Web Log Data Research Articles

Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study

Background In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as gross domestic product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offers the potential for more cost-effective, timely, and less biased assessments. Objective This study aimed to develop a model using internet search data to predict well-being indicators at the regional level in Japan, providing policy makers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. Methods This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. Predictor variables included z score–normalized relative search volume (RSV) data from Google Trends for words relevant to each domain. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to predict RWI using RSVs, with α balancing ridge and lasso effects and λ regulating their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root mean square errors (RMSE) and coefficients of determination (R2) were used to assess the model’s predictive accuracy and fit. Results An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from −1.587 to 3.902, with SDs between 3.025 and 0.053. The best Elastic Net model (α=0.1, λ=0.906, RMSE=1.290, and R2=0.904) was built using 2010-2016 training data and 2-13 variables per domain. Applied to 2019 test data, it yielded an RMSE of 2.328 and R2 of 0.665. Conclusions This study demonstrates the effectiveness of using internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost-efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence-based policy making aimed at enhancing well-being.

Experiential education, customer knowledge and adoption of electric vehicles: an exploratory study

ABSTRACT While previous research has primarily explored functional and informational drivers, this study investigates how experiential education can facilitate electric vehicle adoption. Based on the BMW i3 test drive program case and blogs data, this research identifies two types of customers who participate in such programs: early adopters who have a high level of perceptual knowledge about electric vehicles but a low interest in the brand or the model, and the early majority, i.e. users with a moderate level of perceptual knowledge about electric vehicles and are hesitant to adopt it. This research contributes to the literature on customer education by demonstrating the positive role of experiential education in innovation adoption. Additionally, it contributes to the literature on customer knowledge by showing that customers do not participate in experiential education for the same reason. While early adopters compare similar models in the market, the early majority seek answers to technical questions.

Optimizing Spatial Layout of Campsites for Self-Driving Tours in Xinjiang: A Study Based on Online Travel Blog Data

Within the economic and cultural context of Xinjiang, the tourism industry has rapidly developed as a strategic pillar of the national economy, with the self-driving tour market emerging prominently. However, the uneven spatial layout and insufficient service facilities of self-driving camps limit their development potential. This study aims to enhance the attractiveness of tourism in Xinjiang and improve the visitor service experience by constructing an evaluation system for the layout of self-driving camps based on online travel blog data, utilizing methods such as literature review, surveys, ArcGIS spatial analysis, and web text analysis. The Delphi method and entropy weight method were applied to determine the weights of the influencing factors. The findings reveal spatial imbalances in the layout of Xinjiang’s self-driving camps and propose eight preferred scenic areas for camp location. This study also suggests sustainable development strategies. These insights and recommendations aim to optimize the layout of self-driving camps, enhance the tourism experience, and promote the sustainable development of Xinjiang’s tourism industry.

LEVERAGING IOT AND NATURAL LANGUAGE PROCESSING TO IMPROVE WELL-BEING OF CHRONIC PATIENTS

Abstract According to the Centers for Disease Control and Prevention, in the United States, about 80% of older adults - individuals aged 65 years and older, have at least one chronic health condition. Chronic diseases require ongoing medical attention or limit activities of daily living, which in turn, affects quality of life. Heart diseases, cancer, and diabetes are the leading chronic diseases. Literature shows that timely and frequent follow-ups by providers significantly reduce readmissions of chronic patients and contribute to their well-being. However, health care provides do not have adequate bandwidth for regular patient follow-ups, which is vital for chronic patients. One solution for this growing problem, is to leverage smart home monitoring technologies facilitating self-care, which in turn can contribute to patients’ well-being. The goal of this research is to build and leverage an IoT-based smart home monitoring platform and assess its impact on the well-being of chronic patients. The platform included an mHealth mobile app with daily journaling capability, which allowed patients to blog about their symptoms, daily activities, and feelings. Analyzing patients’ daily blog data with Natural Language Processing (NLP) methods, this research revealed a strong correlation between a patients’ blog entries and their well-being score. The results also demonstrated that patients’ well-being scores can be predicted from such journal entries analyzed at real time, collected remotely from their home environment, leading to timely diagnosis and intervention opportunities. A field trial was conducted involving five heart disease and eight cancer patients for 180 days to validate the conclusions.

An Empirical Study of User Engagement in Influencer Marketing on Weibo and WeChat

Understanding social media users’ engagement is one of the most crucial steps in a successful deployment of an influencer marketing campaign. On mainstream social media platforms, opinion leaders are the major channels to spread opinions and media content to a large population of consumers. Therefore, analyzing how users respond to the blogs of opinion leaders lies at the core of understanding user engagement in social media. In this article, to study the factors that have great impacts on user engagement, we first collect a cross-platform opinion leaders’ blogs dataset, which includes 344 643 blogs published by 93 opinion leaders who have accounts on both Weibo and WeChat, the two most popular social media platforms in China. Based on this dataset, we conduct both characteristics study and semantic study to investigate the impact factors of user engagement with respect to blogs of opinion leaders. To find out the associations between user engagement and practically accessible attributes of opinion leaders and their blogs, we adopt state-of-the-art (SOTA) sentiment analysis models to process the blog data, develop a normalization technique to alleviate the issue caused by the heterogeneity of fall-out intervals of blogs, and devise a saliency method to compute the integrated gradients of sentences in blogs. Utilizing these computational tools, we reveal that user engagement on the two platforms agrees on some common factors, such as the number of tokens of blogs. Meanwhile, the two platforms differ in some aspects. For example, the semantic patterns that can improve the level of user engagement on the two platforms are very different. Our analysis can provide advertisers with valuable insights on how to plan an influencer marketing campaign on the two platforms.

Rethinking anxiety and depression for autistic adults through personal narratives: A mixed‐method analysis of blog data

AbstractBackgroundAutistic adults appear to be more vulnerable to anxiety and depression than their neurotypical peers. However, definitions of emotional well‐being that are suitable for autistic adults are missing from this research, along with a missing complete understanding of what contributes to and alleviates negative emotions.MethodsAutistic adults' experiences of emotions were systematically searched for within blog data from 26 autistic authors. The search strategy identified the context of emotions, without adhering to a priori definitions. Corpus‐based and thematic analyses explored the most salient contributing factors and coping responses. Consultation with autistic adults directed the research.ResultsNegative emotions were most salient and were accompanied by qualifying descriptions of being intense and misrepresented by single‐word labels. The impacts of negative emotions were pain and fatigue, disrupted self‐care, housing and employment, and an accumulated toll on self‐identity. Emotional regulation was achieved through monitoring physiological arousal, ownership of sensory and social stressors, investing in immersive activities, planning what to expect from daily life and rejecting deficit‐based views of autism.ConclusionsThe findings offer some explanation for high estimates of anxiety and depression for autistic adults, by illustrating the unsuitability of neuronormative emotion concepts and assessment tools. There are significant implications for clinical practice, insisting on a formulation of difference rather than outdated practice that foregrounds deficit and disorder. We do not suggest that these views reflect the experiences of all autistic people. This study contributes a creative and participatory method to hear the viewpoint of autistic adults. A well‐being resource is shared.

Memory aware optimized Hadoop MapReduce model in cloud computing environment

<p>In the last decade, data analysis has become one of the popular tasks due to enormous growth in data every minute through different applications and instruments. MapReduce is the most popular programming model for data processing. Hadoop constitutes two basic models i.e., Hadoop file system (HDFS) and MapReduce, Hadoop is used for processing a huge amount of data whereas MapReduce is used for data processing. Hadoop MapReduce is one of the best platforms for processing huge data in an efficient manner such as processing web logs data. However, existing model This research work proposes memory aware optimized Hadoop MapReduce (MA-OHMR). MA-OHMR is developed considering memory as the constraint and prioritizes memory allocation and revocation in mapping, shuffling, and reducing, this further enhances the job of mapping and reducing. Optimal memory management and I/O operation are carried out to use the resource inefficiently manner. The model utilizes the global memory management to avoid garbage collection and MA-OHMR is optimized on the makespan front to reduce the same. MA-OHMR is evaluated considering two datasets i.e., simple workload of Wikipedia dataset and complex workload of sensor dataset considering makespan and cost as an evaluation parameter.</p>

Deep Fuzzy Clustering and Deep Residual Network for Prediction of Web Pages from Weblog Data with Fractional Order Based Ranking

Web page recommendation system has attracted more attention in recent decades. The web page recommendation has various characteristics than the classical recommenders. It is the process of predicting the request of the next web page that users are significantly interested while searching the web. It helps the users to find relevant pages in the field of web mining. In particular, web user may spend more time to identify expected information. To understand behavior of users and to visit the page based on their interest at a specific time, an effective web page recommendation method is developed by developed Multi-Verse Sailfish Optimization (MVSFO)-based Deep Residual network. Accordingly, proposed MVSFO is derived by the integration of Multi-Verse Optimizer (MVO) and Sailfish Optimizer (SFO), respectively. Here, the process of recommendation is carried out using weblog data and the web page image. The sequential patterns are acquired from weblog data, and the patterns are grouped with Deep fuzzy clustering based on cosine similarity. The matching process among test pattern and sequential patterns are made using Canberra distance. Here, the recommended web pages obtained from the weblog data and pages obtained from web pages image using the Deep Residual network are enable to generate the output using fractional order-based ranking. The developed scheme attained more effectiveness by the measures, such as F-measure, precision, and recall as 85.30%, 86.59%, and 86.04%, respectively for MSNBC dataset.

Discovery of village resources in urban regeneration project based on big data analytics

In this study, we examined resources at the local level, conducted status research, and explored alternatives based on big data analytics to regenerate a village in a small area in an information communications technology-based urban regeneration project. In particular, we used big data analytics to analyze the current conditions of the local area and provided a case demonstrating how connecting with a local university for expertise could solve current local problems. The main results can be summarized in three dimensions. First, there is job creation in villages using woodworking. At the center of it, it is possible to link with youth entrepreneurship by using the clubs of the local university (Seangnori Research Institute). Quantitatively, it is possible to design offices and offline stores for the sale of developed products, and qualitatively, it can lead to company growth by increasing the business volume of pre-land transportation-type social enterprises and expansion of regional governance through regional exchanges. Second, in terms of providing new information and education, it is possible to provide the Chungnam Appropriate Technology Federation, a non-profit organization in the region, for smooth education and practice. In particular, it is possible to promote the growth of the council through continuous product development. It is possible to strengthen the cooperative system. Third, the effect of the influx of population within the region can be obtained. As publicity and awareness of the visionary workshop project have been expanded, the youth independent talent nurturing process is systematized, and from this, students graduating from local universities can lead to youth jobs in the local area without looking for jobs in other areas, forming a virtuous cycle system. To this end, we divided real estate big data into the categories of system, technology and data, law and policy, structured data of real estate information and unstructured data of social media, web log data, smart device, and real estate policy, development, appraisal, and local analysis to apply to the case area and suggest implications.

블로그 게시물에 나타난 수도권 보전지역 및 자연자원의 분포 및 특성†

This study aimed to evaluate the awareness of conservation areas and green resources and analyze their characteristics by utilizing accumulated blog data created for specific places and objects. Among all the conservation areas and resources located in the Seoul metropolitan area, places that can be evaluated were classified, and sites were evaluated by dividing them into ten categories based on the number of blog posts written. As a result of the study, the users’ awareness of forests was the highest, and the awareness of conservation areas and green resources was higher in urban areas than suburban areas. The result shows that the conservation areas and green resources located around the metropolitan area serve as natural tourist destinations while being the object of conservation for users. In addition, these results are in the same vein as the research results in domestic and foreign studies on the importance of ecosystem services in urban areas. Unlike existing research methods, this study is meaningful in that it identified the level of user awareness through social media analysis and applied it to evaluating conservation areas and green resources. It can be used as basic data to prepare a management plan considering public interest and awareness or to establish a development plan to increase awareness. In addition, the cumulative amount of blog content used in the study is meaningful in that it can identify and monitor users’ interest in the space. However, it was not possible to examine the contents of each blog in detail because it was evaluated based on the amount of social media content. In addition, in the case of conservation areas and green resources, it is necessary to review and supplement the evaluation contents by adding keyword analysis and content analysis for the site to be evaluated as content other than the pure viewpoint of users may be mixed with development issues.

Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China

Exploring the spatial network structure of tourism flow and its influencing factors is of great significance to the transmission of characteristic culture and the sustainable development of tourism in tourist destinations, especially in backward rural areas. Taking Qiandongnan Miao and Dong Autonomous Prefecture (hereinafter referred to as Qiandongnan Prefecture) as an example, this paper adopts social network analysis and Quadratic Assignment Procedure regression analysis to study the network structural characteristics and influencing factors of tourism flow using online travel blog data. The results show that: (1) There are seasonal changes in tourism flow, but the attractions that tourists pay attention to do not change with the seasons. (2) The tightness of the tourism flow network structure is poor. The core nodes are unevenly distributed, and there are obvious structural holes. (3) The density of the tourism flow network is low. There is a clear core–periphery structure in the network, and the core area has a weak driving effect on the periphery area. There are more cohesive subgroups in the network, but the degree of connectedness between the subgroups varies greatly. (4) Geographical adjacency, transportation accessibility, and tourism resource endowment influence tourism flow network structure. The study found that the influencing factors of tourism flow in rural areas are different from those in urban areas. These results provide useful information for the marketing and development of tourism management departments in rural areas.

The Reproducibility Movement in Psychology: Does Researcher Gender Affect How People Perceive Scientists With a Failed Replication?

The reproducibility movement in psychology has resulted in numerous highly publicized instances of replication failures. The goal of the present work was to investigate people’s reactions to a psychology replication failure vs. success, and to test whether a failure elicits harsher reactions when the researcher is a woman vs. a man. We examined these questions in a pre-registered experiment with a working adult sample, a conceptual replication of that experiment with a student sample, and an analysis of data compiled and posted by a psychology researcher on their public weblog with the stated goal to improve research replicability by rank-ordering psychology researchers by their “estimated false discovery risk.” Participants in the experiments were randomly assigned to read a news article describing a successful vs. failed replication attempt of original work from a male vs. female psychological scientist, and then completed measures of researcher competence, likability, integrity, perceptions of the research, and behavioral intentions for future interactions with the researcher. In both working adult and student samples, analyses consistently yielded large main effects of replication outcome, but no interaction with researcher gender. Likewise, the coding of weblog data posted in July 2021 indicated that 66.3% of the researchers scrutinized were men and 33.8% were women, and their rank-ordering was not correlated with researcher gender. The lack of support for our pre-registered gender-replication hypothesis is, at first glance, encouraging for women researchers’ careers; however, the substantial effect sizes we observed for replication outcome underscore the tremendous negative impact the reproducibility movement can have on psychologists’ careers. We discuss the implications of such negative perceptions and the possible downstream consequences for women in the field that are essential for future study.

Experience: Analyzing Missing Web Page Visits and Unintentional Web Page Visits from the Client-side Web Logs

Web logs have been widely used to represent the web page visits of online users. However, we found that web logs in Chrome’s browsing history only record 57% of users’ visited websites, i.e., nearly half of a user’s website visits are not recorded. Additionally, 5.1% of the visits recorded in the web log occur because of unconscious user actions, i.e., these page visits are not initiated from users. We created a Google Chrome plugin and recruited users to install the plugin to collect and analyze the conscious URL visits, unconscious URL visits, and “missing” URL visits (i.e., the visits unrecorded in the traditional web log). We reported the statistics of these behaviors. We showed that sorting popular website categories based on traditional web logs differs from the rankings obtained when including missing visits or excluding unintentional visits. We predicted users’ future behaviors based on three types of training data – all the visits in modern web logs, the intentional visits in web logs, and the intentional visits plus missing visits in web logs. The experimental results indicate that missing visits in web logs may contain additional information, and unintentional visits in web logs may contain more noise than information for user modeling. Consequently, we need to be careful of the observations and conclusions derived from web log analyses because the web log data could be an incomplete and noisy dataset of a user’s visited web pages.

Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms

The accelerated development of e-commerce has been a concern for business people. Business people should be able to gain customer interest in a variety of ways so that their companies can compete with others. Analyzing click-flow data will help organizations or firms assess customer loyalty, provide advertising privileges, and develop marketing strategies through user interests. By understanding consumer preferences, clickstream data analysis may be used to determine who is participating, assist companies in evaluating customer contentment, boost productivity, and design marketing strategies. This research was performed by defining experimental user interests using Dynamic Mining and Page Interest Estimation methods. The findings of this analysis, using three algorithms at the pattern discovery page, demonstrated that the Decision Tree method excelled in both methods. It indicated that the operational performance of the Decision Tree performed well in the assessment of user interests with two different approaches. The findings of this experiment can be used as a proposal for researching the field of web usage mining, collaborating with other approaches to achieve higher accuracy values.

A framework for recommending tourist attractions using deep learning and association rule mining-based methods

Abstract. Many of tourism recommendation researches are based on the user rating and review data on the tourism platforms, and these approaches might be only suitable for a discrete recommendation for the tourist attractions. It is because each rating and review data on the platforms is created for a tourist place, not for multiple places on a travel itinerary. A travel blog data often contains information about the multiple places on a travel itinerary, but it is difficult to analyse the data compared to the rating and review data since it is like a text document having longer text than the review. In this paper, we introduce a framework consisting of a deep learning-based tourist-attraction extraction method from the blog text and an association rule mining-based recommendation method to recommend a list of tourist attractions that might be favourable to visit together in a travel itinerary.

Web Blog Content Curation Using Fuzzy-Related Capsule Network-Based Auto Encoder

The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method.

Extracting Network Patterns of Tourist Flows in an Urban Agglomeration Through Digital Footprints: The Case of Greater Bay Area

Network patterns of tourist flows can reveal differences in tourism resources among destinations from the perspective of network science, providing valuable suggestions for tourism managers and policymakers to promote the balanced and sustainable development of tourism. This paper focuses on urban agglomerations, a highly developed spatial form of integrated cities, and proposes a research framework to extract the network patterns of tourist flows through digital footprints. Based on an illustrative case study using geo-located travel blog data from Qunar.com, we built a tourist flow network for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. The analysis shows: (1) GBA’s tourist flow network is obviously heterogeneous, showing a pattern of “four cores and three poles”; (2) the strong “administrative barrier effect,” revealed by community detection within the network, is the main obstacle to integrating regional tourism; (3) strengthening the infrastructure of tourism mediation cities such as Guangzhou, Zhuhai, and Shenzhen, so as to avoid the “structural hole” caused by the tourist flows between cities, is an urgent issue that the GBA government needs to address. To summarize, the research framework can provide a theoretical basis and concrete suggestions for the planning and management of the tourism industry in urban agglomerations.

A proposed strategy to secure web usage

With much data on the web, it can be difficult, frustrating, and seemingly impossible to find the exact information you need. There are many powerful search utilities on the web are called search engines, in addition to the visitor tracking in a web to study exactly the behavior of the web visitors, to improve the efficiency of that web. This research concentrates on a particular aspect, which is applying Data mining technique especially by association analysis algorithm on the encrypted web log files, that to ensure the privacy of the original data for these files. So since the input data introduced to mining algorithm is encrypted then the resulted association rules are encrypted that to ensure the privacy of the extracted knowledge. Then analyze the decrypted web log data file for the web usage, to study the visitor tracking. According to this study the server configurations and all the services will be improved.

Link-PLSA-LDA: A New Unsupervised Model for Topics and Influence of Blogs

In this work, we address the twin problems of unsupervised topic discovery and estimation of topic specific influence of blogs. We propose a new model that can be used to provide a user with highly influential blog postings on the topic of the user's interest. We adopt the framework of an unsupervised model called Latent Dirichlet Allocation, known for its effectiveness in topic discovery. An extension of this model, which we call Link-LDA, defines a generative model for hyperlinks and thereby models topic specific influence of documents, the problem of our interest. However, this model does not exploit the topical relationship between the documents on either side of a hyperlink, i.e., the notion that documents tend to link to other documents on the same topic. We propose a new model, called Link-PLSA-LDA, that combines PLSA and LDA into a single framework, and explicitly models the topical relationship between the linking and the linked document. The output of the new model on blog data reveals very interesting visualizations of topics and influential blogs on each topic. We also perform quantitative evaluation of the model using log-likelihood of unseen data and on the task of link prediction. Both experiments show that that the new model performs better, suggesting its superiority over Link-LDA in modeling topics and topic specific influence of blogs.

Weblog Analysis for Predicting Correlations in Stock Price Evolutions

We use data extracted from many weblogs to identify the underlying relations of a set of companies in the Standard and Poor (S\&P) 500 index. We define a pairwise similarity measure for the companies based on the weblog articles and then apply a graph clustering procedure. We show that it is possible to capture some interesting relations between companies using this method. As an application of this clustering procedure we propose a cluster-based portfolio selection method which combines information from the weblog data and historical stock prices. Through simulation experiments, we show that our method performs better (in terms of risk measures) than cluster-based portfolio strategies based on company sectors or historical stock prices. This suggests that the methodology has the potential to identify groups of companies whose stock prices are more likely to be correlated in the future.