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Link to original content: https://doi.org/10.1145/3394231.3397907

Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues | Proceedings of the 12th ACM Conference on Web Science

research-article

Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues

Authors:

Eduardo Graells-Garrido,

Ricardo Baeza-Yates,

Mounia LalmasAuthors Info & Claims

WebSci '20: Proceedings of the 12th ACM Conference on Web Science

Pages 174 - 183

https://doi.org/10.1145/3394231.3397907

Published: 06 July 2020 Publication History

Abstract

Web platforms have allowed political manifestation and debate for decades. Technology changes have brought new opportunities for expression, and the availability of longitudinal data of these debates entice new questions regarding who participates, and who updates their opinion. The aim of this work is to provide a methodology to measure these phenomena, and to test this methodology on a specific topic, abortion, as observed on one of the most popular micro-blogging platforms. To do so, we followed the discussion on Twitter about abortion in two Spanish-speaking countries from 2015 to 2018. Our main insights are two fold. On the one hand, people adopted new technologies to express their stances, particularly colored variations of heart emojis ( & ) in a way that mirrored physical manifestations on abortion. On the other hand, even on issues with strong opinions, opinions can change, and these changes show differences in demographic groups. These findings imply that debate on the Web embraces new ways of stance adherence, and that changes of opinion can be measured and characterized.

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Cited By

Zhou ZElejalde E(2024)Unveiling the silent majority: stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networksEPJ Data Science10.1140/epjds/s13688-024-00469-y13:1Online publication date: 4-Apr-2024
https://doi.org/10.1140/epjds/s13688-024-00469-y
Zhou ZElejalde E(2023)Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal SupervisionCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587640(1030-1038)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587640
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https://dl.acm.org/doi/10.1145/3543507.3583442
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cover image ACM Conferences

WebSci '20: Proceedings of the 12th ACM Conference on Web Science

July 2020

361 pages

ISBN:9781450379892

DOI:10.1145/3394231

Copyright © 2020 ACM.

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Published: 06 July 2020

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WebSci '20: 12th ACM Conference on Web Science

July 6 - 10, 2020

Southampton, United Kingdom

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Cited By

Zhou ZElejalde E(2024)Unveiling the silent majority: stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networksEPJ Data Science10.1140/epjds/s13688-024-00469-y13:1Online publication date: 4-Apr-2024
https://doi.org/10.1140/epjds/s13688-024-00469-y
Zhou ZElejalde E(2023)Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal SupervisionCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587640(1030-1038)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587640
Wildemann SNiederée CElejalde E(2023)Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisisProceedings of the ACM Web Conference 202310.1145/3543507.3583442(2754-2764)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583442
Graells-Garrido EBaeza-Yates RBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)Bots don’t Vote, but They Surely Bother!Proceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531576(302-306)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531576
Aldayel AMagdy W(2022)Characterizing the role of bots’ in polarized stance on social mediaSocial Network Analysis and Mining10.1007/s13278-022-00858-z12:1Online publication date: 4-Feb-2022
https://doi.org/10.1007/s13278-022-00858-z
Alkhalifa RZubiaga A(2021)QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge GraphsEVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 202010.4000/books.aaccademia.7114(198-203)Online publication date: 11-May-2021
https://doi.org/10.4000/books.aaccademia.7114
Freire-Vidal YGraells-Garrido ERowe F(2021)A Framework to Understand Attitudes towards Immigration through TwitterApplied Sciences10.3390/app1120968911:20(9689)Online publication date: 18-Oct-2021
https://doi.org/10.3390/app11209689
Alkhalifa RKochkina EZubiaga AGuidi BMichienzi ARicci L(2021)Opinions are Made to be ChangedProceedings of the 2021 Workshop on Open Challenges in Online Social Networks10.1145/3472720.3483620(27-32)Online publication date: 19-Oct-2021
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Pérez-Arredondo CGraells-Garrido E(2021)Twitter and abortionJournal of Language Aggression and Conflict10.1075/jlac.00056.per9:1(127-154)Online publication date: 4-Mar-2021
https://doi.org/10.1075/jlac.00056.per
ALDayel AMagdy W(2021)Stance detection on social mediaInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10259758:4Online publication date: 1-Jul-2021
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