How to reduce plagiarism? || Research Publications || Hindi || 2024 || Dr. Akash Bhoi

Plagiarism-check similarity scores can be driven down sharply—down from around 82% toward below 10%—but only when authors first diagnose where the overlap is coming from and then rewrite ethically, focusing on the method and results sections rather than merely “editing” text. The core workflow is: upload the paper to see which sources trigger the similarity, identify the exact passages and categories (often internet sources, published papers, or websites), then apply targeted, policy-compliant fixes before re-checking the similarity report.

The process begins with understanding the paper’s own structure and content choices. Similarity spikes often trace back to how the introduction, literature review, methods, and results were written—especially if content was copied or too closely paraphrased from external sources. If the overlap is concentrated in the introduction or literature review, the fix may require reworking that section’s framing and synthesis. If the overlap appears in the method and results, the author is warned to revisit whether the work is truly original or whether earlier wording and phrasing were carried over too directly.

After the initial upload, the similarity report’s color-coded highlights and source breakdown become the diagnostic map. The author emphasizes that the percentage is not a single number to “beat” blindly; it reflects specific matched text and the sources behind those matches. The next step is to rewrite with a clear strategy: exclude or filter irrelevant matches where allowed by institutional rules, and correct technical or repeated terms carefully. The transcript highlights that some technical phrases—such as those involving machine learning, artificial intelligence, or computing—may be difficult to alter without harming scientific meaning, so the focus should be on legitimate rephrasing and proper citation rather than distortion.

To support rewriting, the transcript recommends language and grammar tools to fix spelling, grammar, and clarity issues, arguing that journals and supervisors often expect language correction before resubmission. It also describes using a “rewrite/paraphrase” tool (with a free version) to apply high-level paragraph changes, then copying the revised text back into the paper and re-running the similarity check. In the demonstration, the similarity score drops substantially after rewriting, but the author cautions that automated “freshening” can create problems if it generates text that no longer matches the author’s intended meaning—especially if the tool suggests sentences that are not ethically or scientifically appropriate.

When the similarity score still remains high, the transcript’s final fallback is manual rewriting: take the original base content, understand what is wrong, then rewrite sentences by merging ideas in the author’s own words. The goal is to produce a paper that is genuinely the author’s work—removing or adjusting problematic copied phrasing, cleaning up citation artifacts like quotations/bibliography handling where appropriate, and ensuring the final submission aligns with university and UGC policy expectations. The takeaway is practical: similarity reduction is achievable, but it depends on diagnosis, ethical rewriting, and repeated verification—not on chasing a target percentage through shortcuts.