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Follow the reader: filtering comments on slashdot

Published: 29 April 2007 Publication History

Abstract

Large-scale online communities need to manage the tension between critical mass and information overload. Slashdot is a news and discussion site that has used comment rating to allow massive participation while providing a mechanism for users to filter content. By default, comments with low ratings are hidden. Of users who changed the defaults, more than three times as many chose to use ratings for filtering or sorting as chose to suppress the use of comment ratings. Nearly half of registered users, however, never strayed from the default filtering settings, suggesting that the costs of exploring and selecting custom filter settings exceeds the expected benefit for many users. We recommend leveraging the efforts of the users that actively choose filter settings to reduce the cost of changing settings for all other users. One strategy is to create static schemas that capture the filtering preferences of different groups of readers. Another strategy is to dynamically set filtering thresholds for each conversation thread, based in part on the choices of previous readers. For predicting later readers' choices, the choices of previous readers are far more useful than content features such as the number of comments or the ratings of those comments.

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    cover image ACM Conferences
    CHI '07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    April 2007
    1654 pages
    ISBN:9781595935939
    DOI:10.1145/1240624
    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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    Publication History

    Published: 29 April 2007

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    Author Tags

    1. customization
    2. online discussion
    3. rating systems

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    CHI07: CHI Conference on Human Factors in Computing Systems
    April 28 - May 3, 2007
    California, San Jose, USA

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    CHI '07 Paper Acceptance Rate 182 of 840 submissions, 22%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    Cited By

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    • (2023)A differential machine learning approach for trust prediction in signed social networksThe Journal of Supercomputing10.1007/s11227-023-05044-279:9(9443-9466)Online publication date: 16-Jan-2023
    • (2022)Our Browser Extension Lets Readers Change the Headlines on News Articles, and You Won't Believe What They Did!Proceedings of the ACM on Human-Computer Interaction10.1145/35556436:CSCW2(1-33)Online publication date: 11-Nov-2022
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    • (2021)A Trust-Based Vulnerability Analysis in Signed Social Networks2021 18th International ISC Conference on Information Security and Cryptology (ISCISC)10.1109/ISCISC53448.2021.9720314(23-27)Online publication date: 1-Sep-2021
    • (2021)Influence Maximization in Signed Networks by Enhancing the Negative InfluenceIEEE Access10.1109/ACCESS.2021.30659379(44084-44093)Online publication date: 2021
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    • (2020)ROSE: Role-based Signed Network EmbeddingProceedings of The Web Conference 202010.1145/3366423.3380038(2782-2788)Online publication date: 20-Apr-2020
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