6533b82cfe1ef96bd128fca5

RESEARCH PRODUCT

COURAGE at CheckThat! 2022: Harmful Tweet Detection using Graph Neural Networks and ELECTRA

Francesco LomonacoGregor DonabauerMarco Siino

subject

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionitext graph classificationTwitterCOVID-19harmful tweetELECTRAText graph classification harmful tweets Twitter ELECTRA COVID-19ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI

description

In this paper we propose a deep learning model based on graph machine learning (i.e. Graph Attention Convolution) and a pretrained transformer language model (i.e. ELECTRA). Our model was developed to detect harmful tweets about COVID-19 and was used to tackle subtask 1C (harmful tweet detection) at the CheckThat!Lab shared task organized as part of CLEF 2022. In this binary classification task, our proposed model reaches a binary F1 score (positive class label, i.e. harmful tweet) of 0.28 on the test set. We demonstrate that our approach outperforms the official baseline by 8% and describe our model as well as the experimental setup and results in detail. We also refer to limitations of the approach and future research directions.

yearjournalcountryeditionlanguage
2022-09-01
http://hdl.handle.net/10447/567798