Machine Learning for Readability Assessment and Text Simplification in Crisis Communication: A Systematic Review
Hansen Hieronymus, Widera Adam, Ponge Johannes, Hellingrath Bernd
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.
Disaster Information; Resilience; crisis communication; machine learning; readability assessment; text simplification