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Opinion formation under costly expression

Published: 22 October 2010 Publication History

Abstract

Opinions play an important role in trust building and the creation of consensus about issues and products and a number of studies have focused on the design, evaluation, and utilization of online opinion systems. However, little effort has been spent on the dynamic aspects of online opinion formation. In this article, we study the dynamics of online opinion expression by analyzing the temporal evolution of vey large sets of user views and determine that in the course of time, later opinions tend to show a big difference with earlier opinions, which moderates the average opinion to the less extreme. Online posters also tend to disagree with previous opinions when the cost of expression is high.

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  1. Opinion formation under costly expression

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    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 1, Issue 1
    October 2010
    117 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/1858948
    Issue’s Table of Contents
    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: 22 October 2010
    Accepted: 01 June 2010
    Revised: 01 June 2010
    Received: 01 January 2010
    Published in TIST Volume 1, Issue 1

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    1. Opinion formation
    2. costly expression

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    Cited By

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    • (2024)Deep Inception V5 Convolution Neural Network to detect and prevent the propagation of deepfake information in Social Media Applications and Research Databases2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714749(787-791)Online publication date: 3-Oct-2024
    • (2023)BSTC: A Fake Review Detection Model Based on a Pre-Trained Language Model and Convolutional Neural NetworkElectronics10.3390/electronics1210216512:10(2165)Online publication date: 9-May-2023
    • (2023)What Is the Internet Water Army? A Practical Feature-Based Detection of Large-Scale Fake ReviewsMobile Information Systems10.1155/2023/25650202023Online publication date: 1-Jan-2023
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    • (2020)When profile photos matter: the roles of reviewer profile photos in the online review generation and consumption processesJournal of Research in Interactive Marketing10.1108/JRIM-10-2019-0163ahead-of-print:ahead-of-printOnline publication date: 21-Sep-2020
    • (2020)Understanding the dynamics of review posting and the management intervention effects on those dynamicsInternational Journal of Hospitality Management10.1016/j.ijhm.2020.10252688(102526)Online publication date: Jul-2020
    • (2019)Les échos du pouvoirRéseaux10.3917/res.214.0251n° 214-215:2(251-288)Online publication date: 24-May-2019
    • (2019)Understanding Assimilation-contrast Effects in Online Rating SystemsACM Transactions on Information Systems10.1145/336265138:1(1-25)Online publication date: 17-Oct-2019
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