Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160506
Title: Saving earth one tweet at a time through the lens of artificial intelligence
Authors: Duong, Cuc
Liu, Qian
Mao, Rui
Cambria, Erik
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Source: Duong, C., Liu, Q., Mao, R. & Cambria, E. (2022). Saving earth one tweet at a time through the lens of artificial intelligence. 2022 International Joint Conference on Neural Networks (IJCNN). https://dx.doi.org/10.1109/IJCNN55064.2022.9892271
Abstract: The impacts of climate change and global warming have become more visible globally because of the increasing frequency of extreme weather events, abnormal heatwaves, and other climate crises. Besides the traditional survey method, it is beneficial to automatically distillate climate change opinions from social platforms to measure public reactions quickly. We investigate how to organize climate change opinions on Twitter into meaningful categories to support perspective summarizing tasks. We find that merely using the available taxonomy for this task is ineffective; hence we must consider the entire text content. We recommend five high-level categories (Root cause, Impact, Mitigation, Politics or Policy, Others) and assemble ClimateTweets, a dataset with category and polarity labels. In addition, we construct category classification and polarity detection tasks with a range of opinion mining baselines. The experimental results show that both tasks are challenging for existing models. We release the ClimateTweets dataset to facilitate investigation in public opinion mining using text content and artificial intelligent methods. We hope this study could pave the way for future studies in the climate change domain.
URI: https://hdl.handle.net/10356/160506
ISBN: 978-1-7281-8671-9
ISSN: 2161-4407
DOI: 10.1109/IJCNN55064.2022.9892271
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/IJCNN55064.2022.9892271.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Conference Papers
NEWRI Conference Papers
SCSE Conference Papers

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