Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70890

Machine Learning for Readability Assessment and Text Simplification in Crisis Communication: A Systematic Review

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Title:Machine Learning for Readability Assessment and Text Simplification in Crisis Communication: A Systematic Review
Authors:Hansen, Hieronymus
Widera, Adam
Ponge, Johannes
Hellingrath, Bernd
Keywords:Disaster Information, Resilience, for Emergency and Crisis Technologies
crisis communication
machine learning
readability assessment
text simplification
Date Issued:05 Jan 2021
Abstract:In times of social media, crisis managers can interact with the citizens in a variety of ways. Since machine learning has already been used to classify messages from the population, the question is, whether such technologies can play a role in the creation of messages from crisis managers to the population. This paper focuses on an explorative research revolving around selected machine learning solutions for crisis communication. We present systematic literature reviews of readability assessment and text simplification. Our research suggests that readability assessment has the potential for an effective use in crisis communication, but there is a lack of sufficient training data. This also applies to text simplification, where an exact assessment is only partly possible due to unreliable or non-existent training data and validation measures.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70890
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.277
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Disaster Information, Resilience, for Emergency and Crisis Technologies


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