Using Machine Learning to Fight Plagiarism

Humans appear to be very similar, but if we dig deep enough, we will uncover a vast array of subtle differences.

Plagiarism on Steemit

Another plagiarist bites the dust, after generating thousands of dollars from stolen content.

This seems to be a common theme, and despite the commendable best efforts of the community and groups like #steemcleaners and #steemitabuse, many cheaters get away with stolen content.

@anyx has done some great work on @cheetah, which detects copy/pasted content.

Could we go a step further and build a tool that detects stolen content by analyzing posts by an arbitrary Steemit user?

Looking for patterns

It is well known that individuals can be identified by their physical traits. Several methods have been deployed to take advantage of this, such as:

• facial and voice recognition
• fingerprint and retina scanning
• DNA sequencing

There are also less known, but interesting ways to identify people, by using proxy signals. For instance, it is possible to semi-identify a subject from CCTV footage by analyzing their walking patterns.

Internet companies (such as Google) use browser fingerprinting and behavior analysis to reliably track individuals online, and completely bypass adblock/privacy plugins.

Patterns in writing

Let me start with an anecdote - my writing style. My writing style is probably unique, and here is why. English is not my native language, and I have learned it mostly on the internet.

For instance, I have no idea where should commas (,) go. In fact, I don't really know any of the formal rules of the language.

I also tend to prefix my sentences with things like For instance and In fact quite often.

I am also completely unaware of all the syntax/grammatical errors I repeatedly make (as these are brain coded patterns - a result of neurons firing in the same way each time).

I like to keep sentences short and simple.

Language is an ever-changing thing, and given my age, it is less likely that I am familiar with older or obsolete words.

The region I grew up with determined which words I use, and people I've talked to and the books I've read had shaped my unique style.

And lastly, there are 171,476 words in the Oxford Dictionary alone. I probably know and use only a tiny subset of these. There are many ways to convey a message with a shared meaning, using a completely different set of words.

Using Machine Learning to identify a writing style

Unfortunately, I haven't done any NLP related work, so this topic is a bit foreign to me. I have only considered this problem with the high level idea and did some quick research.

Hypothesis
I have found several tools and techniques that can be used to determine an original author of the works, given enough source material.
Our algorithm for Steemit has an easier task - we don't care about who the original author is, all we care is if a person on Steemit is legit.

If someone is using content that originates from different people, resulting posts will be subtly different. The theory goes, that if the posts are inconsistent, we can raise a red flag for human inspection - as such author is likely sourcing his content from other people.

Disclaimer: I have no idea if this would actually work. I'm putting the idea out there, and maybe someone with experience in this field can help out.

Where this method falls short
Impersonation. If someone is impersonating a single person, then the writing style will be consistent, and thus undetectable under this method.

Resources

I have found several libraries and frameworks that could be used for this problem.

Who wrote the 15th book of Oz?

The tool from the video:
https://github.com/troywatson/Python-Stylometry-Authorship-Ascription-using-Burrow-s-Technique-2.0

More Python Stylometry Tools:
https://github.com/mikekestemont/pystyl
https://github.com/jpotts18/stylometry

And finally an awesome whitepaper, that covers everything from pre-processing to applying classical algos - Naive Bayes, DT and RF - and performance thereof.
https://brage.bibsys.no/xmlui/bitstream/id/360867/Rune%20Borge%20Kalleberg.pdf

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