Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Text

• URL: http://arxiv.org/abs/2207.07308v1
• Date: Fri, 15 Jul 2022 06:21:35 GMT
• Title: Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Text
• Authors: Prerona Tarannum, Firoj Alam, Md. Arid Hasan, Sheak Rashed Haider Noori
• Abstract summary: We describe our participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing steps and applied different models to identify whether a given text is worthy of fact checking or not. We also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models for the experiments.
• Score: 2.0887898772540217
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The wide use of social media and digital technologies facilitates sharing various news and information about events and activities. Despite sharing positive information misleading and false information is also spreading on social media. There have been efforts in identifying such misleading information both manually by human experts and automatic tools. Manual effort does not scale well due to the high volume of information, containing factual claims, are appearing online. Therefore, automatically identifying check-worthy claims can be very useful for human experts. In this study, we describe our participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing steps and applied different models to identify whether a given text is worthy of fact checking or not. We use the oversampling technique to balance the dataset and applied SVM and Random Forest (RF) with TF-IDF representations. We also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models for the experiments. We used BERT-m for the official submissions and our systems ranked as 3rd, 5th, and 12th in Spanish, Dutch, and English, respectively. In further experiments, our evaluation shows that transformer models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and English languages where a different scenario is observed for Spanish.

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