scholarly journals A Study of the Reflection of Current Russian-Cuban Relationship in the Content on the Online News of the Cuban News Agency Prensa Latina

2021 ◽  
Vol 6 (1) ◽  
Keyword(s):  
Online News ◽  
News Agency

Understanding Fatal Crash Reporting Patterns in Bangladeshi Online Media using Text Mining

2021 ◽  
pp. 036119812110142
Author(s):  
Subasish Das
Keyword(s):  
Media Coverage ◽  
News Coverage ◽  
Strong Support ◽  
Online News ◽  
World Health ◽  
Health Concern ◽  
Traffic Fatalities ◽  
Traffic Crashes ◽  
News Agency ◽  
Fatal Crash

Traffic crashes are a major public health concern. In 2016, traffic crashes resulted in over 1.35 million deaths worldwide. In Bangladesh alone, the number of reported traffic fatalities was 2,376 in 2016. However, the World Health Organization estimated that the true number of traffic fatalities in Bangladesh ranges between 20,730 and 29,177. Editorial traffic crash reports in Bangladesh, and the number of crashes that are reported, vary widely among different media outlets. This study employed a Google News Alert to collect fatal crash reports from online English daily newspapers. The current study compiled a database of 419 fatal crash-related reports over a six-month period (November 2018–April 2019). The reports contain a total of 81,019 words. The results of this study reveal that online news coverage of traffic fatalities tends to vary from news agency to news agency. Furthermore, these reports do not usually cover key contributing factors of crash occurrences; the geometric features of crash occurrence sites are rarely reported. The findings demonstrate the importance of deciphering media coverage to develop potential safety risk measures in Bangladesh. The current findings provide strong support for the need for guidelines to help media outlets adequately document fatal crash reports.

scholarly journals The representation of the Syrian conflict in the Syrian online news: A critical discourse analysis

2018 ◽  
Vol 53 ◽  
pp. 02003
Author(s):  
Kais Amir Kadhim
Keyword(s):  
Discourse Analysis ◽  
Critical Discourse Analysis ◽  
Semantic Network ◽  
Online News ◽  
Critical Discourse ◽  
Social Actors ◽  
News Agency ◽  
Research Questions ◽  
Syrian Conflict ◽  
Foreign Fighters

This study investigates the usage of Syrian media tools in manipulating people’s perception towards the uprising in Syria. The language of the Syrian media is analyzed using a critical discourse analysis. For this purpose, two frameworks are combined: the historical approach by Wodak (2002) and the socio-semantic network of social actors by Van Leeuwen (2008). The study particularly focuses on the way the Syrian government and the rebels are represented in the Syrian online news Cham press which is a pro-government news agency. The two research questions of this study are: (i) What are the referential and predication strategies used by Cham press in reporting the Syrian conflict? (ii) How do these strategies reflect the ideology that surrounds the representation of rebels in the Syrian news? Three hundred and ninety-seven articles were selected from Cham press based on the most relevant keywords. These articles were taken for a period of six months that is from June to October 2012 and May 2013. The study reveals that the Syrian uprising is negatively represented using terms with negative connotations like terrorism, foreign conspiracy, against the law, foreign fighters that are targeting people and responsible for the massacres that happened in the country.

The Effect of Searching Versus Surfing on Cognitive and Emotional Responses to Online News

2009 ◽  
Vol 21 (2) ◽  
pp. 49-59 ◽  
Author(s):  
Kevin Wise ◽  
Hyo Jung Kim ◽  
Jeesum Kim
Keyword(s):  
Heart Rate ◽  
Skin Conductance ◽  
Skin Conductance Level ◽  
Emotional Responses ◽  
Online News ◽  
Self Report ◽  
Mixed Design ◽  
Information Need ◽  
News Stories ◽  
Rate Acceleration

A mixed-design experiment was conducted to explore differences between searching and surfing on cognitive and emotional responses to online news. Ninety-two participants read three unpleasant news stories from a website. Half of the participants acquired their stories by searching, meaning they had a previous information need in mind. The other half of the participants acquired their stories by surfing, with no previous information need in mind. Heart rate, skin conductance, and corrugator activation were collected as measures of resource allocation, motivational activation, and unpleasantness, respectively, while participants read each story. Self-report valence and recognition accuracy were also measured. Stories acquired by searching elicited greater heart rate acceleration, skin conductance level, and corrugator activation during reading. These stories were rated as more unpleasant, and their details were recognized more accurately than similar stories that were acquired by surfing. Implications of these results for understanding how people process online media are discussed.

ANALISIS WACANA PADA BERITA NONTON FILM SAKINAH, MAHASISWA UIN YOGYAKARTA BERI PUJIAN (ANALISIS MIKROSTRUKTURAL)

2019 ◽  
Vol 7 (4) ◽  
pp. 57
Author(s):  
Resdianto Permata Raharjo ◽  
Maranita Anjarsari
Keyword(s):  
Data Analysis ◽  
Online News ◽  
Analysis Method ◽  
Literature Study ◽  
Lexical Cohesion ◽  
News Discourse ◽  
Qualitative Descriptive ◽  
Descriptive Approach ◽  
Method Analysis ◽  
Descriptive Method

This study aims to describe 1) the form of cohesion contained in the news Watching Sakinah Movies, UIN Yogyakarta Give Praise Students and 2) describing the forms of coherence contained in the news Watching Sakinah Film, Students of UIN Yogyakarta Give Praise. The subjects used in this study were news of watching Sakinah film, UIN Yogyakarta students giving praise, and the objects used were sentences containing grammatical cohesion, a form of lexical cohesion. The approach used in this study is a qualitative descriptive approach. The data in this study are sentences in the news of Watching Sakinah Movies, This student is a method of literature study. While the method of data analysis in this study is descriptive method analysis method, the method used to analyze and describe cohesion markers and analyze markers of coherence. Test The results of the study show that in the news of watching Sakinah films, UIN Yogyakarta Beri Pujian students have varied markers of cohesion and coherence in the Tebuireng Online news discourse. Cohesion markers were found to reference (3), substitution (1), ellipsis (2), conjunction (3), collocation (1), and markers of coherence found cause-effect relationships (2), relationship coherence suggestions — goals (1), coherence temporal relations (1), and coherence of causal relationships (1).

Role of The Halal Industry in Recovering the National Economy in Covid-19 Pandemic

2020 ◽  
Vol 8 (2) ◽  
pp. 202-214
Author(s):  
Cucu Susilawati
Keyword(s):  
National Economy ◽  
Banking Sector ◽  
Developed Countries ◽  
Online News ◽  
Financial Industry ◽  
National Economic ◽  
Halal Food ◽  
Vigorous Growth ◽  
Business Sectors

The outbreak of the Covid-19 pandemic in Indonesia is attacking not only public health but also the economy. The presence of Covid-19 has many important impacts on developed countries. There are at least four industries most impacted by this pandemic, including households, MSMEs, companies and the financial industry. However, the halal industry is believed to be more resilient to the Covid-19 pandemic. This durability is because of the principles attributed to the halal sector, namely the importance of fairness, balance and openness. The author’s goal is therefore to carry out more in-depth research on the role of the halal industry in supporting the national economy, which is under pressure because of the COVID-19 pandemic. This type of study is a literature review with a material analysis approach that explores the conditions of the halal industry in Indonesia in depth. The material received is as books, published information, and online news. The findings of this study reveal that there are three halal business sectors that are believed to be more vulnerable to the Covid-19 pandemic in order to facilitate national economic recovery. Halal finance, halal food and halal fashion industries are among them. Halal finance from both the banking sector and the Islamic stock market has proved to be more robust than the mainstream financial sector. Besides guaranteed halal food, its wellbeing is also guaranteed, and halal fashion is now on the rise as Muslim fashion is increasingly innovative and global. We believe the three of them to have experienced vigorous growth, and also to continue to draw customers. And also after the Covid-19 pandemic, these three sectors could survive. Thus the halal industry also contributes to Indonesian economy.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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