The purpose of this paper is to analyze the reasons that plagiarism in online literature is so hard to control in China, and it will conclude with a clear solution for the future.
This paper begins its research with the statistics and analysis of plagiarism data and a review of expert interviews regarding online literature publishing. All of these data materials were collected from anti-plagiarism platforms, online literature websites, news report websites and judiciary office websites.
The paper provides empirical insights into why the plagiarism is so rampant in the publishing of online literature in China. It suggests that the current task of controlling network literature plagiarism is arguably created by the literary production platform, which leads to the problem of the validity of the “self-monitoring model.” In fact, controlling plagiarism must be emphasized by means of external monitoring, because strict supervision and various external punitive measurements for committing plagiarism can force literature-generating platforms to strengthen their own internal monitoring.
Online plagiarism occurs almost constantly, but it rarely results in court cases over copyright because of the lack of a robust copyright ecology in China. This paper considers large amounts of data and cases from self-publishing media platforms.
The paper includes implications for the development of plagiarism management in online literature publishing from the publishing Association, media and government.
This paper suggests to online literature users that plagiarism will be controlled when certain active measures against it are taken. The authors hope that this view will promote the development of original online literature.
This paper points out that China must strengthen supervision that comes from outside the online literature generate platforms to control the current rampant plagiarism that occurs on these platforms.
This paper is supported by the National Social Science Fund of China grant “Copyright Developing and Protection of Online literature in Big Data”(17BXW101).
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