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4 - Evaluating Web Impact

Published online by Cambridge University Press:  20 December 2023

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Summary

Introduction

The web provides the opportunity for metrics to be used for a wide range of investigations, most of which may be broadly categorised as either evaluative or relational. This chapter takes a general view of evaluative metrics for websites, blogs and other self-hosted content; Chapter 5 considers evaluative metrics for third-party social media services that a large number of individuals and organisations make use of; and Chapter 6 considers relational web metrics. In many ways the split between self-hosted and third-party hosted content is an artificial one. For example, millions of blogs are hosted on third-party sites such as Blogger.com, while individuals and organisations do not have to rely on a microblogging service such as Twitter, but could host their own microblogging service using open source software such as Mastodon (https://joinmastodon.org). The split reflects a significant difference in the control a user has over the data that is available and an uncertainty in the data that is collected.

When people or organisations host their own content on their own servers the only limitations on the information that they gather about the use of content on their site are the technical problems to be overcome. However, when a website is integrated into the wider ecosystem there is more uncertainty: in the same way that there is no simple answer to how many web pages exist, there is not a simple answer to how many links a website has pointing to it or how many times it is mentioned. This is a problem that is made more difficult as the web is necessarily viewed through the limited view of a particular search engine or other tool, and the limitations of the tool are not always explicit. In comparison, many of the large social network sites provide a restricted environment where only certain behaviour is allowed (because of editorial and technical restrictions) and only certain data may be accessed. These restrictions also reduce the ambiguity in the data available from the services; a Twitter account has a known number of followers, a Facebook user has a known number of friends, a YouTube video has a known number of views. We may disagree with the concept of Facebook's ‘Like’ button or what counts as a view on YouTube, but these are known discernible units.

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  • Evaluating Web Impact
  • David Stuart
  • Book: Web Metrics for Library and Information Professionals
  • Online publication: 20 December 2023
  • Chapter DOI: https://doi.org/10.29085/9781783305681.005
Available formats
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To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Evaluating Web Impact
  • David Stuart
  • Book: Web Metrics for Library and Information Professionals
  • Online publication: 20 December 2023
  • Chapter DOI: https://doi.org/10.29085/9781783305681.005
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Evaluating Web Impact
  • David Stuart
  • Book: Web Metrics for Library and Information Professionals
  • Online publication: 20 December 2023
  • Chapter DOI: https://doi.org/10.29085/9781783305681.005
Available formats
×