TY - JOUR
T1 - MUCIC at CheckThat! 2021
T2 - 2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021
AU - Balouchzahi, Fazlourrahman
AU - Shashirekha, Hosahalli Lakshmaiah
AU - Sidorov, Grigori
N1 - Publisher Copyright:
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2021
Y1 - 2021
N2 - Since the beginning of Covid-19 era in November 2019, the patient growth curve is closely accompanied by the growth of fake news. Therefore, developing tools and models for the detection of fake news from real ones in various domains have become more significant than the earlier days. To address the detection of fake news, in this paper, we, team MUCIC, describe the models submitted to 'Fake News Detection', a shared task organized by CLEF-2021-CheckThat! Lab. This shared task contains two subtasks namely; Fake News Detection of News Articles (Subtask 3A) and Topical Domain Classification of News Articles (Subtask 3B) and both are multi-class text classification tasks. The proposed models have been developed by fine-tuning the three transformer-based language models namely; Roberta, Distilbert, and BERT from HuggingFace using training data and then ensembling them as estimators with majority voting. The proposed models performances evaluated through the evaluation script provided by organizers obtained F1-scores of 0.5309 and 0.8550 for Subtask 3A and Subtask 3B respectively.
AB - Since the beginning of Covid-19 era in November 2019, the patient growth curve is closely accompanied by the growth of fake news. Therefore, developing tools and models for the detection of fake news from real ones in various domains have become more significant than the earlier days. To address the detection of fake news, in this paper, we, team MUCIC, describe the models submitted to 'Fake News Detection', a shared task organized by CLEF-2021-CheckThat! Lab. This shared task contains two subtasks namely; Fake News Detection of News Articles (Subtask 3A) and Topical Domain Classification of News Articles (Subtask 3B) and both are multi-class text classification tasks. The proposed models have been developed by fine-tuning the three transformer-based language models namely; Roberta, Distilbert, and BERT from HuggingFace using training data and then ensembling them as estimators with majority voting. The proposed models performances evaluated through the evaluation script provided by organizers obtained F1-scores of 0.5309 and 0.8550 for Subtask 3A and Subtask 3B respectively.
KW - BERT
KW - Distilbert
KW - Domain identification
KW - Fake news detection
KW - Roberta
KW - Transformers
UR - http://www.scopus.com/inward/record.url?scp=85113505995&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85113505995
SN - 1613-0073
VL - 2936
SP - 455
EP - 464
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 21 September 2021 through 24 September 2021
ER -