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

Hansen Hieronymus, Widera Adam, Ponge Johannes, Hellingrath Bernd


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.

Keywords
Disaster Information; Resilience; crisis communication; machine learning; readability assessment; text simplification



Publication type
Forschungsartikel in Sammelband (Konferenz)

Peer reviewed
Yes

Publication status
Published

Year
2021

Conference
54th Hawaii International Conference on System Science HICSS

Venue
Hawaii, USA

Start page
2265

End page
2274

Language
English

ISBN
978-0-9981331-4-0

DOI

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