CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

Autor:	Poddar, Soham, Samad, Azlaan Mustafa, Mukherjee, Rajdeep, Ganguly, Niloy, Ghosh, Saptarshi
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Computers and Society Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models. Our dataset and codes are available at: https://github.com/sohampoddar26/caves-data Comment: Accepted at SIGIR'22 (Resource Track)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.13746 Zobrazit plný text záznamu View this record from Arxiv