5q032e@SMM4H'22: Transformer-based classification of premise in tweets related to COVID-19

TL;DR

This paper presents a transformer-based model, specifically RoBERTa, for classifying the presence of premises in COVID-19 related tweets, demonstrating its effectiveness in understanding social media attitudes during the pandemic.

Contribution

The work introduces a transformer-based approach for premise classification in tweets, highlighting RoBERTa's superior performance over other models in this task.

Findings

RoBERTa outperforms other transformer models in premise prediction

Achieved ROC AUC of 0.807 and F1 score of 0.7648

Effective for social media stance analysis during COVID-19

Abstract

Automation of social network data assessment is one of the classic challenges of natural language processing. During the COVID-19 pandemic, mining people's stances from public messages have become crucial regarding understanding attitudes towards health orders. In this paper, the authors propose the predictive model based on transformer architecture to classify the presence of premise in Twitter texts. This work is completed as part of the Social Media Mining for Health (SMM4H) Workshop 2022. We explored modern transformer-based classifiers in order to construct the pipeline efficiently capturing tweets semantics. Our experiments on a Twitter dataset showed that RoBERTa is superior to the other transformer models in the case of the premise prediction task. The model achieved competitive performance with respect to ROC AUC value 0.807, and 0.7648 for the F1 score.