• Thomas BLASCHKE - Josef STROBL - Julia WEGMAYR (Eds.)

GI_Forum 2021, Volume 9, Issue 1

12th International Symposium on Digital Earth

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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.


Starting 2016, GI_Forum publishes two issues a Year.
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GI_Forum 2021, Volume 9, Issue 1

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8947-3
Online Edition



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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2,
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: bestellung.verlag@oeaw.ac.at
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Spatially Supervised Text Mining for Social Media Cleaning and Preprocessing

    Martin Werner

GI_Forum 2021, Volume 9, Issue 1, pp. 68-75, 2021/06/29

12th International Symposium on Digital Earth

doi: 10.1553/giscience2021_01_s68

doi: 10.1553/giscience2021_01_s68


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doi:10.1553/giscience2021_01_s68



doi:10.1553/giscience2021_01_s68

Abstract

In this paper, we show a framework for partial bot rejection based on spatially supervised text mining from social media messages. We show qualitative results towards the reduction of known bots and give hints on how this cleaning technique can help us in filling gaps of current signals related to human life on Earth based on social media. The bot rejection framework is based on using a spatial signal for supervising a machine learning model with extreme label noise still being able to reject some of the unwanted components of the social media stream. Furthermore, we comment that such models show significant biases and can, therefore, not be used responsibly without bias analysis and mitigation per application.

Keywords: social media analysis, text mining, data cleaning

Published Online: 2021/06/29 09:45:59

Object Identifier: 0xc1aa5572 0x003c9b56

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Rights: https://creativecommons.org/licenses/by-nd/4.0/