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Writing a research paper is not an easy task; it requires delving deep into articles and references and carrying out an extensive study.
And with that amount of hard work and relentless research, the last thing you wish to experience is constant mistakes and plagiarism in the content.
This has nothing to do with honesty; sometimes formatting and citation habits are what contribute to active plagiarism.
So, if you are a researcher who wishes to avoid these obstacles in their published work, here is a guide to do so effectively.
A plagiarism checker compares the submitted text against a large database of existing content and reports overlap at the level of words and phrases. The tool does not distinguish between a passage that was copied without attribution and a passage that legitimately overlaps because of shared standards in how research is presented. Both look identical to a text matching algorithm.
That is why formatting matters more than most students realize. Standard reference formatting, common phrasing in methodology sections, and conventional citation patterns all produce legitimate overlap that the tool identifies as similarity. When this overlap accumulates across a long document, the overall score can look surprisingly high on writing that contains no actual copying at all.
Six specific mistake patterns appear consistently in similarity reports for careful academic writing. Each one has a legitimate reason for appearing in the writer’s work, and each one has a practical fix that reduces the inflation without compromising the writing’s substance.
The six mistakes, what each one looks like in a typical academic document, why it increases the similarity score even on original work, and the specific fix that addresses each without changing the writing’s meaning are mapped below.
| Formatting or Citation Mistake | What It Looks Like | Why It Inflates Score | The Fix |
|---|---|---|---|
| Long uncited direct quotations | Extended quoted passages missing quote marks or block format | Match reads as verbatim copy rather than legitimate quote | Apply proper block quotation formatting throughout |
| Inconsistent citation style | Mix of citation formats within one document | Some references not detected as citations | Apply one consistent style across the manuscript |
| Common phrase overuse | Standard field phrasing repeated across sections | Multiple matches on non-distinctive language | Vary phrasing where the exact wording is not required |
| Missing reference list separation | Bibliography inline or improperly delimited | Reference list scanned as body text overlap | Clearly separate references from main body |
| Uncited common definitions | Standard technical definitions used without attribution | Matches against widely used identical wording | Cite standard definitions or paraphrase substantially |
| Recycled boilerplate from own prior work | Reusing sections from previous publications without noting | Detected as self-overlap against author’s own record | Note prior publication or substantially rewrite reused text |
The pattern across all six is that each mistake produces similarity that has nothing to do with borrowing ideas from other sources. The fix in each case involves either better formatting or clearer referencing, not rewriting the substance of the work. Once these mechanical issues are addressed, the similarity score typically decreases to a level that more accurately reflects the writing’s genuine originality.
A well-formatted academic document produces a similarity result that a reviewer can read at a glance. Quotations are clearly marked. Citations are formatted uniformly. Reference lists are placed after the main body. Common definitions are credited to their sources. Any reuse of the author’s own prior work is documented in an appropriate note.
None of these practices change the writing’s reasoning or evidence. They change how the writing reads to the automated system, which is a separate question from how the writing reads to a human reviewer. Both matter, and both benefit from the same formatting consistency.
For researchers who want to identify and address these formatting issues before submitting work through a formal review, running the document through a plagiarism analysis tool as a pre-submission step reveals the specific passages producing matches, which makes the six common mistakes visible before they reach anyone’s inbox. The segment-level view shows exactly where the score is originating from, which is where the practical fixes actually happen.
Used this way, the pre-check becomes a review step for identifying legitimate formatting cleanup rather than a verdict on the writing’s originality. The score after cleanup is a much more accurate measure of the manuscript’s actual overlap with existing sources than the score before cleanup.
Beyond plagiarism checking specifically, Phrasly.AI operates a platform that bundles plagiarism checking, AI detection, writing enhancement, and several writing utilities in one place. Plagiarism checking and AI detection remain separate analyses producing separate reports, since the two tools measure different properties of writing.
Fixing formatting and citation mistakes reduces score increases on writing that was genuinely original to begin with. It does not, and cannot, turn writing that borrowed significantly from other sources into original work. If the underlying manuscript contains real unattributed copying, cleanup addresses the technical layer but leaves the substantive question untouched.
The distinction remains because writers occasionally hope that formatting fixes will resolve a similarity score that reflects a deeper issue. They will help. The formatting cleanup is worth doing on its own value, and it produces a more accurate report, but it is not a substitute for the substantive originality that a genuine research contribution requires from the writer directly.
For researchers, thesis writers, grant applicants, and other academic authors who use similarity checkers as part of their pre-submission review, the silent inflation problem is worth recognizing as a specific property of how these tools measure. Formatting choices that seem unconnected to originality can quietly push aggregate scores well above what the writing’s actual overlap with sources would suggest.
A working reading of any similarity report considers this inflation before drawing conclusions about the writing itself. What the writer sees in the aggregate score is a combination of legitimate overlap from formatting conventions and any actual borrowing that may be present. Separating those two requires reading the segment-level breakdown, addressing the mechanical issues first, and then judging the substantive originality on the cleaner report that results.
The score increases quietly. The fixes are visible and practical. Knowing which is which is what turns a confusing report into a useful editorial resource.
Formatting and citation mistakes are what increase your plagiarism score, making it appear AI-generated as a result. If you begin fixing these issues, the content can appear authentic, validating the research carried out behind publishing that paper.
To avoid plagiarism, provide appropriate credit to your sources by adding author–date in-text citations for direct quotations and ideas (e.g., credit the originators of theories).
A single research paper with a high number of citations (e.g., 100) is usually considered more valuable and impactful than 100 different papers that each have only one citation.
No, it is not possible to cite your sources with ChatGPT. You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format.
Using someone else’s text without attribution is plagiarism, whether you mean to do it or not. In fact, a writer can even commit plagiarism by using their own work without proper citation.
