BibTeX details

@inproceedings{10.1145/3560107.3560296,
	author = "Diaz, Gabriela Andrea and Ches\~{n}evar, Carlos Iv\'{a}n and Estevez, Elsa and Maguitman, Ana",
	title = "Stance Trees: A Novel Approach for Assessing Politically Polarized Issues in Twitter",
	year = "2022",
	isbn = "9781450396356",
	publisher = "Association for Computing Machinery",
	address = "New York, NY, USA",
	url = "https://doi.org/10.1145/3560107.3560296",
	doi = "10.1145/3560107.3560296",
	abstract = "Social and political polarization, which sometimes is the result of misinformation, is a common obstacle that can be harmful at the moment of communicating government policies. Intelligent tools that aid critical thinking in the light of different opinions and standpoints available in social media can help ameliorate this obstacle. This paper presents preliminary research work toward developing such tools by proposing a methodology for building stance trees based on tweets collected from social media. Stance trees are hierarchical structures where nodes represent arguments pro, anti, or uncertain about a target issue and edges stand for attack relations between those arguments. The proposed methodology includes retrieving tweets relevant to the target issue, manually labeling a sample set of the collected tweets, developing and applying a model for stance detection, and finally building a stance tree. We illustrate the expected results through a case study on the politically polarized “COVID-19 vaccine” issue. Our preliminary results demonstrate the feasibility of the proposal and highlight the utility of stance trees as a tool for aiding critical thinking.",
	booktitle = "Proceedings of the 15th International Conference on Theory and Practice of Electronic Governance",
	pages = "19–24",
	numpages = "6",
	keywords = "Argumentation, Artificial Intelligence, Stance Detection, Political Polarization, E-Participation, Social Media",
	location = "Guimar\~{a}es, Portugal",
	series = "ICEGOV '22",
}