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HCMUS MediaEval 2021: Multi-Model Decision Method Applied on Data Augmentation for COVID-19 Conspiracy Theories Classification
MediaEval 2021 Workshop, MediaEval 2021 ; 3181, 2021.
Article in English | Scopus | ID: covidwho-2012718
ABSTRACT
Corona Virus and Conspiracies Multimedia Analysis Task is the task in MediaEval 2021 Challenge that concentrates on conspiracy theories that assume some kind of nefarious actions related to COVID-19. Our HCMUS team performs different approaches based on multiple pretrained models and many techniques to deal with 2 subtasks. Based on our experiments, we submit 5 runs for subtask 1 and 1 run for subtask 2. Run 1 and 2 both introduces BERT[5] pretrained model but the difference between them is that we add a sentimental analysis to extract semantic feature before training in the first run. In run 3 and 4, we propose a naive bayes classifier[4] and a LSTM[8] model to diversify our methods. Run 5 ultilize an ensemble of machine learning and deep learning models - multimodal approach for text-based analysis[3]. Finally, in the only run in subtask 2, we conduct a simple naive bayes algorithm to classify those theories. In the final result, our method achieves 0.5987 in task 1, 0.3136 in task 2. Copyright 2021 for this paper by its authors.
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Collection: Databases of international organizations Database: Scopus Language: English Journal: MediaEval 2021 Workshop, MediaEval 2021 Year: 2021 Document Type: Article

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Collection: Databases of international organizations Database: Scopus Language: English Journal: MediaEval 2021 Workshop, MediaEval 2021 Year: 2021 Document Type: Article