How Do Home Computer Users Browse the Web?

With the ubiquity of web tracking, information on how people navigate the internet is abundantly collected yet, due to its proprietary nature, rarely distributed. As a result, our understanding of user browsing primarily derives from small-scale studies conducted more than a decade ago. To provide an broader updated perspective, we analyze data from 257 participants who consented to have their home computer and browsing behavior monitored through the Security Behavior Observatory. Compared to previous work, we find a substantial increase in tabbed browsing and demonstrate the need to include tab information for accurate web measurements. Our results confirm that user browsing is highly centralized, with 50% of internet use spent on 1% of visited websites. However, we also find that users spend a disproportionate amount of time on low-visited websites, areas with a greater likelihood of containing risky content. We then identify the primary gateways to these sites and discuss implications for future research.

Internet Addiction and the Pattern of Internet Use among Under Graduate Medical Students: A Cross- Sectional Study from North India

Introduction: Excessive use of the Internet affects the academic achievements of students. This study aimed to investigate the prevalence of Internet addiction and the pattern of Internet use among undergraduate medical students. Method: This analytical cross-sectional study conducted on 177 undergraduate medical students in batch 2016, 2017 and 2018, who were included in this study by convenience sampling method. The study tool was Young’s Internet Addiction Test containing questions regarding the pattern of Internet use. Data entry and analysis was done by using Microsoft excel and SPSS version 21. Qualitative data were described in terms of frequency and percentage while quantitative data were expressed as mean and standard deviation. Chi square test and multiple linear regression were used to find out the relationship between various factors and Internet Addiction (IA). The significance level was considered 0.05. Results: The mean age of the participants was 20.3 years (± 1.19), and 62 % of the subjects were males. The prevalence rate of Internet Addiction (IA) was found to be 56.5% and 42.9% of them were in mild and 13.6% were in moderate addiction category. Being male (β= -0.143, p=0.038), staying at a hostel (β= 0.167, p=0.018), not having a time preference for using the Internet (β= -0.174, p=0.012), spending more time on the Internet everyday (β= 0.201, p=0.000), being always online (β= 0.276, p=0.000) and more years of using the Internet (β= 0.175, p=0.015) were significantly related with IA in students. Conclusion: A large proportion of medical students were found to be addicted to the Internet. Therefore, this issue should be addressed immediately.

Internet Use, Users, and Cognition: Determination of the Cognitive Relationships Between Internet-Based Technology and Internet Users

This study aims to investigate growing Internet use in relation to cognition. Existing literature suggests human capability to utilize the Internet as an external (transactive) memory source. Formational mechanisms of such transactive memory systems and comparative effects of Internet use on transactive memory and semantic memory are both relatively unknown points of research explored in this study.This study comprises two experimental memory task surveys, confirming and yielding findings in memory research. Semantic memory is negatively affected by notions of information saved online. An adaptive dynamic is also revealed—1) as users often have a vague idea of desired information before searching for it on the Internet, first accessing semantic memory serves as an aid for subsequent transactive memory use and 2) successful initial transactive memory access eliminates the need for subsequently accessing semantic memory for desired information. Internet users form and reinforce transactive memory systems with the Internet by repeatedly defaulting to first accessing semantic memory then transactive memory or to accessing transactive memory only, and decrease reliance on transactive memory systems by repeatedly defaulting to only semantic memory. Users have some degree of control over transactive memory systems they engage in, a phenomenon to be potentially explored in future research directions.