2020/10/18 by Anushka Prakash, Harish Tayyar Madabushi, Prakash, Anushka +1 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Video Analysis and Summarization #cs.CL
paper · pdf · doi:10.48550/arxiv.2010.09078
arxiv created 2020/10/18 · openalex publication_date 2020/10/18 · arxiv updated 2020/10/20 · openalex created_date 2020/10/22 · openalex updated_date 2026/07/28
The explosive growth and popularity of Social Media has revolutionised the way we communicate and collaborate. Unfortunately, this same ease of accessing and sharing information has led to an explosion of misinformation and propaganda. Given that stance detection can significantly aid in veracity prediction, this work focuses on boosting automated stance detection, a task on which pre-trained models have been extremely successful on, as on several other tasks. This work shows that the task of stance detection can benefit from feature based information, especially on certain under performing classes, however, integrating such features into pre-trained models using ensembling is challenging. We propose a novel architecture for integrating features with pre-trained models that address these challenges and test our method on the RumourEval 2019 dataset. This method achieves state-of-the-art results with an F1-score of 63.94 on the test set.