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Supporting Comment Moderators in Identifying High Quality Online News Comments

Published: 07 May 2016 Publication History
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  • Abstract

    Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments.

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      cover image ACM Conferences
      CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
      May 2016
      6108 pages
      ISBN:9781450333627
      DOI:10.1145/2858036
      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 the author(s) 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: 07 May 2016

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      1. asynchronous collaboration
      2. computational journalism
      3. online comments

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      May 7 - 12, 2016
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      CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;
      Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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      • (2024)MOTIV: Visual Exploration of Moral Framing in Social MediaComputer Graphics Forum10.1111/cgf.15072Online publication date: 28-Mar-2024
      • (2023)"How Do You Quantify How Racist Something Is?": Color-Blind Moderation in Decentralized GovernanceProceedings of the ACM on Human-Computer Interaction10.1145/36100307:CSCW2(1-27)Online publication date: 4-Oct-2023
      • (2023)Decolonizing Content Moderation: Does Uniform Global Community Standard Resemble Utopian Equality or Western Power Hegemony?Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581538(1-18)Online publication date: 19-Apr-2023
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