We know that plagiarism is bad, identity theft is bad & we have @cheetah, @spaminator, @steemcleaners etc taking a strong stand against it. They combines NLP (Natural Language Processing) and human intelligence.

Now, what if there is a way to circumvent all this an effective way and still being correct from a "original content" and copy right stand point.

Here the ethics is not weighed, but a scenario is given and an algorithmic approach using NLP and Computer Vision (CV) and deeper analysis of content its similarities. Further, spelling mistakes MUST be considered as a "positive", Too "perfect" grammar has to be considered a trigger.

Now the components are just assembled & the basic tool used is wcopyfind. This has limited the availability for now as instead of using a scalable architecture WINE software is used to run wcopyfind on a Debian Box which slows down the entire process.

While I was upto this, wcopyfind had found headlines by bringing Shakespere to the mix.

(By Steve Evans from Citizen of the World (London Shopping 0017) [CC BY 2.0], via Wikimedia Commons)

1. Plagiarism software finds Shakespeare plundered cool words from a little-known book
2. The Guardian has a neutral, piece :
   Plagiarism software pins down new source for Shakespeare's plays

While I am no one make any comment on the news, my use of wcopyfind did give some confidence.

Steemit based Business model

The elevator pitch:

This is a lean & scalable business model using the fastest, FREE blockchain & circle-voting scenario which is 100% legit & practical.

1. Identify one content writer - may not be on steemit
2. One or two models
3. Create accounts using the original identification of the models and receive model release and agreements in place
4. Establish a remuneration model & a commission structure
5. Create a content generation
6. Plan few simple shoots with the models
7. carefully distribute the photos as taken with multiple cameras
8. content writer is assigned tasks and paid for words
9. The operator posts content across multiple accounts ensuring maximum ROI
10. circle vote
11. Once the proof of work is identified and a certain followers are accumulated with "social engineering", go for "whale pitches"
12. Pitch some whales, get STEEM delegations or setup up voting mechanism with a profit share
13. anyone asks for proof of id, the models gives their identification as per the arrangement
14. Everyone gets paid.

Now I am not sure whether this is already attempted or not, but sounds like a good plan to make some quick money. If we can diversify and hire models across various ethnic and other orientation, this can be scaled to ensure a substantial revenue.

The diversification can be directly proportional to the steemit traffic to ensure maximum ROI.

a business model canvas eagerly waiting a whale to be filled and made into a proposal

(By Business Model Alchemist (http://www.businessmodelalchemist.com/tools) [CC BY-SA 1.0], via Wikimedia Commons)

An algorithmic approach to identification of the business model

From the NSA leaks and all other recent data leaks, we might have heard about the word "Meta data". This is very important as meta data is often ignored and can be used to identify patterns.

The algorithm has to mimic a human being and look for what human beings do best - mistakes!

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"No one is Perfect"

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Experienced content writers and also authors follow few patterns:

Rule0

1. Similar grammatical structure and choice of words
2. Either crisp or long sentences but often with near equal number of words
3. Punctuation are always used
4. Slang is avoided
5. No incomplete sentences eg:- But ....
6. Proper capitalization
7. Two sentences are always separated by the same amount of SPACE or TAB (Delimiter)
8. Experienced bloggers will have smaller sentences with near equal number of words in each sentence
9. Experienced bloggers will give importance to Above the FOLD content as opposed to Below the Fold content (eye tracking, mouse tracking, higher click-through, attention span)

We all follow certain words in sequences without our own knowledge.

Bingo. As the New York Times reports:

In the dedication to his manuscript, for example, North urges those who might see themselves as ugly to strive to be inwardly beautiful, to defy nature. He uses a succession of words to make the argument, including “proportion,” “glass,” “feature,” “fair,” “deformed,” “world,” “shadow” and “nature.” In the opening soliloquy of Richard III (“Now is the winter of our discontent …”) the hunchbacked tyrant uses the same words in virtually the same order to come to the opposite conclusion: that since he is outwardly ugly, he will act the villain he appears to be.

Now, from a STEEMit perspective, we can formulate that,

Rule_Set1:

1. Spelling mistakes are good
2. Incomplete sentences, slang etc are good!
3. Minimal or one or two plagiarized posts is good
4. Grammatical errors, Extra long sentences and Paragraphs are good

Rule_Set2: Basic Computer Vision and Textual analysis

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A Photo can tell stories but metadata can make those stories real

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1. Look for visually similar images - flickr is a mandatory data source
2. look for licensing
3. Majority of the free to use images are "Editorial Use" & needs attribution
4. Not giving attribution and back-link irrespective of the licensing of the work MUST trigger an alarm.

As a matter of fact I give high weight to any sort of slightest abuse of photographs as I have personally a victim of theft and I know the effort behind each and every photograph even from a serious ameature. Myself have faced near death scenarios, hit by charging bulls, nearly held hostage etc. I have known legends like Victor George & K J Vincent who died while working on their last assignments.

K J Vincent was hit by a train & I had met him only once - for the first and last time.

Rule_Set3: Meta data extraction and comparison

When a single master account is employed, often there will be precious information like camera, model, time stamps & geo-location data hidden inside the IPTC - EXIF tags.

1. Extract EXIF
2. Use time stamps to compare against target accounts
3. Triangulate position if geographical information is available

Rule_set4:

This is very minimal for now.

1. Analyse memos - not done
2. Posting time stamps, intervals
3. Apps used

Putting it all together

We will not be looking for only internet, google books, PubMed or other scientific journals alone. We have to start without ourself and look for good content and use it to to train the neural network.

• Apply Rule0 and group the good authors
• This helps to whitelist/greylist/blacklist users and avoid wasting CPU power.
• This takes out all the good writers / good Samaritans out of the cross checks (sort of)
• Good authors are given certain weightages & used as the benchmark to compare against
• This has disadvantage that the very first author employing a paid content writer will mostly be marked legit
• But the reasoning is, if @cheetah, @spaminator, @blacklist-a & @steemcleaners have failed, no point in attempting again.

Scenario 1: A new legitimate user joins steem

This will invoke a trigger as he will pass checks for "too good" as per Rule0 & will undergo plagiarism checks. (Make few spelling mistakes or spaces or unwanted comma and the algorithm fails!) Soon the algorithm will internally assign a "good Samaritan Medallion" and put the case for rest. This is not the best approach as such and needs to be tweaked. But, other systems will be able to track deception.

Scenario 2: A second user managed by a good user joins

This is when the alarm rings! The Rule0 kicks in, Rule_Set1 fails & a comparison of typical plagiarism content is done. A mutual comparison and check against "Good Samaritan" list is made. By this time Rule_Set2 may either come back with a good score or a bad score.

In either case, of Rule_Set2 is good or bad, the sets of "Good Samaritans" and new users and compared further. If the score shows significant similarities between users or triggers a normal plagiarism check, voting patterns are analyzed based on the group of voters, time of votes after the post and a (unique) set is extracted from multiple posts.

In a nutshell, if the intersection of all the voters exists & an intersection of these users against the "Good Samaritan" set exists, we have a match. [basic set theory]

In simpler terms, if the same people are voting the posts and there is a similarity of content, it means there is some sort of collaboration. It doesn't prove anything though.

so what are we saying ?

Well,

1. Its impossible to detect types of plagiarism where people are adapting content from non-digitized sources
2. Its very difficult and impossible to deny the possibility of using an army of paid users and models to generate original content
3. The scenario is perfectly legal as no one has a concern about copy right violations or any rights.

Do we have a deal ?

So the question is, if this business model is possible and working, do I get investors ? See, I even have the business model canvas downloaded and ready !!!!!

Questions left unanswered

• Are there proven instances of this happening ?
• What will be the stand of @steemcleaners & the community ?
• Is this morally & ethically correct ?

Before the down-votes come

The algorithm, the bot, API, NLP all are words of fiction. None of them exists. Any resemblance to movies under any licensing arrangements is PURELY coincidental but any resemblance to works under public domain and derivatives allowed creative commons share alike-commercial is not. Any resemblance with DEAD are coincidental if there is anyone with legal heir ship and ready to take legal action if not its intentional. No person living has nothing to do with it as all this is just a simulation. No bots, software, computers, unicorns or ICOs were hurt in the process of writing this article. No white papers were read, torn apart or burned during the process too.

(I didn't even press any buttons of anti-plagiarism software - I hired a pro by offering an upvote to do that for me!)

Like someone said, "Don't be fooled by randomness or the lack of it. All that matters is up-votes."

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