A Cross-lingual Natural Language Processing Framework for Infodemic Management
This provides a tool for health systems to proactively disseminate accurate information during epidemics, though it is incremental as it combines existing NLP techniques.
The authors tackled the problem of managing COVID-19 misinformation by developing a cross-lingual NLP framework that matches daily news with WHO guidelines, achieving top performance with a LexRank summarizer on Word2Vec embeddings and Word Mover distance after evaluating 36 models.
The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artificial Intelligence. Hence, we have exploited the potential of Natural Language Processing for identifying relevant information that needs to be disseminated amongst the masses. In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by matching daily news with trusted guidelines from the World Health Organization. The proposed pipeline deploys various techniques of NLP such as summarizers, word embeddings, and similarity metrics to provide users with news articles along with a corresponding healthcare guideline. A total of 36 models were evaluated and a combination of LexRank based summarizer on Word2Vec embedding with Word Mover distance metric outperformed all other models. This novel open-source approach can be used as a template for proactive dissemination of relevant healthcare information in the midst of misinformation spread associated with epidemics.