An Unsupervised Approach to User Characterization in Online Learning and Social Platforms

Dan Vilenchik

doi:10.1007/978-3-030-59177-9_2

An Unsupervised Approach to User Characterization in Online Learning and Social Platforms

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Making sense of data that is automatically collected from online platforms such as online social media or e-learning platforms is a challenging task: the data is massive, multidimensional, noisy, and heterogeneous (composed of differently behaving individuals). In this chapter we focus on a central task common to all on-line social platforms and that is the task of user characterization. For example, automatically identify a spammer or a bot on Twitter, or a disengaged student in an e-learning platform.

Original language	American English
Title of host publication	Mathematics (Education) in the Information Age
Editors	S.A. Costa, M. Danesi, D. Martinovic
Place of Publication	Cham
Publisher	Springer
Pages	15-36
Number of pages	22
ISBN (Electronic)	978-3-030-59177-9
ISBN (Print)	978-3-030-59176-2
DOIs	https://doi.org/10.1007/978-3-030-59177-9_2
State	Published - 11 Dec 2020

Publication series

Name	Mathematics in Mind
Publisher	Springer

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10.1007/978-3-030-59177-9_2

Cite this

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An Unsupervised Approach to User Characterization in Online Learning and Social Platforms

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