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Knowledge of the mental workload induced by a Web page is essential for improving users’ browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmo-graphy (PPG), electroencephalogram (EEG), temperature and pupil dilation) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves four levels of mental workload. Also, by combining all the sensors, the efficiency of the classification reaches 93.7%.

Accessing the Web from mobile handheld devices has become increasingly common. However, accomplishing that task remains challenging mainly due to the physical constraints of handheld devices and the static presentation of Web pages. Adapting the presentation of Web pages is, therefore, critical to enabling effective mobile Web browsing and information searching. Based on cognitive fit theory and information foraging theory, we propose a novel hybrid approach to adapting Web page presentation that integrates three types of adaptation techniques, namely tree-view, hierarchical text summarization, and colored keyword highlighting. By following the design science research framework, we implemented the proposed approach on handheld devices and empirically evaluated the effects of presentation adaptation on mobile Web browsing. The results show that presentation adaptation significantly improves user performance and perception of mobile Web browsing. We also discover that the positive impact of presentation adaptation is moderated by the complexity of an information search task. The findings have significant theoretical and practical implications for the design and implementation of mobile Web applications.

The development of Web 3.0 is aimed at utilizing available information to enhance users’ experiences of searching, allowing this process to be catered by different users to their own needs. However, websites do not make life easier for blind users. Unconditional situations lead to the frustration and total exclusion of blind users. To overcome the accessibility problems, this study aims to develop a blind user mental model (BlUMM) for browsing websites. We conducted a qualitative empirical study via usability test with structured interviews and task analysis in five different scenarios on a website using JAWS screen reader. Six blind users participated in this study. Their feedback was recorded on a video and transcribed into a GOMS model. From the activities, the participants indicated the challenges they faced to accomplish the tasks. The information obtained was analyzed, and BlUMM was developed based on Nielsen’s usability principles, GOMS model, and Norman’s action cycle model. The methodology was divided into three phases. Phase 1 was the identification of the challenges encountered by blind users while browsing the website. Phase 2 was the analysis of the ability of the blind users to accomplish tasks. Phase 3 involved mapping the blind users’ actions. As a result, the BlUMM was developed with 14 elements, which were set/plan the goal, explore the browser/website, apply the existing mental model, discover new features, select features, predict the feedback, execute the plan, execute the task, perceive the feedback, interpret the feedback, change strategy, accomplish the goal, update the strategy, and implement the new mental model with four stages of user action: planning, selection, execution, and evaluation.

With the wide application of Internet of things (IoT) devices in enterprises, the traditional boundary defense mechanisms are difficult to satisfy the demands of the insider threats detection. IoT insider threat detection can be more challenging, since internal employees are born with the ability to escape the deployed information security mechanism, such as firewalls and endpoint protection. In order to detect internal attacks more accurately, we can analyze users’ web browsing behaviors to identify abnormal users. The existing web browsing behavior anomaly detection methods ignore the dynamic change of the web browsing behavior of the target user and the behavior consistency of the target user in its peer group, which results in a complex modeling process, low system efficiency and low detection accuracy. Therefore, the paper respectively proposes the individual user behavior model and the peer-group behavior model to characterize the abnormal dynamic change of user browsing behavior and compare the mutual behavioral inconsistency among one peer-group. Furthermore, the fusion model is presented for insider threat detection which simultaneously considers individual behavioral abnormal dynamic changes and mutual behavioral dynamic inconsistency from peers. The experimental results show that the proposed fusion model can accurately detect insider threat based on the abnormal user web browsing behaviors in the enterprise networks.

In today's digital era, the internet plays a pervasive role in daily life, influencing everyday activities such as communication, work, and leisure. This online engagement intertwines with offline experiences, shaping individuals' overall well-being. Despite its significance, existing research often falls short in capturing the relationship between internet use and well-being, relying primarily on isolated studies and self-reported data. One major contributor to deteriorated well-being is stress. While some research has examined the relationship between internet use and stress, both positive and negative associations have been reported. This study aimed to identify the associations between an individual's internet use and their stress. We conducted a 7-month longitudinal study. We combined fine-grained URL-level web browsing traces of 1490 German internet users with their sociodemographics and monthly measures of stress. Further, we developed a conceptual framework that allows us to simultaneously explore different contextual dimensions, including how, where, when, and by whom the internet is used. We applied linear mixed-effects models to examine these associations. Our analysis revealed several associations between internet use and stress, varying by context. Increased time spent on social media, online shopping, and gaming platforms was associated with higher stress. For example, the time spent by individuals on shopping-related internet use (aggregated over the 30 days before their stress was measured via questionnaires) was positively associated with stress on both mobile (β=.04, 95% CI 0.00-0.08; P=.04) and desktop devices (β=.03, 95% CI -0.00 to 0.06; P=.09). In contrast, time spent on productivity or news websites was associated with lower stress. Specifically, in the last 30 days of mobile usage, productivity-related use showed a negative association with stress (β=-.03, 95% CI -0.06 to -0.00; P=.04). In addition, in the last 2 days of data, news usage was negatively associated with stress on both mobile (β=-.54, 95% CI -1.08 to 0.00; P=.048) and desktop devices (β=-.50, 95% CI-0.90 to -0.11; P=.01). Further analysis showed that total time spent online (β=.01, 95% CI 0.00-0.02; P<.001), social-media usage (β=.02, 95% CI 0.00-0.03; P=.02), and gaming usage (β=.01, 95% CI 0.00-0.02; P=.02) were all positively associated with stress in high-stress Perceived Stress Scale (PSS>26) individuals on mobile devices. The findings indicate that internet use is associated with stress, and these associations differ across various usage contexts. In the future, the behavioral markers we identified can pave the way for designing individualized tools for people to self-monitor and self-moderate their online behaviors to enhance their well-being, reducing the burden on already overburdened mental health services.

Browsing sessions are rich in elements useful to build profiles of user interests, but at the same time HTML pages include noisy data such as advertisements, navigation menus and privacy notes. Moreover, some pages cover several different topics making it difficult to identify the most relevant to the user. For these reasons, they are often ignored by personalized search and recommender systems. We propose a novel approach for recognizing valuable text descriptions of current user information needs—namely cues —based on the data mined from browsing interactions over the web. The approach combines page clustering techniques based on Document Object Model-based representations for acquiring evidence about relevant correlations between text contents. This evidence is exploited for better filtering out irrelevant information and facilitating the construction of interest profiles. A comparative framework proves the accuracy of the extracted cues in the personalize search task, where results are re-ranked according to the last browsed resources.

To find useful information on the Web, a user must define the search according to their interests, then they must select and analyze one or more web pages, and finally they must decide which content is most useful to them. This process requires visual attention, certain skills, and interaction with the web browser through keyboards, screens, or mice. Web browsing can be difficult for people who have some disability or lack of knowledge in the use of information and communications technology, causing them to stop this activity. This paper proposes a model to facilitate web browsing and contribute to reducing the digital divide among the population. The model input is the user’s request in natural language using voice, and the output, presented in sound, text, or graphic format, is the most suitable content that corresponds to the user’s interests. First, a content search is performed based on the user’s context. Subsequently, among the results obtained, the most appropriate for the user are identified by analyzing the context of web pages. We implemented a prototype, which was evaluated by users. The results show that it reached an acceptable usability level and that 84.75% of users obtained relevant results in their interactions.

Discovering time-related transaction behavior or patterns is helpful for businesses in suggesting appropriate products to their customers. For web systems, it is important to understand customers' browsing behavior to design or recommend products or services that customers need. This study proposes an approach for predicting web browsing behavior that integrates the concepts of sequential data mining, Borda majority count, bit-string operation, and PrefixSpan algorithm. By incorporating the concept of Borda majority count and sequential data mining, the proposed approach can discover majority-based priorities of items for recommendation and improve prediction accuracy. In addition, the proposed approach employs the concept of bit-string operation and the PrefixSpan algorithm to increase computational efficiency. This research employs the concept of ensemble methods that combine multiple models to derive improved results. Compared to previous methods, the proposed approach can yield higher prediction accuracy. Moreover, the proposed approach can provide flexibility for decision-makers in adjusting a minimum support level and the number of items for recommendation. The proposed approach can also be applied to many fields.