Blog Information Research Articles

A Narrative Review of Commercial Platforms Offering Tracking of Heart Rate Variability in Corporate Employees to Detect and Manage Stress.

The COVID-19 pandemic has resulted in employees being at risk of significant stress. There is increased interest by employers to offer employees stress monitoring via third party commercial sensor-based devices. These devices assess physiological parameters such as heart rate variability and are marketed as an indirect measure of the cardiac autonomic nervous system. Stress is correlated with an increase in sympathetic nervous activity that may be associated with an acute or chronic stress response. Interestingly, recent studies have shown that individuals affected with COVID will have some residual autonomic dysfunction that will likely render it difficult to track both stress and stress reduction using heart rate variability. The aims of the present study are to explore web and blog information using five operational commercial technology solution platforms that offer heart rate variability for stress detection. Across five platforms we found a number that combined HRV with other biometrics to assess stress. The type of stress being measured was not defined. Importantly, no company considered cardiac autonomic dysfunction because of post-COVID infection and only one other company mentioned other factors affecting the cardiac autonomic nervous system and how this may impact HRV accuracy. All companies suggested they could only assess associations with stress and were careful not to claim HRV could diagnosis stress. We recommend that managers think carefully about whether HRV is accurate enough for their employees to manage their stress during COVID.

Spreading mechanism of Weibo public opinion phonetic representation based on the epidemic model

Public opinion is the abbreviation of public ideas, which is the sum of the beliefs, attitudes, opinions and emotions expressed by the masses about various phenomena in the society. Network public opinion is not only the mapping of public opinion in the network, but also the direct response of social public opinion. Weibo public opinion has gradually become an important medium for people to obtain public opinion in time. The outbreak of Weibo public opinion is very likely to cause social repercussions, which will even affect people’s trust in the government and even social stability. The scale of the Internet is expanding, the number of users is increasing, and the applications provided by the Internet are becoming more and more popular. In this context, Weibo public opinion analysis has become an important research topic. Facing the rapid growth of micro blog information, how to obtain the hot topics of micro blog public opinion timely, comprehensively and accurately is the primary problem to be solved in the process of micro blog public opinion analysis. Based on the infectious disease model, this paper proposes an analysis model of Weibo public opinion communication. The speech analysis model is proposed to extract the information for the processing, and the representation of the data is applied to improve the algorithm efficiency. The experimental results compared with the state-of-the-art have proven the satisfactory performance.

Sedition Law and the Bloggers’ Freedom of Expression in Malaysia

The advancement of Information Technology has transformed the landscape of communication to the new medium of communication. Therefore, blog becomes one of the main communication mediums for a blogger in Malaysia. Bloggers write blog posts, share their likes and dislikes. They voice their opinions; provide suggestions, report news, and form groups in the Blogosphere. However, many bloggers were arrested and prosecuted in court under Sedition Law. This hinders the bloggers’ freedom of expression which is protected by Article 10(1) of the Federal Constitution of Malaysia. The objective of this article is to explore the impact of sedition law in the era of Information Technology for bloggers in practicing their freedom of expression in Malaysia. The qualitative research design has been adopted in analyzing three selected blogs and bloggers who were investigated and prosecuted in court. The authors submit that the Sedition Law does not override bloggers’ freedom of expression since the freedom itself is not originally absolute. This study will create awareness among bloggers on the importance of their blog information and the limitations of their freedom of expression.

RETRACTED: The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

An intelligent personalized web blog searching technique using fuzzy-based feedback recurrent neural network

In web information retrieval, every single user has a unique contextual objective when searching for information on web blogs. Based on the given query, the task of the web search engine is to fetch the most related information from the collection of web blogs. In order to enhance the searching ability, efficient semantic matching models, richer training, and evaluation resources are required. The conventional keyword-based search algorithms have a minimum efficiency in knowing users’ intentions compared to machine learning algorithms. Recently, neural networks are well-recognized in information retrieval due to the ability of vector representation learning. This paper proposes an adaptive fuzzy feedback recurrent neural network-based web blog searching technique which follows inverse filtering (IF) algorithm using Word2Vec representation. Initially, the user query is pre-processed, and then given to the IF for accelerating the search process. Inside the IF, the web blog content is labelled according to their blog information and stored in the hash table, and then relevant contents are extracted by TF–IDF; this result is given to the similarity estimation to obtain the similarity score. Finally, the proposed technique considers the feedback got from users and performs re-ranking by fuzzy-based RNN to achieve rich user intention satisfaction. The implementation is performed in Python, and statistical measures such as accuracy, precision, and recall are utilized for performance evaluation. The results demonstrate that the proposed technique has enhanced accuracy (94%) compared to conventional techniques, namely deep auto-encoder, deep neural networks, and artificial neural networks.

텍스트 분석의 신뢰성 확보를 위한 스팸 데이터 식별 방안

최근 뉴스, 블로그, 소셜미디어 등을 통해 방대한 양의 비정형 텍스트 데이터가 쏟아져 나오고 있다. 이러한 비정형 텍스트 데이터는 풍부한 정보 및 의견을 거의 실시간으로 반영하고 있다는 측면에서 그 활용도가 매우 높아, 학계는 물론 산업계에서도 분석 수요가 증가하고 있다. 하지만 텍스트 데이터의 유용성이 증가함과 동시에 이러한 텍스트 데이터를 왜곡하여 특정 목적을 달성하려는 시도도 늘어나고 있다. 이러한 스팸성 텍스트 데이터의 증가는 방대한 정보 가운데 필요한 정보를 획득하는 일을 더욱 어렵게 만드는 것은 물론, 정보 자체 및 정보 제공 매체에 대한 신뢰도를 떨어뜨리는 현상을 초래하게 된다. 따라서 원본 데이터로부터 스팸성 데이터를 식별하여 제거함으로써, 정보의 신뢰성 및 분석 결과의 품질을 제고하기 위한 노력이 반드시 필요하다. 이러한 목적으로 스팸을 식별하기 위한 연구가 오피니언 스팸 탐지, 스팸 이메일 검출, 웹 스팸 탐지 등의 분야에서 매우 활발하게 수행되었다. 본 연구에서는 스팸 식별을 위한 기존의 연구 동향을 자세히 소개하고, 블로그 정보의 신뢰성 향상을 위한 방안 중 하나로 블로그의 스팸 태그를 식별하기 위한 방안을 제안한다. Recently, tremendous amounts of unstructured text data that is distributed through news, blogs, and social media has gained much attention from many researchers and practitioners as this data contains abundant information about various consumers' opinions. However, as the usefulness of text data is increasing, more and more attempts to gain profits by distorting text data maliciously or nonmaliciously are also increasing. This increase in spam text data not only burdens users who want to obtain useful information with a large amount of inappropriate information, but also damages the reliability of information and information providers. Therefore, efforts must be made to improve the reliability of information and the quality of analysis results by detecting and removing spam data in advance. For this purpose, many studies to detect spam have been actively conducted in areas such as opinion spam detection, spam e-mail detection, and web spam detection. In this study, we introduce core concepts and current research trends of spam detection and propose a methodology to detect the spam tag of a blog as one of the challenging attempts to improve the reliability of blog information.

The science grapevine: Influence of blog information on the online media coverage of the 2010 arsenic-based life study

Informal chatter has always been part of the scientific community, but with the emergence of online blogs these casual debates have become public. Here, I present a case study of how blog information influenced the online news frames during the December 2010 coverage of the arsenic-based life study. The results show how media frames were homogeneous and transformed abruptly from a positive ‘Gee Whiz’ frame towards a critical frame and lastly to a meta media frame over the course of a few days. From beginning to end, the blogosphere heavily influenced the news content, use of sources and spread of information between news outlets. Such rapid and uncontrollable influence of blogosphere information on online news coverage can be explained theoretically as an example of science grapevine communication.

Ease Into Social Media as Receptive Communication

Ease Into Social Media as Receptive Communication

Interest Mining Algorithm Based on Blog Information

Recently, the amount of blogs on the Internet rises sharply. Hence, mining valuable information in blogs possesses realistic significance for improving user experience, network services, etc. This paper proposes a mining algorithm with blog authors' interests based on classification techniques, which introduces an evaluation standard of non-empty intersection. This algorithm can also improve the hit ratio of recommendation service based on blog authors' interests by means of the interest collection from expanding prediction; therefore, it can reach a higher degree of satisfaction. In addition, this paper performs experiments with the data set from Sina Blog and NetEase Blog, whose result illustrates the higher accuracy of our algorithm. Keywords: Blog mining; Interest mining; Classification algorithm; Non-empty intersection.

Medical Student Dermatology Interest Groups

The Dermatology Interest Group (DIG) at the University of Texas Medical Branch (UTMB) blog (digutmb.blogspot.com) was created in 2004 with the aims of increasing communication and collaboration among students, faculty, residents, and alumni, promoting educational opportunities, and fostering the missions for which DIG was created. This blog is unique, because its frequent activity is directed toward the educational and professional needs of medical students and residents. We assessed the use of this blog by evaluating the number of blog views and audience members with relationship to the number of posts and post content over time via a tracking system. We found that there has been an increase in blog posts, views, and subscribers, as well as in areas of post content including dermatology resources/news/articles, residency applications, and resident-related information. Usefulness of such posts expands beyond UTMB students, which increases blog views and widens viewer audience. An international viewer population also was evaluated. Recorded blog viewing time was 1 minute, 57 seconds, which is more time than needed to read a post, suggesting use of additional blog information. This review of the DIG at the UTMB blog demonstrates how the use of web-based tools, in addition to the inherent benefits of medical student interests groups, are valuable resources for students, residents, and faculty.

Defining What Matters When Preserving Web-Based Personal Digital Collections: Listening to Bloggers

User-generated content (UGC) has become a part of personal digital collections on the Web, as such collections often contain personal memories, activities, thoughts and even profiles. With the increase in the creation of personal materials on the Web, the needs for archiving and preserving these materials are increasing, not only for the purpose of developing personal archives but also for the purpose of capturing social memory and tracking human traces in this era. Using both survey and interview methods, this study investigated blogs, one popular type of UGC, and analyzed travel bloggers’ perceptions of the value of blogs and the elements of blogs that are important for preservation. The study respondents found personal and sentimental value (e.g., a way to express themselves, a way to keep personal memories and thoughts, and a way to maintain a record for their family) to be the most important reason for preserving blogs, followed by informational value and cultural/historical value. Sharing also appeared as one of the values that respondents found in their blogs. The respondents reported that self-created blog posts (content) and information related to the blog posts (context) are more important to preserve than some other elements (behavior and appearance). Integrating what bloggers consider as most valuable and what archivists think are worth preserving may be an important step when collecting personal blogs.

Monitoring Propagations in the Blogosphere for Viral Marketing

Even though blog contents vary a lot in quality, the disclosure of personal opinions and the huge blogging population always attracts marketing∂ s attention on blog information. In this paper, we investigate how marketers can identify the information propagation in degree among blog communities. In this way, topic similarity, relatedness, and word repetition between leader and followers∂ writing products are considered as the propagated information. The contribution of this paper is twofold. The work presented here is to show how blog content can be economically and feasibly analyzed by existing internet sources such as Wikipedia database and the usage of page return from a Japanese search engine. To this extent, this system, which combined in-link algorithms and text mining analyzes, tracing propagation channels and propagateable information allows analyzing the power of influences in viral marketing. We demonstrated the effectiveness of the system by applying blogger identification, topic identification, and the topic propagations.

A rank-based Prediction Algorithm of Learning User's Intention

Abstract Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query “Palace” contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

Verification of a destination image influence model for Taiwan bicycle tourism blogs for Chinese tourists

The impact of information technology increases day by day. The bicycle tourism industry in Taiwan has started to actively adopt internet applications in its development. The content of internet information in commercial behavior is an even more important development issue. This study constructed the following influence models: blog information recommendation, website experience, message trust, and destination image, and 388 Chinese residents that had visited Taiwanese bicycle tourism blogs in 2010 were used as research subjects. A structural equation model (SEM) was applied to test the model hypothesis. The results verified that blog information recommendation, message trust, and website experience all directly and positively influenced the destination image awareness of Chinese tourists. The study then discussed the research results and proposed several suggestions regarding the management aspect and research methods. Key words: Bicycle, blogs, destination image.

Fashion blogs: too trendy for libraries or useful resources?

Do your students look at blogs? Do you see them as information sources? Are they part of your library’s resources? This article summarises some recent research on fashion blogs, both ‘amateur’ and ‘professional’, and their use in providing information to fashion students. It highlights the potential usefulness of subject blogs, based on points that arose from the data collected from fashion librarians and students in interviews, an online survey and a round-table discussion. Hopefully the results will encourage you to consider your own stance on blog information in the library, whichever subject you support.

芥川賞・直木賞が受賞作品の売上とBlog 上に与える影響に関する研究

芥川賞・直木賞が受賞作品の売上とBlog 上に与える影響に関する研究

Collaborative E-Learning Using Semantic Course Blog

Collaborative e-learning delivers many enhancements to e-learning technology; it enables students to collaborate with each other and improves their learning efficiency. Semantic blog combines semantic Web and blog technology that users can import, export, view, navigate, and query the blog. We developed a semantic course blog for collaborative e-learning. Using our semantic course blog, instructors can import the lecture course. Students can team up for projects, ask questions, mutually discuss problems, take the comments, support answers, and query the blog information. This semantic course blog provided a platform for collaborative e-learning framework. In this chapter, we described some collaborative e-learning and semantic blog technology, and then we introduced functions, implementation and how collaborative e-learning appears in semantic course blog.

Deconstructing blogs

PurposeA growing amount of information available on the web can be classified as contextual information, putting already existing information into a new context rather than creating isolated new information resources. Blogs are a typical and popular example of this category. By looking at blogs from a more context‐oriented view, it is possible to deconstruct them into structures which are more contextual than just focused on the content, facilitating flexible reuse of these structures: that is the aim of this paper.Design/methodology/approachThe paper looks at the underlying structures of blogs and blog posts, representing them as multi‐ended links. This alternative representation of blogs and blog posts allows us to represent them as reusable information structures. This paper presents blogs as a popular content type, but the approach of restructuring Web 2.0 content can be extended to other classes of information, as long as they can be regarded as being mainly contextual.FindingsBy deconstructing blogs and blog posts into their essential properties, it can be shown how there is a simple and universal representation for blogs. This representation allows the reuse of blog information across specific blog or blogging platforms, and can even go beyond blogs by representing other web content which provides context.Originality/valueThis paper presents a novel approach for mapping a popular web content type to a simple and universal representation. The value of such a unified representation lies in exposing the structural similarities among blogs and blog posts, and making them available for reuse.