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Keyword density helper – This tool comes with a built-in keyword density helper in some ways similar to the likes of SurferSEO or MarketMuse the difference being, ours is free! This feature shows the user the frequency of single or two word keywords in a document, meaning you can easily compare an article you have written against a competitor to see the major differences in keyword densities. This is especially useful for SEO’s who are looking to optimize their blog content for search engines and improve the blog’s visibility.
You can also easily compare text by copying and pasting it into each field, as demonstrated below.
Ease of use
Our text compare tool is created with the user in mind, it is designed to be accessible to everyone. Our tool allows users to upload files or enter a URL to extract text, this along with the lightweight design ensures a seamless experience. The interface is simple and straightforward, making it easy for users to compare text and detect the diff.
Multiple text file format support
Our tool provides support for a variety of different text files and microsoft word formats including pdf file, .docx, .odt, .doc, and .txt, giving users the ability to compare text from different sources with ease. This makes it a great solution for students, bloggers, and publishers who are looking for file comparison in different formats.
Protects intellectual property
Our text comparison tool helps you protect your intellectual property and helps prevent plagiarism. This tool provides an accurate comparison of texts, making it easy to ensure that your work is original and not copied from other sources. Our tool is a valuable resource for anyone looking to maintain the originality of their content.
User Data Privacy
Our text compare tool is secure and protects user data privacy. No data is ever saved to the tool, the users’ text is only scanned and pasted into the tool’s text area. This makes certain that users can use our tool with confidence, knowing their data is safe and secure.
Compatibility
Our text comparison tool is designed to work seamlessly across all size devices, ensuring maximum compatibility no matter your screen size. Whether you are using a large desktop monitor, a small laptop, a tablet or a smartphone, this tool adjusts to your screen size. This means that users can compare texts and detect the diff anywhere without the need for specialized hardware or software. This level of accessibility makes it an ideal solution for students or bloggers who value the originality of their work and need to compare text online anywhere at any time.
As one of the most pressing global challenges, climate science relies on clear, accurate, and credible communication of research findings, often in the form of formal scientific articles.
So, what happens when the integrity of those papers’ abstracts is called into question?
The emergence of large language models (LLMs) like ChatGPT has made it increasingly easy for researchers to draft scientific content, including abstracts, with minimal effort. However, generative AI tools aren’t perfect and have been known to hallucinate and make mistakes.
Without proper oversight, it’s therefore possible that AI-generated climate change abstracts may not be as accurate as they should — or need — to be.
Since it’s often the first (and in some cases, only) portion of a paper read by policymakers, media, and the general public, maintaining the integrity of abstracts is crucial not only academically, but also socially and politically.
Due to this influence, this study examines the prevalence of AI-generated abstracts in climate change-related publications from 2018 to 2025 and assesses implications for the growing role of AI in scientific authorship.
Based on thousands of climate change abstracts collected from 2018 to 2025, this study aimed to:
We found that the proportion of AI climate change abstracts in scientific journals rose dramatically from 2018 to 2025.
Check out our quick overview chart to see the rates of Likely AI climate change abstracts by year.
Then, keep reading for a year-by-year discussion of the data.
The rate of AI-generated climate abstracts stayed fairly low and steady from 2018 to 2020:
Then, overall, despite a slight drop in 2022, the trend began to accelerate:
This means that by 2025, nearly half of all climate change abstracts now contain content that is likely generated with the assistance of AI.
Overall, the increase from 2018 to 2025 amounts to a 1367% rise in the rate of AI-generated abstracts.
This exponential growth underscores the rapid adoption of AI tools in academic writing, particularly following the release of ChatGPT at the end of 2022.
Although the sharp rise in AI-generated abstracts from 2018 to 2025 reflects a fundamental shift in how scientific knowledge is being produced and communicated, this trend isn’t limited to academia.
The use of generative AI tools has increased across many industries during this period, especially in online reviews. Previous studies we have conducted found growing rates of likely AI-generated bank reviews, Glassdoor reviews, and lawyer’s office reviews.
So, the finding that 45.83% of climate change journal abstracts in 2025 are likely AI-generated highlights a broader pattern.
It also raises important questions about authorship, accountability, and academic publishing standards.
The prevalence of AI climate change abstracts has several significant implications:
Ultimately, researchers, journals, and institutions will likely need to reconsider policies around AI disclosure, authorship standards, and peer-review processes to preserve the credibility of climate change research and other scientific disciplines.
This study reveals an exponential rise in AI-generated climate change abstracts from 2018 to 2025. Considering the potential implications of this trend, such a significant increase underscores the need for academics, researchers, and publishers to manage the ethical and practical implications of AI in academic writing.
To uphold transparency, trust, and integrity in scientific communication, those in the academic community should consider:
Until more robust guidelines are developed regarding generative AI use in climate change and other scientific disciplines, readers may also consider evaluating abstracts more critically themselves.
Do you think you’re reading an AI-generated abstract? Try Originality.ai’s AI Checker to find out today.
Curious about the trend of AI-generated content on other platforms? Read more:
To study the prevalence of AI-generated abstracts in climate change research, we analyzed scientific abstracts published from 2018 to 2025. Abstracts were collected using the OpenAlex API, filtered by the keyword “climate change,” publication year, and presence of non-empty abstracts, with up to 500 entries per year. A custom function reconstructed the abstracts from OpenAlex's inverted index, preserving key metadata.
Each abstract (minimum 50 words) was evaluated using the Originality.ai API, which provided an AI-likelihood score and binary classification. Scans were repeated if API errors occurred.
All data — including titles, abstracts, metadata, and AI detection results — were compiled into a CSV file using pandas. This dataset supported further analysis, including trends in AI-generated content over time.
No, that’s one of the benefits, only fill out the areas which you think will be relevant to the prompts you require.
When making the tool we had to make each prompt as general as possible to be able to include every kind of input. Not to worry though ChatGPT is smart and will still understand the prompt.
Originality.ai did a fantastic job on all three prompts, precisely detecting them as AI-written. Additionally, after I checked with actual human-written textual content, it did determine it as 100% human-generated, which is important.
Vahan Petrosyan
searchenginejournal.com
I use this tool most frequently to check for AI content personally. My most frequent use-case is checking content submitted by freelance writers we work with for AI and plagiarism.
Tom Demers
searchengineland.com
After extensive research and testing, we determined Originality.ai to be the most accurate technology.
Rock Content Team
rockcontent.com
Jon Gillham, Founder of Originality.ai came up with a tool to detect whether the content is written by humans or AI tools. It’s built on such technology that can specifically detect content by ChatGPT-3 — by giving you a spam score of 0-100, with an accuracy of 94%.
Felix Rose-Collins
ranktracker.com
ChatGPT lacks empathy and originality. It’s also recognized as AI-generated content most of the time by plagiarism and AI detectors like Originality.ai
Ashley Stahl
forbes.com
Originality.ai Do give them a shot!
Sri Krishna
venturebeat.com
For web publishers, Originality.ai will enable you to scan your content seamlessly, see who has checked it previously, and detect if an AI-powered tool was implored.
Industry Trends
analyticsinsight.net
Tools for conducting a plagiarism check between two documents online are important as it helps to ensure the originality and authenticity of written work. Plagiarism undermines the value of professional and educational institutions, as well as the integrity of the authors who write articles. By checking for plagiarism, you can ensure the work that you produce is original or properly attributed to the original author. This helps prevent the distribution of copied and misrepresented information.
Text comparison is the process of taking two or more pieces of text and comparing them to see if there are any similarities, differences and/or plagiarism. The objective of a text comparison is to see if one of the texts has been copied or paraphrased from another text. This text compare tool for plagiarism check between two documents has been built to help you streamline that process by finding the discrepancies with ease.
Text comparison tools work by analyzing and comparing the contents of two or more text documents to find similarities and differences between them. This is typically done by breaking the texts down into smaller units such as sentences or phrases, and then calculating a similarity score based on the number of identical or nearly identical units. The comparison may be based on the exact wording of the text, or it may take into account synonyms and other variations in language. The results of the comparison are usually presented in the form of a report or visual representation, highlighting the similarities and differences between the texts.
String comparison is a fundamental operation in text comparison tools that involves comparing two sequences of characters to determine if they are identical or not. This comparison can be done at the character level or at a higher level, such as the word or sentence level.
The most basic form of string comparison is the equality test, where the two strings are compared character by character and a Boolean result indicating whether they are equal or not is returned. More sophisticated string comparison algorithms use heuristics and statistical models to determine the similarity between two strings, even if they are not exactly the same. These algorithms often use techniques such as edit distance, which measures the minimum number of operations (such as insertions, deletions, and substitutions) required to transform one string into another.
Another common technique for string comparison is n-gram analysis, where the strings are divided into overlapping sequences of characters (n-grams) and the frequency of each n-gram is compared between the two strings. This allows for a more nuanced comparison that takes into account partial similarities, rather than just exact matches.
String comparison is a crucial component of text comparison tools, as it forms the basis for determining the similarities and differences between texts. The results of the string comparison can then be used to generate a report or visual representation of the similarities and differences between the texts.
Syntax highlighting is a feature of text editors and integrated development environments (IDEs) that helps to visually distinguish different elements of a code or markup language. It does this by coloring different elements of the code, such as keywords, variables, functions, and operators, based on a predefined set of rules.
The purpose of syntax highlighting is to make the code easier to read and understand, by drawing attention to the different elements and their structure. For example, keywords may be colored in a different hue to emphasize their importance, while comments or strings may be colored differently to distinguish them from the code itself. This helps to make the code more readable, reducing the cognitive load of the reader and making it easier to identify potential syntax errors.
With our tool it’s easy, just enter or upload some text, click on the button “Compare text” and the tool will automatically display the diff between the two texts.
Using text comparison tools is much easier, more efficient, and more reliable than proofreading a piece of text by hand. Eliminate the risk of human error by using a tool to detect and display the text difference within seconds.
We have support for the file extensions .pdf, .docx, .odt, .doc and .txt. You can also enter your text or copy and paste text to compare.
There is never any data saved by the tool, when you hit “Upload” we are just scanning the text and pasting it into our text area so with our text compare tool, no data ever enters our servers.
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This table below shows a heat map of features on other sites compared to ours as you can see we almost have greens across the board!
In an extension of the peer-reviewed study, “The accuracy-bias trade-offs in AI text detection tools and their impact on fairness in scholarly publication,” Originality.ai demonstrated exceptional results.
AI & Plagiarism Detector for Serious Content Publishers
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Originality.ai
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