Unmasking The JonBenet Ransom Note With Stylometry Software (new additions july 2018)

The tragic and bizarre murder of JonBenet Ramsey is 20 years old. The ransom note from this case is analysed using the latest stylometric software to determine the authorship.

The program Jstylo has Writeprints as it's backbone, which "automatically extracts thousands of multilingual, structural, and semantic features to determine who is creating 'anonymous' content online. Writeprint can look at a posting on an online bulletin board, for example, and compare it with writings found elsewhere on the Internet. 

By analyzing these certain features, it can determine with more than 95 percent accuracy if the author has produced other content in the past." (University Arizona)

The software uses "cutting-edge technology and novel new approaches to track their moves online, providing an invaluable tool in the global war on terror" . (University Arizona)

Over the years there have been dozens of handwriting studies done, but considering that this long, rambling,strange and bizarre ransom note was designed to disguise handwriting (the letter a for example changes 6 times in it's construction), logically there would never be a match that would stand up in court.

Drexel Research University released anti plagiarism software called Jstylo which perked my interest in this murder case and the ransom note.

There are 375 words in the ransom note. Forsyth and Holmes show that a minimum of 250 words are required to attribute a document to an anonymous author. 
 R. S. Forsyth and D. I. Holmes, “Feature-finding for test classification,” Literary and Linguistic Computing, vol. 11, no. 4, pp. 163–174, 1996

This made it viable to test the ransom note against writing from Patsy and John Ramsey.

The software has been shown to be effective with accuracy rates of around 80% in identifying anonymous users on hack forums, with probabilities rising to 93%-97% accuracy in identifying a target document from among 50 authors (Abbasi and Chen). Rates drop to around 90% for 100 authors.

Its' also been used to identify programming source code authorship.

I downloaded their software JSTYLO

This is superb for a few reasons: it has embedded in it WEKA, an incredible data mining suite from the university of Waikato, NZ, and also WRITEPRINTS, the gold standard forensic stylometric characteristic generator for author identification with an automated interface.

Combined together, over 800 variables are created by Writeprints limited for each piece of text, which is then analysed by Weka for a linguistic "fingerprint" amongst all the test samples you give it.

Stylometry is the statistical analysis of writing style to identify authorship. This style involves many "invisible" words such as articles, function words, adverbs and pronouns which become unique to us as we develop our writing style, it not just a frequency count of obvious words. The hidden unconscious aspect of this makes it ideal for computer analysis. (James Pennebaker, The Secret Life Of Pronouns)

There was always suspicion on the parents because of their strange behaviour. There is interesting video interview footage on the internet, where they ask Patsy if she would take a lie detector test and she says she would whilst simultaneously shaking her head in a no motion, a classic incongruity between what was said and done (see former FBI agent Joe Navarro's book What Everybody Is Saying for more this non verbal cue).

Deceptive people use language differently to innocent people, see ten Brinke and Porter, Psychology, Crime & Law 2015). Another interesting study on language changes in deception relates to Dutch Professor Diederik Stapel who reported false data in 25 of his academic papers. The study compared his 25 fraudulent papers with his 25+ legitimate papers. Academic Fraud Study

The outcome: "This research supports recent findings that language cues vary systematically with deception, and that deception can be revealed in fraudulent scientific discourse."

For this post, I will only look at the stylometric aspect of the ransom not. A future post will look at the linguistics of this case.

Above: page 1 of the two and a half page ransom note.

I located 5 notes written by Patsy Ramsey, including 1995 and 1996 Xmas notes. I haven't had much luck locating anything sizable written by John Ramsey, however.

But I needed a placebo--lots of random notes and emails to test against.

Many universities are using the Enron Email Corpus from Carnegie Mellon--https://www.cs.cmu.edu/~./enron/

The email servers were seized during the Enron fraud trails where a dozen executives went to jail. After the court case, the emails were acquired by a university and have been made available for various political/social studies. It is the largest email corpus (1.5 million emails) which show day to day life in a large corporation.

The emails make a perfect training set, and have been used as that in various studies, as well as creating models such as being able to identify male and female writing with 80% probability.

Schein and Caver show that attribution accuracy is greatly affected by topic, I've tried to avoid this by greatly varying topics by using the Enron dataset.

http://stealthserver01.ece.stevens-tech.edu/gendercreatetext?count=9885

The reason the Enron corpus is being used by the University of British Columbia and others for language and social engineering studies is that Enron was in effect a small city -- it was a vast corporate structure that had thousands of daily emails on all subjects, from business, to small talk to flirting to deception.

I downloaded the Enron corpus and randomly selected about 60 emails and added the Ramsey letters mentioned above.

All this was put into Jstylo, the authorship attribution software.

The ransom note was put in the Test side, the 80 odd emails and text was in the Training side.

With all the emails and ransom note loaded in, I went to the data mining section and selected an  algorithm with the least error after cross validation which looks for similarity between the writing samples.

The next step was to run the "trained" model (trained on Enron and Patsy writing)  on the test writing (ransom note) and look for the closest match. In effect I am asking it--which text does this ransom note look most like?

Writeprints creates 800 variables per document, creating  a "sliding window" as it analyses a broad range of text characteristics.

Result--Patsy Ramsey at 75%.

I ran it again with different emails and text, and then different data mining algorithms, same result.

There is some good advice on which classifier to pick by Edwin Chan.


More....

Patsy died of cancer in 2006, and that is probably why the Police Commissioner Mark Beckner said they don't expect to make any arrests in the future, even though the case is still open.

Police Chief  Mark Beckner did participate in an interview on Reddit, and one of the questions that always stuck out to me was this:

Q: “When Patsy wrote out the sample ransom note for handwriting comparison, it is interesting that she wrote “$118,000″ out fully in words (as if trying to be different from the note).
Who writes out long numbers in words? Does this seem contrived to you?”Beckner: “The handwriting experts noted several strange observations.”

Update 1: Sept 2016

It has been pointed out to me by two people, DocG and also Eve Berger (no relation) from Linkedin that John Ramsey was also reported as having used the notorious "and hence" in an interview. I did find a transcript of this interview with both John and Patsy talking to student journalists, including an incredible part where Patsy says, "...Even If We Are Guilty.....".

Shades of O.J Simpson and "If I Did It.....".
That's worth a look all on it's own which I'll do in the next day or two.

John + Patsy Transcript

But, how unusual is "and hence"? Well, using the Google Ngram viewer which searches books from the 1800 to 2008, here's a graph I made:

Very uncommon, it would seem.
I will look into the linguistics using the interview material soon, referencing some of the recent automated deception detection methods.


Update 2:

I've had a few questions about Jstylo.

Let's get something out of the way, DocG asked me to use his text to test, which turned out to be speech, a NO-NO.  The results didn't work because it should be speech to speech, text to text. I told him this when I found out, and said it wasn't valid. He couldn't accept it because of the Sunk Cost Fallacy. He loved the outcome because he thought he had found a weak link. 

DocG said he uses "instinct", "intuition" and "social research experience".  I told him I was only interested in EMPIRICAL results against his "intuition", so we agree to disagree.

1 -- Firstly, Jstylo is a closed system.
This means that the suspect must be among the text samples you are analysing. The software will pick the closest match.

2 -- Speech with speech and text with text. People use language differently when they talk compared to how they write. Different parts of the brain are used for speech and writing. If you want to identify speech, use all speech as your input. If you want to identify written text, all your inputs should be text.

The Pennebaker text analysis software LIWC has frequency averages over many thousand of samples for blogs, speech, newspapers etc. This program shows the dramatic and consistent differences between speech and written text, see below for average frequencies.

3 -- Generally, the more text samples that you have from your target, the better. Recommended amount of text to ID document Target is 550 words, but Forsyth and Holmes show that 250 words is a minimum. For various authors to test against, about 5000+ words recommended.

I have been reading a study where reviewers on YELP are linked (identified) and where the reviews are only average 149 words in length: https://arxiv.org/pdf/1405.4918.pdf
I don't have more details on this.

4 -- There seems to be a way to create an open system with Jstylo, where if it doesn't identify an author, it won't just point to the closest match, but will come up with unknown.
http://www.stolerman.net/papers/classify_verify_ifip-wg11.9-2014.pdf
I don't have more details on this.

5 -- Jstylo is not a black box, it is an automated GUI or interface combining established open source established software: JGAAP, Writeprints and Weka. Writeprints uncovers writing characteristics. Input features can be added or removed, and the spreadsheet can be exported showing the most significant important variables.

 6 -- News, Academic papers and Security Conferences using Jstylo around the world:
https://psal.cs.drexel.edu/index.php/Main_Page

7 --  All software works on this principle:
garbage in = garbage out


Ransom Note Contradictions

The writer of the ransom note probably did not commit the murder, although they were part of the cover up. The note is a contradictory and naive attempt to use psychological misdirection to point the investigators in another direction.

First it's a "faction, (a small dissenting group within a larger group??), then there's a suggestion it may be someone at John Ramsay's workplace who is aware of his exact Christmas bonus, there are numerous movie quotes in an effort to appear more criminal, and a psychological attempt to issue a secondary threat of not releasing the body for "proper" burial because the writer knew the child was already dead.

The numerous contradictions involve telling a sleeping person to be well rested, not realising a kidnapper doesn't deliver a victim, crossing out deliver then using the word pickup.

There is also the issue of a kidnapper calling between 8.00-10.00am with delivery instructions, yet banking hours start at 9.00am, and the option of withdrawing the money earlier for an earlier delivery/pickup phone call from the kidnapper!

The CBS show established the murder weapon was the flashlight. The expert forensic pathologist was able to show that a 10 year old child could create the exact injury (same hole dimensions too) on a human skull with pigskin using the flashlight. The flashlight belonged to the Ramsey household, yet had been wiped of prints, as well as the batteries. The motivation to wipe the batteries clean becomes clear if you think about guilty knowledge.

Pathologist Dr. Werner Spitz said that the child was brain dead from the blow to the skull, so the intricate garrote was theatrical misdirection to shift attention away.

The Ramsey's themselves ignored nearly all the instructions on the note, they phoned the police, they invited friends over, John sent his friend to the bank, they had no concern when the telephone call deadline passed without incident, and so on.

Guilty knowledge relies not on lying but recognition of information you shouldn't know with resultant anomalous behaviour.

 911 Call

The 911 call also stood out in using the strange phrase, "We have a kidnapping..."
Many 911 calls are used to set up an alibi.
This one is no exception, IMO.

Check out FBI research on guilty and innocent 911 calls and their checklist.
https://leb.fbi.gov/2008-pdfs/leb-june-2008

Porter and ten Brinke 2015 note that females give off more guilty verbal cues than males, and that is certainly the case here with Patsy giving more red flag cues over the course of the investigation, particularly in her video interviews and her statements. Automated software using verbal and written analysis also confirms this.


Update 2 Sept 2016:
2nd Jstylo Run

I have been studying and testing more of the Jstylo software capabilities over the last week. I've decided to run it again over different training samples instead of Enron.

Drexel University provide different problem sets, and there is one with a couple of dozen authors, each with 4 or 5 pieces of text to test against .

I used 2 of the top classifiers here, Weka's SMO and Random Forest with 300 trees on a shortened version of Writeprints, Called Writeprints Limited.

I includes 2 of Patsy's known texts, and John Ramsey's written speech when he was running for office in Michigan.

Using different classifiers and different training authors from my first test, I got the same results with Patsy leading the pack in both classifiers and John Ramsey barely moving the needle. I removed each of the four texts from Patsy one at a time and retested, and each text made a difference --  each written text from Patsy contributed something to the classification. These are not probabilities, but ranking results.

Patsy has linguistic fingerprints on the ransom note. Even a visual examination shows she uses exact whole sentence structures, not just the words "and hence".

The first sentence is from the ransom note, the second is from her Christmas note to friends. The word delivery was crossed out and pickup was added when the author realised that a kidnapper would not deliver the kidnap victim back, but would phone to say where the victim could be found.

The complete sentence structure is identical, on each side of "and hence". It is part of her "linguistic fingerprint", besides all the invisible characteristics that get picked up by the Writeprints software.

Different software, different analysis--
Different Ransom Notes Comparisons Using Linguistic Inquiry and Word Count software

Also known as LIWC, this software from psychologist James Pennebaker from the University of Texas has been well validated and used in many studies, over 6000 on Google Scholar, to date.

According to Tausczik and Pennebaker:
"LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect  meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences."

LIWC has been used to in various studies, from assessing depression  to deception detection (Newman Pennebaker).

Of interest to me is the Gender analysis, again from Tausczik and Pennebaker:

"Sex differences in language use show that women use more social words and references to others, and men use more complex language. A meta-analysis of the texts Tausczik and Pennebaker from many studies shows that that the largest language differences between males andfemales  are in the complexity of the language used and the degree of social references (Newman, Groom, Handelman, & Pennebaker, 2008). Males had higher use of large words, articles, and prepositions. 
Females had higher use of social words, and pronouns, including first-person singular and third-person pronouns."

I located 2 more actual ransom notes, the longest ones I could find. These are the Barbara Mackle kidnapping and the Leopold and Loeb kidnapping. All the kidnappers were caught and convicted and were men.

LIWC was run on all the ransom notes as well as a complete average on 4 of Patsy's notes she wrote.

As per Pennebaker above, the Mackle Leopold notes have no I pronoun and lower We  He She pronouns. Women use less articles and again the Mackle Leopold notes have more articles. Women use more social words, and the JonBenet note has very high social language.

What is very interesting here is that anxiety of the letter writer is revealed in writing, and even though that JonBenet note was written in the house and would have taken about half an hour to write (21 minutes just to copy it, as the CBS show noted), there was NO anxiety. Yet there was anxiety in the other pre-written notes!

Also, as a measure of authenticity, the JonBenet note is very low and there were more tentative words (not shown, but also a female indicator).


3rd Supporting Software Analysis


Whissell's Dictionary of Affect is a very useful measure of pleasantness, not what the words mean but a sentiment rating of the overall pleasantness of the text.

I have found a direct correlation to pleasantness and deception, and a study at Columbia University confirms this, but increased social language increases pleasantness too:

The JonBenet note is above average in pleasantness and social language and higher than both other ransom notes, showing it more likely to be written by a female as per Pennebaker above.

As FBI profiler Roger Depue wrote in his book, the ransom note was essentially nonsensical, obviously staged, and  was "feminine" with terms such as "gentlemen watching over", and telling sleeping people to "be well rested.".

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MORE on Analysing the Syntactic and linguistic Structure of the JonBenet Ransom note, or taking the words away and leaving the parts of speech and Syntactic Tree structure here of the text:

http://www.elastictruth.com/2017/04/new-analysis-of-ramsey-ransom-note.html





FBI interest on this website:

I have had a very amicable email exchange with Frank Marsh from the FBI who wanted to know a bit about training suggestions and material on statement analysis etc. It's a credit to the agency that they take the time look around to see if there is any new information or techniques they need to know. I blocked out a few details above for privacy. I would love to take up Frank on his offer of a dinner "next time I am near Quantico."



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NEW ADDITION July 2018

In Clustering Analysis, variables that are similar to each other form a cluster or group. The software runs through the data in an unsupervised way which means there is no targ et variable used, and looks for the closest and most similar clusters. It is similar to a decision tree, but works without a classification variable.

The software I am using is the free MDL Cluster software: https://www.kdnuggets.com/2016/08/mdl-clustering-unsupervised-attribute-ranking-discretization-clustering.html

It is very efficient, on par or better then k-means and EM and works as a Java exe in standalone mode. It can also be used for discretisation and corpus building, although I won't be using those capabilities.

I wanted to see what other ransom notes have in common. Obviously at surface level and at the most basic, they all have demands, a list of instructions, possibly a threat and so on.

The JonBenet ransom note was a very long rambling note of 370 words.
I managed to find 3 other complete ransom notes, two even longer than the JonBenet one:

1 - The Leopold and Loeb random note of 401 words, https://en.wikipedia.org/wiki/Leopold_and_Loeb

2 - The Barbara Mackle ransom note at a whopping 972 words, https://en.wikipedia.org/wiki/Barbara_Mackle_kidnapping

3 - The Rob Wiles ransom note at 152 words, https://crimewatchdaily.com/2017/06/08/ransom-arrest-conviction-but-no-body-what-happened-to-robert-wiles/

Along with the ransom notes, I have four notes from Patsy Ramsey, titled Patsy 1 + 2 and Patsy 1995 and 1996. A 2110 word letter from John Ramsey was included in this experiment.

The idea is to see what a clustering algorithm would find by lumping Patsy and John Ramsey along with the four ransom notes. The software is blind to who the note belongs too--the classification variable which specifies the owner of the note is NOT used by the software. In other words, the clustering software is looking for similarities.

At the basic word level, all the ransom notes are similar. This is obvious and useless, a ransom note is completely different to a Christmas Card for example.

So we need to look at a deeper level. I have already looked at a syntactic level in another blog post, now I want to used LIWC, the linguistic inquiry and word count software from James Pennebaker at the Uni of Texas.

http://journals.sagepub.com/doi/abs/10.1177/0261927X09351676?journalCode=jlsa

The built in dictionary has categories for things like anger, negation, function words etc. I wanted to try this on a custom built dictionary by Jeremy Frimer called the Prosocial dictionary--
http://www.pnas.org/content/112/21/6591

This has been used in interesting hypotheses, such as a decline in prosocial (helping, caring language) language tracking with dissatisfaction of politics. They have even created a model tracking approval ratings of politicians based on their prosocial language.

I downloaded the Prosocial Lexicon and ran it over the ransom notes. This added up how often certain words in certain categories appeared in each note:

There were about 127 columns, many sparse, only a few are shown here. This is now the input for the MDL Cluster software. It ignores the last classification variable of who the author of the note was, and runs through all the variables, looking for similar groups or clusters.

The output using the best 20 variables was:
Attributes: 21
Ignored attribute: filename
Instances: 9 (non-sparse)
Attribute-values in original data: 57
Numeric attributes with missing values (replaced with mean): 0
Minimum encoding length of data: 450.94
---------------------------------------------------------------
(48.70) (9.74)
#ProSocial<=0.008772 (11.45)
  #support*<=0 (-0.66) [0,1,0,0,0,1,1,0,0] jon_ransom.txt
  #support*>0 (-2.00) [0,0,0,1,0,0,0,1,0] mackle_demand.txt
#ProSocial>0.008772 (11.57)
  #help*<=0 (-2.00) [0,0,1,0,0,0,0,0,1] leo_loeb.txt
  #help*>0 (-2.00) [1,0,0,0,1,0,0,0,0] john_letter.txt

---------------------------------------
Number of clusters (leaves): 4
Correctly classified instances: 4 (44%)
Time(ms): 41


A new spreadsheet was created by the software, showing the clusters:

Four clusters were found. Cluster 0 shows Patsy 1995 and Patsy 1996 lumped with the JonBenet ransom note!! There was similarity with Patsy2 note and the Mackle note in Cluster1, as well as John's letter and Patsy 1 in Cluster3. Rob Wiles and the Leopold and Loeb note were put together in Cluster2.

A completely automated clustering approach with NO information about who wrote which note, groups Patsy with the JonBenet ransom note, even though on the surface, all the ransom notes appear similar in that there are demands and instructions and so on.

The Prosocial Dictionary tracks helping and caring language, it could probably be thought of as an empathy indicator which has proved useful for a few studies. It seems that the Patsy notes and the JonBenet ransom note are in the same cluster because of a low level use of caring language in the ransom note which has a similar "signature" to Patsy. It has been observed by a few people that the ransom note is "feminine" in the way it is written, talking about being well rested and so on. This is confirmed with various online Gender handwriting analysis sites when the ransom note is analysed.

Another potential direction is the new field of Sentiment Analysis, used to detect the sentiment in product reviews, hotel reviews and so on--
https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html

An exciting new method is DepecheMood, which used 37 000 terms along with emotion scores--
https://arxiv.org/abs/1405.1605

They have built an online website to test text--
http://www.depechemood.eu/

Plugging the ransom notes into Depechemood shows different sentiment--

The JonBenet Ransom Note above

The Mackle Note above

The Leopold Note above

The Robe Wiles note above


It's interesting to see that the two top emotions in the JonBenet ransom note (top) are Sadness and Anger, consistent with what you would expect if the CBS special scenario played out ie JonBenet being accidentally killed by her brother as she snatched some pineapple from him during a late night snack.

More to follow......