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

Autor:	Rehman, Abdul, Abbasi, Rabeeh Ayaz, Qureshi, Irfan ul Haq, Khattak, Akmal Saeed
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning 68T01 I.2.7
Zdroj:	Multimedia Benchmark Workshop, Bergen, Norway and Online, 12-13 January 2023
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published in Multimedia Benchmark Workshop 2022, Bergen, Norway and Online, 12-13 January 2023: https://2022.multimediaeval.com/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.03706 Zobrazit plný text záznamu View this record from Arxiv