Evolution of user navigation behavior for online news

Purpose The purpose of the study is to use web serer logs in analyzing the changes of user behavior in reading online news, in terms of desktop and mobile users. Advances in mobile technology and social media have paved the way for online news consumption to evolve. There is an absence of research into the changes of user behavior in terms of desktop versus mobile users, particularly by analyzing the server logs. Design/methodology/approach In this paper, the authors investigate the evolution of user behavior using logs from the Malaysian newspaper Berita Harian Online in April 2012 and April 2017. Web usage mining techniques were used for pre-processing the logs and identifying user sessions. A Markov model is used to analyze navigation flows, and association rule mining is used to analyze user behavior within sessions. Findings It was found that page accesses have increased tremendously, particularly from Android phones, and about half of the requests in 2017 are referred from Facebook. Navigation flow between the main page, articles and section pages has changed from 2012 to 2017; while most users started navigation with the main page in 2012, readers often started with an article in 2017. Based on association rules, National and Sports are the most frequent section pages in 2012 and 2017 for desktop and mobile. However, based on the lift and conviction, these two sections are not read together in the same session as frequently as might be expected. Other less popular items have higher probability of being read together in a session. Research limitations/implications The localized data set is from Berita Harian Online; although unique to this particular newspaper, the findings and the methodology for investigating user behavior can be applied to other online news. On another note, the data set could be extended to be more than a month. Although initially data for the year 2012 was collected, unfortunately only the data for April 2012 is complete. Other months have missing days. Therefore, to make an impartial comparison for the evolution of user behavior in five years, the Web server logs for April 2017 were used. Originality/value The user behavior in 2012 and 2017 was compared using association rules and Markov flow. Different from existing studies analyzing online newspaper Web server logs, this paper uniquely investigates changes in user behavior as a result of mobile phones becoming a mainstream technology for accessing the Web.

Multiple evidence combination for web site search using server log analysis

In this thesis, a novel method is proposed to improve the retrieval performance by using web server logs. Web server logs are grouped into different sessions and then terms are extracted for each page in the session, meanwhile weights of terms are calculated. A new representation of web page from user's perspective is generated after going through the entire log. The new representation and the anchor-based representation are combined with original text-based representation. Two combination methods: combination of document representations and combination of ranking scores are investigated. In the experiments, three measurements are employed to evaluate the performance and the results show that for Cosine Similarity model, the highest improvement on top-10 precision is around 38%, for Okapi model, the hightest improvement is around 13%, for TFIDF model, the highest improvement is around 48% and for Indri model, the highest improvement is around 17%.

Multiple evidence combination for web site search using server log analysis

In this thesis, a novel method is proposed to improve the retrieval performance by using web server logs. Web server logs are grouped into different sessions and then terms are extracted for each page in the session, meanwhile weights of terms are calculated. A new representation of web page from user's perspective is generated after going through the entire log. The new representation and the anchor-based representation are combined with original text-based representation. Two combination methods: combination of document representations and combination of ranking scores are investigated. In the experiments, three measurements are employed to evaluate the performance and the results show that for Cosine Similarity model, the highest improvement on top-10 precision is around 38%, for Okapi model, the hightest improvement is around 13%, for TFIDF model, the highest improvement is around 48% and for Indri model, the highest improvement is around 17%.

Data Mining applied on Web Robots Detection: A Systematic Mapping

Browsing on Internet is part of the world population’s daily routine. The number of web pages is increasing and so is the amount of published content (news, tutorials, images, videos) provided by them. Search engines use web robots to index web contents and to offer better results to their users. However, web robots have also been used for exploiting vulnerabilities in web pages. Thus, monitoring and detecting web robots’ accesses is important in order to keep the web server as safe as possible. Data Mining methods have been applied to web server logs (used as data source) in order to detect web robots. Then, the main objective of this work was to observe evidences of definition or use of web robots detection by analyzing web server-side logs using Data Mining methods. Thus, we conducted a systematic Literature mapping, analyzing papers published between 2013 and 2020. In the systematic mapping, we analyzed 34 studies and they allowed us to better understand the area of web robots detection, mapping what is being done, the data used to perform web robots detection, the tools, and algorithms used in the Literature. From those studies, we extracted 33 machine learning algorithms, 64 features, and 13 tools. This study is helpful for researchers to find machine learning algorithms, features, and tools to detect web robots by analyzing web server logs.

HOPE eIntervention: Uptake and engagement in an online sexual health intervention among African-American young adults (Preprint)

BACKGROUND African American young adults have low rates of uptake and engagement with health technologies, which may further widen sexual health inequalities. OBJECTIVE We examined factors influencing uptake and engagement for a consumer health informatics (CHI) intervention for HIV/STI prevention among African American young adults using the diffusion of innovation theory, the trust-centered design framework and O’Brien and Toms’ model of engagement. METHODS This community-based participatory research, mixed-methods study included surveys at four time points (N=315; 280 African-American) of young adults aged 18 to 24 involved in an HIV/STI prevention intervention described as “parties”. Qualitative interviews were conducted with a subset of participants (N=19) after initial surveys, website server logs, and social media accounts indicated low uptake and engagement. A generalized linear mixed-effects model identified predictors of eIntervention uptake, server logs were summarized to describe use over time, and interview transcripts were coded and thematically analyzed to identify factors affecting uptake and engagement. RESULTS Self-reported eIntervention uptake was low, but increased significantly over time, Demographic factors and HIV/STI-related behaviors were not significantly correlated with uptake. The most frequent activity was visiting the website, followed by visiting the Facebook page. Factors driving uptake were the desire to share HIV/STI prevention information with others, trust in the intervention, and gender homophily. Factors undermining uptake were personal and group distrust online. Factors driving initial engagement were audience-targeted website aesthetics and appealing visuals; long-term engagement was impeded by insufficiently frequent updates. CONCLUSIONS To encourage uptake, CHI interventions for African-American young adults can leverage users’ desire to share information about HIV/STI prevention with others. Ensuring implementation through trusted organizations is also important, though there is a need for vigorous promotion. Visual appeal and targeted content foster engagement at first, but ongoing engagement may require continual content changes. A thorough analysis of CHI intervention use can inform the development of future interventions in order to promote uptake and engagement. To guide future analyses, we present an expanded uptake and engagement model for CHI interventions targeting African American young adults based on the empirical results presented here.

Evaluation of Autopsy and Volatility for Cybercrime Investigation

In this article, the authors successfully created two new plugins one for Autopsy Forensic Tool, and the other for Volatility Framework. Both plugins are useful for encoding digital evidences in Forensic Lucid which is the goal of this work. The first plugin was integrated in Autopsy to generate a report for the case of a Brute Force Authentication attack by looking for evidence in server logs based on a key search. On the other hand, the second plugin named ForensicLucidDeviceTree aims to find whether a device stack has been infected by a root-kit or not expression is implied by the previous statement. The results of both plugins are shown in Forensic Lucid Format and were successfully compiled using GIPC compiler.

Examination of Users Conduct in Arrange Online Shoping Website

Online searching is converting into delivered further to delivered commonplace in our day-to-days stay. Comprehending humans' price of pastimes and furthermore conduct is vital so that you can adjust buying net internet websites to customers' goals. The statistics concerning human beings' conduct is keep on within the net net server logs. The evaluation of such statistics has centered on the usage of facts dealing with techniques everywhere a as an opportunity static characterization is used to model humans' conduct and furthermore consequently the gathering of the movements performed through them isn't regularly idea-about. Therefore, incorporating a have a have a look at of the method adhered to with the resource of the use of clients at some point of a session are commonly of accurate interest to become aware of greater complex interest styles. To cope with this hassle, this paper proposes a linear-temporal reasoning model analyzing method for the evaluation of set up e-trade internet logs. By way a criterion technique of mapping log data consistent with the ecommerce framework, net logs are commonly simply repair proper into event logs any place the practices of customers is recorded. Then, without a doubt amazing predefined questions are typically performed to discover numerous assignment styles that do not forget about the numerous movements completed thru the usage of manner of a consumer in a few unspecified time in the destiny of a consultation. Finally, the top exceptional of the deliberate technique has sincerely been researched thru making use of it to a actual case have a observe of a Spanish e-change net net internet net website online online. The consequences have understood exciting findings which have actually created workable to suggest a few improvements in the net net net internet web page on-line style with the purpose of growing its effectiveness