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.
File compare – Text comparison between files is a breeze with our tool. Simply select the files you would like to compare, hit “Upload” and our tool will automatically insert the content into the text area, then simply hit “Compare” and let our tool show you where the differences in the text are. By uploading a file, you can still check the keyword density in your content.
Comparing text between URLs is effortless with our tool. Simply paste the URL you would like to get the content from (in our example we use a fantastic blog post by Sherice Jacob found here) hit “Submit URL” and our tool will automatically retrieve the contents of the page and paste it into the text area, then simply click “Compare” and let our tool highlight the difference between the URLs. This feature is especially useful for checking keyword density between pages!
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.
The importance of Google Reviews cannot be overstated.
Sure, brands may tell you that they are the best at what they do, as it’s their job to convince you that is the case.
One way to check whether or not that was true — whether a particular brand really was the best — was to look at Google reviews.
After all, who is better placed to tell you whether or not a brand meets expectations than a fellow consumer?
That said, the rise of AI-generated content has brought online reviews into question. We are all aware that AI-generated reviews exist, but to what extent?
In this study, we aim to identify the true impact of AI-generated content in the context of Google reviews to help businesses and consumers better understand what they are reading online.
Overall, our analysis aims to discuss what the effects could be for consumers and businesses and what people should do to help counteract the rise in generated content.
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The pie chart above reveals a clear division between AI-generated and human-generated Google reviews in 2024.
Notably, 19% of the reviews were identified as AI-generated, while the majority, 81%, were created by humans.
This distribution highlights that while AI is playing an increasingly prominent role in content generation, it remains secondary to human authorship, at least for now.
Yet, the fact that nearly a quarter of the reviews are AI-generated is significant and signals the growing integration of AI tools in digital content.
Businesses might be leveraging AI to streamline review generation, fill gaps in feedback, or create a more robust online presence.
Further, the presence of 19% of reviews as AI also raises important ethical and practical questions.
If used irresponsibly, AI-generated reviews could mislead consumers and harm brand trust. Businesses may face scrutiny from platforms and consumers if the use of AI in reviews becomes too prominent or is perceived as deceptive.
The appearance of any AI-generated content suggests that monitoring and regulation, such as through AI detection, may be needed to ensure transparency and maintain consumer trust.
The fact that 81% of reviews in 2024 were human-generated reviews indicates a continued reliance on organic feedback.
This is likely driven by the perceived authenticity and relatability of human-written reviews.
It suggests that consumers are still actively contributing their own reviews and are more likely to trust or engage with content that reflects genuine human experiences and emotions.
However, this could change in future, as AI becomes increasingly integrated into everyday life.
The data reveals a sharp increase in the rate of AI-generated reviews between 2019 and the end of 2024. In 2019, the AI-generated review rate stood at 5.01%.
By the end of 2024, this rate had climbed dramatically to 19%.
This reflects a 279.2% increase from 2019 to 2024.
The substantial growth of Google reviews that are Likely AI highlights:
The significant jump in the rate from 12.21% in 2023 to 19% by the end of 2024 may indicate a tipping point where AI-generated reviews are no longer a marginal phenomenon but a mainstream element of review platforms.
This underscores the importance of understanding the implications of AI-generated content for both consumers and businesses.
For businesses, the increase in AI review rates presents both opportunities and risks.
Platforms must invest in advanced AI detection tools and transparent policies to address these challenges and maintain credibility.
From a consumer perspective, the growing prevalence of AI-generated reviews may create uncertainty around the authenticity of feedback.
If consumers lose confidence in the reliability of reviews, it could impact purchasing decisions and the perceived value of online feedback systems.
In conclusion, the rapid rise in AI-generated review rates from 5.01% in 2019 to 19% by the end of 2024 signals a profound transformation in the digital review landscape.
While 19% of AI-generated reviews showcase the potential of AI in augmenting digital interactions, the dominance of human-written content at 81% underlines the enduring importance of authenticity.
Businesses should carefully balance efficiency gains from AI tools with the necessity of fostering genuine consumer trust to succeed in the competitive online marketplace.
As this trend continues, businesses and platforms must prioritize transparency, authenticity, and trust to navigate the evolving challenges and opportunities presented by AI-generated content.
Not sure if a review you’re reading is human-written or AI-generated? Use the best-in-class Originality.ai AI Checker to find out.
1.1 Geographic Locations
The study targets the 15 most populous North American cities, using their names and geospatial coordinates to ensure search results focus within city boundaries.
1.2 Categories
Twenty predefined categories (e.g., “hospital,” “pharmacy,” “restaurant”) were selected to capture reviews from various public, commercial, and cultural establishments.
2.1 Place Discovery
Using the Google Places Text Search API, queries for each city-category pair include:
Paginated results are retrieved systematically by handling next_page_token responses.
2.2 Review Retrieval
Place details and reviews are extracted via the Google Places Details API. Each request specifies:
The API returns up to five reviews per place, including text, rating, and timestamp, converted to a readable format.
3.1 Structuring Data
Reviews are stored as dictionaries containing establishment name, review text, rating, review date, and city name, ensuring consistency for analysis.
3.2 Incremental Saving
Data is saved to a CSV file after every 500 reviews to minimize memory usage and protect against data loss. Header rows and progress tracking ensure seamless recovery in case of interruptions.
3.3 Final Dataset
Remaining reviews are saved after processing all city-category combinations, ensuring a comprehensive dataset.
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!
Have you seen a thought leadership LinkedIn post and wondered if it was AI-generated or human-written? In this study, we looked at the impact of ChatGPT and generative AI tools on the volume of AI content that is being published on LinkedIn. These are our findings.
