Cultural Bias in Plagiarism Detection: Rethinking Authorship in the Digital Age

Cultural Bias in Plagiarism Detection: Rethinking Authorship in the Digital Age

Abstract: This study examines how widely used plagiarism detection systems misclassify culturally embedded phrases as plagiarized content. Based on a review of 2,847 academic texts and eight major detection tools, 68% of these expressions were incorrectly flagged. I introduce a new metric called the Cultural Embedding Quotient (CEQ), which helps systems recognize phrases that function as shared cultural knowledge. Applying CEQ-based adjustments to detection algorithms reduces false positives by 47% while maintaining overall accuracy. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5358965

Introduction: Plagiarism detection systems are now a routine part of academic assessment, but their limitations deserve closer attention. A key issue is how these systems treat culturally embedded phrases. These are expressions that have entered common usage and are often used in academic writing to connect with broader cultural conversations. Phrases such as "I have a dream" or "To be or not to be" are frequently flagged as plagiarism, even when used appropriately. This study explores how often and why these misclassifications occur. It also proposes a framework for improving detection accuracy by recognizing the cultural status of certain expressions.

Methods: This study uses a dataset of 2,847 academic documents drawn from seven disciplines, including literature, history, and sociology. Twelve widely recognized phrases were selected to reflect a range of cultural, historical, and disciplinary contexts. Each phrase was tested in four usage types: direct quotation, contextual reference, metaphorical use, and incidental mention. Eight major plagiarism detection systems were used to evaluate how these phrases were handled. I also developed a metric called the Cultural Embedding Quotient (CEQ), which scores phrases based on their frequency, level of attribution, time since origin, and presence across different cultural contexts.

Results: The results show that 68 percent of culturally embedded phrases were wrongly flagged as potential plagiarism. False positives were more common with scientific and political phrases than with literary ones. Even correctly attributed quotations were often flagged. Across all systems, the rate of incorrect detection remained consistently high. However, when algorithms were adjusted using CEQ scores, false positives dropped by 47 percent. These improvements did not affect the systems' ability to detect actual plagiarism, which remained stable.

Discussion: These findings reveal a structural weakness in how current detection systems evaluate text. The tools often ignore the cultural function of language, treating any similarity as a sign of misconduct. This leads to misjudgments, particularly when phrases have become part of collective knowledge. The CEQ provides a practical method for recognizing this kind of language and offers a path toward more accurate and fair assessment. Addressing this issue is not only a technical challenge but also a matter of policy and educational practice. Plagiarism detection systems should be updated to reflect how language actually works in academic and cultural settings.

Cultural Bias in Plagiarism Detection: Rethinking Authorship in the Digital Age

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Cultural Bias in Plagiarism Detection: Rethinking Authorship in the Digital Age

Preprint

Preprint