Whatever people produce on digital media can be a relevant source of knowledge and behavioural analysis. This is the subject of interest of a wide part of the new discipline known as Web Science. However, special care must be exercised when setting up studies on this kind of sources. Indeed, these studies rarely satisfy the established scientific method guidelines, because of the nature and size of the data, as well as because of the bias and scarce generalizability of results. This paper identifies some of the most crucial challenges that need to be addressed when tackling knowledge extraction and data analysis out of observational studies on human-generated content.
Myths and Challenges in Knowledge Extraction and Big Data Analysis on Human-Generated Content from Web and Social Media Sources
Marco Brambilla
2017-01-01
Abstract
Whatever people produce on digital media can be a relevant source of knowledge and behavioural analysis. This is the subject of interest of a wide part of the new discipline known as Web Science. However, special care must be exercised when setting up studies on this kind of sources. Indeed, these studies rarely satisfy the established scientific method guidelines, because of the nature and size of the data, as well as because of the bias and scarce generalizability of results. This paper identifies some of the most crucial challenges that need to be addressed when tackling knowledge extraction and data analysis out of observational studies on human-generated content.File | Dimensione | Formato | |
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