A Cross-lingual Natural Language Processing Framework for Infodemic Management

TL;DR

This paper introduces a cross-lingual NLP framework that matches daily news with WHO guidelines to combat misinformation during health crises, utilizing summarization, embeddings, and similarity metrics.

Contribution

The work presents a novel NLP pipeline combining multiple techniques for cross-lingual healthcare information dissemination during epidemics.

Findings

36 models evaluated with best performance from LexRank + Word2Vec + Word Mover's Distance

Effective in matching news articles with trusted health guidelines

Open-source framework for misinformation management during epidemics

Abstract

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.