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Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings

arXiv 2303.03706 ↗First posted 7 Mar 2023cs.CL cs.LGcited by 0

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Abstract (arXiv)

The FakeNews task in MediaEval 2022 investigates the challenge of finding accurate and high-performance models for the classification of conspiracy tweets related to COVID-19. In this paper, we used BERT, ELMO, and their combination for feature extraction and RandomForest as classifier. The results show that ELMO performs slightly better than BERT, however their combination at feature level reduces the performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 7 Mar 2023.

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References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasetscited by 299
  3. 2019Text Classification Algorithms: A Surveycited by 163
  4. 2019Classification and Clustering of Arguments with Contextualized Word Embeddingscited by 57
  5. 2019Word Embeddings: A Surveycited by 45

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