分析政治新闻评论偏见，促进网络社区透明化

Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures Pub Date : 2020-04-25 DOI:10.1145/3391203.3391216

Joonho Gwon, Minji Kwon, Hyunggu Jung

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引用次数: 0

摘要

对新闻文章的评论会影响人们的看法和行为。然而，人们如何确定政治新闻评论的偏见程度(DoB)却知之甚少。为了解决这种偏见问题，目前的新闻平台提供了对新闻文章的多条评论进行分类的标准。然而，当发布者提供各种分类评论的标准时，评论的DoB是否减少了，我们知之甚少。我们进行了调查，以确定人们如何确定对新闻文章的评论的DoB，以及偏见如何根据评论的分类方式而变化。本研究结果显示，评语对学生的学业成绩有显著的影响。未来的工作仍然是开发一种算法，通过使用对政治新闻文章及其DoB的现有评论来生成公正的评论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Analyzing Bias of Comments on Political News Articles to Facilitate Transparent Online Communities

Comments on the news articles can affect people's perceptions and behaviors. However, little is known about how people determine the degree of bias (DoB) of comments on political news articles. To address such bias issues, current platforms of news articles offer criteria to sort multiple comments on the news articles. However, little is known about whether the DoB of comments is reduced when publishers offer various criteria for sorting comments. We conducted surveys to identify how people determine DoB of comments on the news article, and how bias varies depending on how comments are sorted. The findings of this study revealed that there was a significant difference among the DoB by comments. Future work remains to develop an algorithm generating unbiased comments by using existing comments on political news articles and their DoB.

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Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures

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