Recommendations for Reporting Web Usage Studies

2011 ◽  
pp. 181-204 ◽  
Author(s):  
Kirstie Hawkey ◽  
Melanie Kellar
Keyword(s):  
Contextual Information ◽  
Experimental Methodology ◽  
Prior Work ◽  
Web Browsing ◽  
User Characteristics ◽  
Web Usage ◽  
Browsing Behavior ◽  
Proper Context ◽  
Collection Methods ◽  
The Web

This chapter presents recommendations for reporting context in studies of Web usage including Web browsing behavior. These recommendations consist of eight categories of contextual information crucial to the reporting of results: user characteristics, temporal information, Web browsing environment, nature of the Web browsing task, data collection methods, descriptive data reporting, statistical analysis, and results in the context of prior work. This chapter argues that the Web and its user population are constantly growing and evolving. This changing temporal context can make it difficult for researchers to evaluate previous work in the proper context, particularly when detailed information about the user population, experimental methodology, and results is not presented. The adoption of these recommendations will allow researchers in the area of Web browsing behavior to more easily replicate previous work, make comparisons between their current work and previous work, and build upon previous work to advance the field.

Traversal Pattern Mining in Web Usage Data

2008 ◽  
pp. 2004-2021
Author(s):  
Jenq-Foung Yao ◽  
Yongqiao Xiao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

Traversal Pattern Mining in Web Usage Data

2004 ◽  
pp. 335-358 ◽  
Author(s):  
Yongqiao Xiao ◽  
Jenq-Foung (J.F.) Yao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

scholarly journals Online News in India: Appraising the Digital News Consumption Landscape in theWorld’s Largest Democracy (2014-2018)

2021 ◽  
Author(s):  
Subhayan Mukerjee
Keyword(s):  
Online News ◽  
Indian Society ◽  
Web Browsing ◽  
News Consumption ◽  
News Sources ◽  
Internet Users ◽  
Digital News ◽  
Different Types ◽  
Browsing Behavior ◽  
The Web

How do people in the world's largest democracy consume online news? This article reports findings from the analysis of a novel empirical dataset tracking the web-browsing behavior of more than 50,000 Indian internet users over 45 months. In doing so, it seeks to understand the digital news consumption landscape of a crucial, but understudied context and appraise the prominence and longitudinal trends of the audience share of different types of news sources in the online Indian space. It finds that while digital-born media have not contested the hegemony of legacy media, regional vernacular media have suffered significant declines in their audience shares. The article proposes the concept of audience mobility, using it to identify qualitatively distinct dynamics in how vernacular audiences in India have migrated to national vis-à-vis international outlets. The findings are discussed in light of contemporary changes in Indian society that is characterized by increasing digitization and literacy.

scholarly journals Extraction of Knowledge from Web Server Logs Using Web Usage Mining

2019 ◽  
Vol 8 (S3) ◽  
pp. 12-15
Author(s):  
B. Harika ◽  
T. Sudha
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Server ◽  
Primary Source ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Usage ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

Taxonomical Classification of Web Usage Mining Applications and its Ontological Representation

2018 ◽  
Vol 7 (3) ◽  
pp. 39-43
Author(s):  
Satyaveer Singh ◽  
Mahendra Singh Aswal
Keyword(s):  
Web Usage Mining ◽  
Web Usage ◽  
Navigation Patterns ◽  
Product Recommendations ◽  
Taxonomical Classification ◽  
The Comparative Study ◽  
The Web ◽  
Real World Problems

Web usage mining is used to find out fascinating consumer navigation patterns which can be applied to a lot of real-world problems, such as enriching websites or pages, generating newly topic or product recommendations and consumer behavior studies, etc. In this paper, an attempt has been made to provide a taxonomical classification of web usage mining applications with two levels of hierarchy. Further, the ontology for various categories of the web usage mining applications has been developed and to prove the completeness of proposed taxonomy, a rigorous case study has been performed. The comparative study with other existing classifications of web usage mining applications has also been performed.

Assessing Triangulation Across Methodologies, Methods, and Stakeholder Groups: The Joys, Woes, and Politics of Interpreting Convergent and Divergent Data

2018 ◽  
Vol 41 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Rebecca Campbell ◽  
Rachael Goodman-Williams ◽  
Hannah Feeney ◽  
Giannina Fehler-Cabral
Keyword(s):  
Large Scale ◽  
Contextual Information ◽  
Policy Makers ◽  
Specific Data ◽  
Qualitative And Quantitative ◽  
Stakeholder Groups ◽  
Data Points ◽  
Evaluation Project ◽  
Collection Methods

The purpose of this study was to develop triangulation coding methods for a large-scale action research and evaluation project and to examine how practitioners and policy makers interpreted both convergent and divergent data. We created a color-coded system that evaluated the extent of triangulation across methodologies (qualitative and quantitative), data collection methods (observations, interviews, and archival records), and stakeholder groups (five distinct disciplines/organizations). Triangulation was assessed for both specific data points (e.g., a piece of historical/contextual information or qualitative theme) and substantive findings that emanated from further analysis of those data points (e.g., a statistical model or a mechanistic qualitative assertion that links themes). We present five case study examples that explore the complexities of interpreting triangulation data and determining whether data are deemed credible and actionable if not convergent.

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