media.

BackgroundVaccination is an important intervention to prevent the incidence and spread of serious diseases. Many factors including information obtained from the internet influence individuals’ decisions to vaccinate. Misinformation is a critical issue and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted on the relationship between the size of the population reached by misinformation and the vaccination decisions made by that population. A number of fact-checking services are available on the web, including the Islander news analysis system, a free web service that provides individuals with real-time judgment on web news. In this study, we used such services to estimate the amount of fake news available and used Google Trends levels to model the spread of fake news. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan.ObjectiveIn this study, we aimed to quantify the impact of the magnitude of the propagation of fake news on vaccination decisions.MethodsWe collected public data about COVID-19 infections and vaccination from Taiwan's official website and estimated the popularity of searches using Google Trends. We indirectly collected news from 26 digital media sources, using the news database of the Islander system. This system crawls the internet in real time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a fake news percentage variable was produced. We used multivariable linear regression, chi-square tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data.ResultsA total of 791,183 news items were obtained over 43 weeks in 2021. There was a significant increase in the proportion of fake news in 11 of the 26 media sources during the public vaccination stage. The regression model revealed a positive adjusted coefficient (β=0.98, P=.002) of vaccine availability on the following week's vaccination doses, and a negative adjusted coefficient (β=–3.21, P=.04) of the interaction term on the fake news percentage with the Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term showed that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the proportion of fake news exceeded 39.3%.ConclusionsThere was a significant relationship between the amount of fake news to which the population was exposed and the number of vaccination doses administered. Reducing the amount of fake news and increasing public immunity to misinformation will be critical to maintain public health in the internet age.

News coverage of social protests in global society

This article links media and social movement studies with world society theory to explain cross-national variations in media attention to domestic social protests. We compile a novel large-scale dataset with over 1.2 million protest-related news articles from 12,644 web news sites across 140 countries/areas in 2015–2020. Our cross-national analysis shows that both media- and country-level characteristics explain news coverage of domestic social protests. Our findings show that web news outlets with high web traffic and a propensity to report conflictual events tend to cover more protests. In addition, web news sites in nations with vibrant civil society organizations report more protest events. We also find that there is a positive relationship between online censorship and news coverage in general. But this is driven by news media in democratic countries, and news sites in authoritarian regimes experiencing strong censorship cover fewer protest events. Finally, news media in authoritarian nations with more organizational ties to the international community cover more domestic protests.

Framing of social protest news in Web portals in Chile and Colombia during 2019

PurposeLate in 2019, massive protest demonstrations rocked both Chile and Colombia. They were an expression of discontent with the economic model and social policies implemented in both countries in recent decades. The purpose of this study is to investigate how Chilean and Colombian news websites framed these social protests and what aspects of the social movements promoted these media to public opinion.Design/methodology/approachThe methodology of this research is empirical; the authors use quantitative and discourse analysis techniques to study the news published by the top three most visited main news websites in each country and deduce the main frameworks emerging from them. After analyzing the framing of reality by media in 4,252 news items, the authors suggest that the main news portals in these Latin American countries not only portrayed the collective actions but also promoted interpretations that did not necessarily reflect the demands of the demonstrations.FindingsThe findings were that in the 2019 protests in Chile and Colombia, the media approach to collective action was not necessarily positive for the activists. This was mainly because framing in the Chilean and Colombian websites had to do with negative aspects of the movements, such as violence or economic costs.Originality/valueThe originality and value of this research is that despite the fact that there are abundant case studies on the relationship between social movements and media framing in the literature, this study adds to the current knowledge because it compares two Latin American countries and focuses on Web news portals.