News Web Pages Research Articles

Eye-Tracking Analysis: Examining the Impact of Fan Culture and Entertainment News on Engagement with Social Justice Information

Celebrity culture and idolatry are prevalent nowadays. The development of online social media facilitates the distribution of entertainment news, fan culture has become increasingly prevalent. On the other hand, the extreme fan culture brought negative impacts to people which affects their psychological and physical status. This also leads to a negative stereotype of fan culture, i.e. addicted to entertainment news and not concern themselves of public affairs. This study aims to investigate the impact of fan culture on individuals’ attention to public affairs. Social justice news refers to news that reports and reveals inequality, injustice and oppression in society. Public attention to social justice news can help promote fairness and justice, social progress and change. This study utilized eye-tracking to collect data to investigate the effect of idolatry on social justice news attention. In the experiment, subjects were randomly divided into two groups; Group A subjects would view images of news web pages containing entertainment news and social justice news, while Group B subjects’ material consisted of social justice news and other types of news. Subjects were asked if they are a fan of any celebrity after the trial. The entire browsing process was recorded by an eye-tracker. The eye-tracking data revealed that, generally, people paid less attention to social justice news than to entertainment news and other unrelated topics. This is evidenced by shorter total fixation duration (TFD), fewer fixation counts (FC), and longer times to first fixation (TFF) for social justice news. Analysis of the two categories of subjects’ gender and whether or not they were stargazers revealed that, contrary to the stereotypes commonly perceived by the general public, fans and females would pay more attention to social justice information, exhibiting more TFD and FC. The present study’s use in this area of research. The public to abandon stereotypes and view fans and females in a fair and rational way. At the same time, the media should increase the exposure and weight of social justice news and direct viewers to pay more attention to social news.

Online Patient Education Materials on Iron Deficiency Anemia Are Too Difficult to Read and Low Quality: A Readability and Quality Analysis.

Introduction Patients increasingly rely on online health information to understand and manage their diseases. Concerns about the quality and readability of these materials have been reported in the literature. Poor quality and difficult-to-read information lead to delayed diagnoses and adverse outcomes. We assessed the quality and readability of online health information about iron deficiency anemia (IDA) on Google. Method We searched for online web pages using the term "iron deficiency anemia"on Google. One hundred and twelve out of 200 web pages were included. We assessed web page typology, readability, the Journal of the American Medical Association (JAMA) score, the DISCERN score, and the Health on the Net Foundation certification (HONcode). Statistical analysis was performed with R version 4.2.2. Result Non-profit and scientific journal web pages were the most common. Scientific journal web pages were of the highest quality. News web pages were the most readable. The first Google Page web pages did not have greater JAMA scores or lower Flesch-Kinkaid Reading Grade Level (FKGL) and Simple Measure of Gobbledygook (SMOG) scores. Forty-six percent of all web pages were high-quality. Web pages on the first Google page were more likely to have HONCode certification. Conclusion We highlight gaps in the readability and quality of online information about IDA. Online web pages exceeded the recommended reading level for patients. Most web pages were low quality; only a quarter were HONcode-certified; and the first Google page web pages were not higher in quality than the later web pages on search.

Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news.

Webpage Intelligent Parsing Algorithm Based on Text and Symbol Density

Web page intelligent parsing is an inevitable part of data collection. News web pages contain a lot of information with little relevance to the topic, which makes it difficult to locate the text content directly and quickly during the data collection process. This paper proposes a web page intelligent parsing algorithm based on text and symbol density. Through empirical research on the pages of mainstream news websites in China, the algorithm can quickly and accurately extract the text of news web pages.

Automatic Web Page Classification System with Improved Accuracy

Nowadays, the Internet contain s a wide variety of online documents, making finding useful information about a given subject impossible, as well as retrieving irrelevant pages. Web document and page recognition software is useful in a variety of fields, including news, medicine, and fitness, research, and information technology. To enhance search capability, a large number of web page classification methods have been proposed, especially for news web pages. Furthermore existing classification approaches seek to distinguish news web pages while still reducing the high dimensionality of features derived from these pages. Due to the lack of automated classification methods, this paper focuses on the classification of news web pages based on their scarcity and importance. This work will establish different models for the identification and classification of the web pages. The data sets used in this paper were collected from popular news websites. In the research work we have used BBC dataset that has five predefined categories. Initially the input source can be preprocessed and the errors can be eliminated. Then the features can be extracted depend upon the web page reviews using Term frequency-inverse document frequency vectorization. In the work 2225 documents are represented with the 15286 features, which represents the tf-idf score for different unigrams and bigrams. This type of the representation is not only used for classification task also helpful to analyze the dataset. Feature selection is done by using the chi-squared test which will be in the task of finding the terms that are most correlated with each of the categories. Then the pointed features can be selected using chi-squared test. Finally depend upon the classifier the web page can be classified. The results showed that list has obtained the highest percentage, which reflect its effectiveness on the classification of web pages.

Proximate or Primary Source? How Multiple Layers of News Sources on Social Media Predict News Influence

News shared on social media presents multiple layers of sources, from reputable news organizations to individual users who share news on social media. A web-based experiment investigated (a) whether the influence of a primary news source (news organization) on viewers decreases as it becomes less proximate with the presence of a more immediate source (individual user who shared news), and (b) if so, how the evaluations of both sources, along with a varying degree of issue relevance, affect viewers’ agreement with news position. Participants read one news article either shared on Facebook by a well-known celebrity or directly posted onto a news website. The perceived credibility of news organizations predicted viewers’ agreement with the news position, but only when the news was presented on a news web page so that the news organization was shown as the proximate source. When multiple sources were displayed, the influence of news organization credibility disappeared when the given news lacked personal relevance.

A bilingual news webpage ideology translation

Background: Cultural problem commonly arising in translation is related to the cultural differences between the two languages involved. However, deciding what ideology should be used in the translation process is complicated.
 Purpose: This paper aims to give the explanation to the readers about the type and dominant ideology of translation in the article of www.Berita2Bahasa.com.
 Design and methods: To answer the purpose, this study uses the descriptive method to analyze those articles. The data were gathered based on news from May 29 – June 3, 2017. To analyze the data, first step is deciding the right topic, writer chooses the article. The writer also prepares the theories to support the analysis
 Results: This study revealed that in each article of www.Berita2Bahasa.com, the translator uses 2 type ideology of translation, those are Domestication and Foreignization Ideology. Most of articles is dominantly used domestication Ideology.

Let Me Make You Happy, and I'll Tell You How You Look Around: Using an Approach-Avoidance Task as an Embodied Emotion Prime in a Free-Viewing Task.

The embodied approach of human cognition suggests that concepts are deeply dependent upon and constrained by an agent's physical body's characteristics, such as performed body movements. In this study, we attempted to broaden previous research on emotional priming, investigating the interaction of emotions and visual exploration. We used the joystick-based approach-avoidance task to influence the emotional states of participants, and subsequently, we presented pictures of news web pages on a computer screen and measured participant's eye movements. As a result, the number of fixations on images increased, the total dwell time increased, and the average saccade length from outside of the images toward the images decreased after the bodily congruent priming phase. The combination of these effects suggests increased attention to web pages' image content after the participants performed bodily congruent actions in the priming phase. Thus, congruent bodily interaction with images in the priming phase fosters visual interaction in the subsequent exploration phase.

Chinese College Students’ Source Selection and Use in Searching for Health-related Information Online

This lab-based experimental study examined college students’ source selection and use behaviors when searching for health-related information using a major Chinese search engine, Baidu.com. Participants searched for four health-related tasks, with two being receptive tasks and two critical tasks. Five types of sources were identified from students’ search logs: Social Q&A webpages (QA), Professional Health webpages (PH), News webpages (NW), Wiki-type webpages (WT), and Other. Students’ source selection and use behaviors were distinguished based on their clicking and writing behaviors during the search process. The results showed that Chinese college students heavily rely on sources in Baidu's ecosystem, with Baidu Knows, Baidu Library, Baidu Scholar, Baidu Experience, and Baidu Baike all being selected and used frequently. Overall, among all five types of sources, students selected and used Social Q&A webpages the most frequently, but the likelihood of Wiki-type webpages to be used after being selected was the highest. Professional Health Webpages specialized in providing professional medical and health care information were less selected and used than expected. Task types had a significant impact on source selection and use. Students selected and used more Wiki-type webpages for receptive tasks and more Social Q&A and News webpages to address critical tasks. More efforts are needed to improve Chinese college students’ eHealth literacy and optimize the current online health information environment.

How hospitals in mainland China responded to the outbreak of COVID-19 using information technology-enabled services: An analysis of hospital news webpages.

ObjectiveMany countries have implemented quarantine rules during the global outbreak of coronavirus disease 2019 (COVID-19). Understanding how hospitals can continue providing services in an effective manner under these circumstances is thus important. In this study, we investigate how information technology (IT) helped hospitals in mainland China better respond to the outbreak of the pandemic.Materials and MethodsWe conducted a content analysis of pages published on the websites of the top 50 hospitals in mainland China between January 22 and February 21, 2020. In total, we analyzed 368 pages that the hospitals published during the initial days of the COVID-19 pandemic. The purpose was to identify common themes related to the utilization of IT by these hospitals in response to the pandemic’s outbreak.ResultsWe identified 5 focal themes across the webpages published by the hospitals during our study period, including (1) popular medical science education, (2) digitalized hospital processes, (3) knowledge management for medical professionals, (4) telemedicine, and (5) new IT initiatives for healthcare services. Our analysis revealed that Chinese hospitals spent greater effort in promoting popular medical science education in the initial stages of our study period and more on telemedicine in the latter stages.DiscussionWe propose a configurational approach for hospitals to design response strategies to pandemic outbreaks based on their available resources.ConclusionsOur study provides rich insights for hospitals to better utilize their IT resources and some recommendations for policymaker to better support hospitals in the future.

Web page filtering for kids

Internet has become integral part of everyday life of children, grownup, schools, colleges, institutions and business etc. Facilities like email, social networking, tweeting, online purchase, searching are the most popular activities amongst every generation. However, the lack of ownership common agreement about the precise approach to educating and protecting children has resulted in further challenges. The abundance of unethical information available to a child and its ill effect on child psychology is a matter of great concern. In this paper a system is proposed that filters the unethical, harmful content blocks from the web page. The system focus on eliminating the bad content (unethical) and restructuring the web page to provide user with the clean web page. The cosine measure of similarity is used for comparing the web page block content, image alternate text and image tooltip with the dictionary of objectionable words. Empirical evaluation shows that approach worked incredible when applied to general news web pages from popular media websites.

Old and Young Users’ White Space Preferences for Online News Web Pages

In the future, the importance and usage rate of the Internet will increase gradually year by year. However, in the case of frequently browsed news web pages, mass news information derived from visual design elements or different layout designs may affect the preferences of users from different demographics and socio-economic backgrounds. This is due to the dissimilarities between their physical and psychological limitations. The purpose of this study is to investigate users' white-space ratio preferences for news web pages. We tested the top ten online Chinese and English websites, 20 news web pages in total. Two statements were picked from the system usability scale (SUS) and the visual aesthetics of website inventory (VisAWI) to evaluate these samples. We took these two statements as questionnaire questions. They are the following: Q1 “I think that I would like to use this system frequently” and Q2 “Everything goes together on these web pages.” The design of the questionnaire was based on a five-point Likert scale. The research variables include age, gender, education, occupation, white-space ratio, along with the other demographic properties. Our results indicated that there are significant differences between age, white-space ratio, education, occupation, news sources, computer usage time, and computer usage history. The group aged between 31-45 years old scored all samples higher than other groups. None prefer too high (90%) or too low (50%) white-space ratio. It is necessary to accumulate more preferences from different groups prior to the coming age of AI and machine learning.

Race and Resistance Amid Feminism, Priming, and Capitalism: The (surprisingly-globalized) Visual of an Asian American Woman Activist

See that photo above of the three Asian Americans holding protest signs? I am the feminist Asian American ciswoman in the right corner supporting the yellowish-green sign. This picture has circulated on Chicago television programs, various print newspapers, and international news webpages. But why was this photo the one that became associated with this particular protest? In this article, I use critical autoethnography, informed by critical race theory and intersectional feminist theory, to examine the origin, selection, and circulation of the above visual of this protest and how this photo is related to race, feminism, activism, and resistance.

Construction of Multimedia Teaching Resource Management Evaluation Model in the Big Data Environment

In view of the deficiencies in the studies of the multi-media teaching resource management method and resource management evaluation model, a multi-media teaching resource management evaluation model in the big data environment is proposed in this paper based on the existing work. The teaching resource management evaluation method of the proposed model is further studied based on the elaboration of the component elements of the model. Guided by the multimedia teaching resource management evaluation model, the construction method for the multimedia teaching resource management evaluation model is studied in the open big data environment. And the multimedia remote teaching data of 360 encyclopedia and news web pages are used as the basis for experimental verification. The results show that the proposed model and method can effectively evaluate the multimedia teaching resource management.

One Method of Keyword Extraction for Tibetan News Webpage for Tibetan News Webpage

Keywords of Tibetan text is important in the area of text text clustering/categorization, automatic abstracting, IR and so on. However, there are no keywords in the Tibtan news WebPages. Besides, many algorithm for keywords extraction need the manually annotated corpus, so it is poor augment ability. Because Keywords can be considered as a set of words which are important and subject correlated cohesively in a document, this paper improved the CHI-Squared Statistic, use the idea of recommendation to extact keywords. Experiments from Tibtan news webpages demonstrate that this method is better than the method of TFIDF integrating with location information.

Content extraction from news web pages using tag tree

As the web endures to develop, there is an enormous amount of information which is typically designed for its users, which makes it difficult to extract relevant data from numerous sources. In this paper, we propose an approach for extracting the main content from news web pages. Our approach is based on the concept of tokenisation of HTML page, these tokens construct the tag tree; web pages from different websites are parsed into Tag tree and generated a template from each web pages and discover matching patterns and multiple sequence alignment. It finds and removed shared token sequences from the web pages until the relevant information is extracted from them. We perform experiments on 500 web pages from ten different news websites. Experimental results show that our approach efficiently extracts the relevant information.

Measuring web page complexity by analyzing TCP flows and HTTP headers

To understand website complexity deeply, a web page complexity measurement system is developed. The system measures the complexity of a web page at two levels: transport-level and content-level, using a packet trace-based approach rather than server or client logs. Packet traces surpass others in the amount of information contained. Quantitative analyses show that different categories of web pages have different complexity characteristics. Experimental results show that a news web page usually loads much more elements at more accessing levels from much more web servers within diverse administrative domains over much more concurrent transmission control protocol (TCP) flows. About more than half of education pages each only involve a few logical servers, where most of elements of a web page are fetched only from one or two logical servers. The number of content types for web game traffic after login is usually least. The system can help web page designers to design more efficient web pages, and help researchers or Internet users to know communication details.

Media and political agenda setting: the case of mental health policy in Lithuania

Media has a very specific role in forming agenda of mental health policy. In their reports, journalists directly question systemic issues, trajectories of the mental health policy and respective legislation. Media also acts as a mediator between citizens and politicians rendering understanding to the latter that their electorate is scared and supports increasing means of guardianship and control. Finally, media can ignore important outcomes of mental health policy. This strategy gives an account that mental health issues are of low importance and public scrutiny is not needed. The data for the context and content analysis derived from two main sources: online news webpage (time frame covers year 2000–2011, 1353 cases) and Parliament records of official proceedings (year 1990–2010, 567 cases). Media discourse proved to have a significant influence on political agenda, since in most cases political discussions on the topic of mental health were referred to media sources. Research data suggests that media coverage as well as the political debates on mental health are characterised by general stagnation, superficial understanding and stereotypical attitudes. This overall pattern was breached during the period of 2004–2006 which was marked by significant international events, namely the EU entrance and adoption of the Mental Health Declaration for Europe. During this period the policy of mental health received considerable attention in the media, decreased media articles related to criminalisation of mental health. The research identified major players that were able to break the vicious circle in the media and political discourse around the marginalised and stigmatised subject.

Web News Extraction via Tag Path Feature Fusion Using DS Theory

Contents, layout styles, and parse structures of web news pages differ greatly from one page to another. In addition, the layout style and the parse structure of a web news page may change from time to time. For these reasons, how to design features with excellent extraction performances for massive and heterogeneous web news pages is a challenging issue. Our extensive case studies indicate that there is potential relevancy between web content layouts and their tag paths. Inspired by the observation, we design a series of tag path extraction features to extract web news. Because each feature has its own strength, we fuse all those features with the DS (Dempster-Shafer) evidence theory, and then design a content extraction method CEDS. Experimental results on both CleanEval datasets and web news pages selected randomly from well-known websites show that the F1-score with CEDS is 8.08% and 3.08% higher than existing popular content extraction methods CETR and CEPR-TPR respectively.

An Efficient Multiclass Classifier Using On-Page Positive Personality Features for Web Page Classification for the Next Generation Wireless Communication Networks

Over the years, wireless communication networks have been widely used by a large community of users in wide variety of applications such as intelligent transportation systems, energy management, safety, and security etc. But, during this era, due to large number of user's request, there may be a performance bottleneck in some part of the network with respect to various QoS parameters such as congestion and network delay. Hence, there is a requirement of an efficient classification technique to reduce congestion in the network so that throughput of various applications can be increased. Classification helps in searching, sorting, retrieval, and querying of a document for the wireless networks. World Wide Web (WWW) contains huge repository of information in the form of web pages. However, size of Internet is growing day-by-day. The huge repository of information poses challenge to collect and process the relevant related information of a particular domain. So, traditional text classification techniques are difficult to apply on the rapidly growing web-based contents. Hence, novel approaches and techniques need to be devised to reduce the manual efforts in web page classification. Keeping focus on these points, this paper proposes a novel approach for multiclass classifier based on unique personality features of the web page of particular domain category for the next generation wireless networks. Personality features are collected and assigned weights in the proposed scheme. Then, the proposed classifier is trained based on these special features. Results obtained depict that proposed classifier successfully classified news domain pages, education, resume, online shopping, and research web pages from large database repository. Accuracy of the proposed classifier is found to be satisfactory from a large data set of different categories. Also, there is a 10---15 % overall performance gain using the proposed scheme in comparison to the other existing schemes.