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Representativeness of Abortion Legislation Debate on Twitter: A Case Study in Argentina and Chile

Published: 20 April 2020 Publication History

Abstract

The role of the Web in political exchange has been crucial for society. Its platforms have connected people and allowed manifestation, organization, and access to information; however, they have also produced negative outcomes, such as increased polarization and fast disinformation spreading. These types of phenomena are not completely understood in the context of continuous technological change. Here we propose to grow knowledge in these issues by focusing on representativeness, through the following question: How demographic groups are represented in the discussion on micro-blogging platforms? Our aim is to answer this question on the discussion about a specific topic, abortion, as observed on one of the most popular micro-blogging platforms. As a case study, we followed the abortion discussion on Twitter in two Spanish-speaking countries from 2015 to 2018. Our results indicate differences in representativeness with respect to country, stance, and time of publication, a process that affects to on-going legislation. These findings show that demographic groups differ in how they generate content, and that under- and over-represented groups are not the same between countries, implying that single-country outcomes are not generalizable.

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cover image ACM Conferences
WWW '20: Companion Proceedings of the Web Conference 2020
April 2020
854 pages
ISBN:9781450370240
DOI:10.1145/3366424
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 April 2020

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Author Tags

  1. Data Bias
  2. Social Networks
  3. Stance Prediction

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WWW '20: The Web Conference 2020
April 20 - 24, 2020
Taipei, Taiwan

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  • (2024)Predicting the demographics of Twitter users with programmatic weak supervisionTOP10.1007/s11750-024-00666-y32:3(354-390)Online publication date: 8-Feb-2024
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