Work

Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings

Year: 2021

Type: conference-paper

Abstract: We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic.We compared 4 multitask learning models for this task and found that a model trained with English BERT achieves the best results for English, and multilingual BERT achieves the best results for Bulgarian and Arabic.We experimented with zero shot, few shot, and target-only conditions to evaluate the impact of target-language training data on classifier performance, and to understand the capabilities of different models to generalize across languages in detecting misinformation online.This work was performed as a submission to the shared task, NLP4IF 2021: Fighting the COVID-19 Infodemic.Our best models achieved the second best evaluation test results for Bulgarian and Arabic among all the participating teams and obtained competitive scores for English.

Source: Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda (NLP4IF)

DOI: 10.18653/v1/2021.nlp4if-1.19

Authors: Subhadarshi Panda, Sarah Ita Levitan

Institution: Hunter College

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W3169508552