I’ve been asked by a number of people if the images seen in the video of the October 11 Minnesota riots that took place during President Trump’s rally show Ilhan Omar, Tim Mynett, and Isra Hirsi. Running the video footage through face recognition (FR) came back inconclusive with the following match rates:
Face recognition for Ilhan Omar = 52% Face recognition for Tim Mynett = 71% Face recognition for Isra Hirsi (Ilhan Omar’s daughter) = 56%
The reason for these low scores is that the Persons Of Interest (POI) that resemble the three individuals have key facial features such as the nose, mouth, lips, chin, either obscured or distorted. That said, several other video analytics did find multiple partial matches on general personal characteristics such as ethnicity, body size, face build, hand size, and scarf wrapping style. Tim Mynett’s glasses for example, matched at 94%.
Face Cover and Proximity Evaluating the appearance of the various POIs in the footage shows that they didn’t cover their faces at all times. In several instances, POI-1, who resembles Ilhan Omar took the scarf of her face. POI-4, an accomplice of POI-1 (the same ethnicity), didn’t cover her face at all and was wearing her head scarf in a casual non-traditional Somali wrap. POI-2, who partially matched Tim Myentt also periodically removed his face cover. This suggests that at least some of the participants didn’t have extreme privacy concerns or believed that alternately appearing with/without a face cover would be beneficial. From the spatial awareness point of view, POI-1-4 repeatedly moved in and out of the Field Of View (FOV) and operated at a very close proximity to the photographer. In at least 2 occasions reaching as close as 18-36 inches from the camera.
Image 1: POI-1 and her accomplice POI-4 frolicking with uncoverd faces in front of the camera
Motion Dynamics Following the FR, I also performed motion analysis of the four POIs identified. Motion pattern analysis examines the movements of individual objects in a field of view and classifies them according to their trajectory, velocity, and movement pattern. In a typical public gatherings such as demonstrations, airport/subway passenger traffic, sport events, street traffic, etc. individuals and crowds tend to exhibit certain patterns of motion like loitering, flowing at the speed of traffic, coordination, queuing, pacing, etc.). The results of this analysis flagged the following anomalies:
POI-1 who resembles Ilhan Omar didn’t move organically within the FOV as did the other demonstrators. She loitered around the camera with another female accomplice (POI-4 of the same ethnicity) and seemed to be more interested in being observed by the camera and less in joining the demonstration at the police bicycle barricades where the action was taking place.
At one point in the footage, POI-1 that resembles Ilhan Omar turned to walk away towards the camera, she noticed the camera, and quickly turned around and used both hands to adjust the scarf on her face. She then did a 180 degree pirouette and walked right back in the same direction towards the camera. Typically, this would not be the pattern of motion for a person who is trying to avoid being seen. Rather, one would expect her to turn her back to the camera and walk to either side or straight ahead into the crowd to avoid being identified (see Video 1).
POI-1 who resembles Ilhan Omar exhibited coordinated motion with POI-2 (they frequently shared the FOV). Both moved fluidly, slowly, and deliberately as if to provide image capture opportunities.
POI-2 who matched Tim Mynett also interacted with the camera on multiple occasions in the same way as POI-1 did. On at least one occasion, he walked right across the FOV with a clear profile shot and at one point deliberately pulled down his scarf exposing his face and faced the camera. He was aware of his action because he made direct eye contact with the center off the lens (Video 1).
Video 1: POI-1 and POI-2 coyly promenading in front of the camera
Image 2: The Minnesota riot video POI match analysis details
Linkage and Geospatial Analysis Running a linkage analysis on several entities involved in this video shows a direct relationships between the actors and Ilhan Omar. For example, Andy Mannix, who recorded the video of the riot is a MinPost reporter that knows Ilhan Omar and Tim Mynett.
Mannix, also knows Cory Zurowski from his days in the Minnesota City Pages newspaper. In 2016, Zurowski published a fictitious biographical piece about Ilhan Omar (he didn’t verify any of Ilhan’s bio claims) and in that article had a little Freudian slip and fall and identified Ilhan Omar’s real family name as Elmi, but then promptly changed it back to Omar. Andy Mannix also happens to be married to Briana Bierschbach, a former AP reporter who currently works for Minnesota Public Radio as a political correspondent. Bierschbach interviewed Ilhan Omar on multiple occasions and wrote several supportive articles about her. The Bierschbach Omar stories are political puff pieces that are light on investigative facts and heavy on personal aggrandization and read like a press release written by Tim Mynett (who is Ilhan Omar’s PR manager/latest romantic interest). The common theme in all of these writings is to highlight Omar’s heroic qualities and whitewash the dubious details about her and her family’s history.
From the geospatial point of view, having Mannix, Mynett, Hirsi, and a crypto Omar within a radius of 10 feet of each other, without either one of them being aware of the others is suspicious to say the least. This act reminds me of the plot line in the P. G. Wodehouse’s novel “Jeeves Takes Charge”. In it, Jeeves’s makes the following observation to Bertie Wooster:
“Any undertaking that requires the presence of four people all in one place, all at the same time, while two of them are unaware of the fact, is fraught with the possibility of mishap sir.”
Image 3: Andy Mannix, Briana Bierschbach, Cory Zurowski, and their Ilahn Omar linkage
Prophetic Visions of Fake News Mannix’s social media activity prior to the rally may also be relevant to this discussion. On October 10 at 11:40 AM, almost a day before the riots started, using what seems to be a prophetic vision, he twitted the following prediction for the upcoming event:
“There will also inevitably be a lot of fake [news] or unsubstantiated claims…”
Suspiciously, the only viral fake news story that came out of this event is associated with the footage that he recorded.
Image 4: Andy Mannix’s prophetic tweet about future fake news relating to the Minnesota Trump rally
It certainly seems that the whereabouts of Ilhan Omar, her daughter Isra Hirsi, and Tim Mynett during the riots fit into the debate about ‘unsubstantiated news claims’. What is not clear though, is what role did Mannix and his progressive reporter network played in engineering this event.
Conclusion My take on this is that based on the positive match for Tim Maynett and Isra Hirsi and the multiple partial matches for Ilhan Omar there is a strong possibility that this was a publicity stunt with a look-alike done for the purpose of crowd sourcing a false identification. One possible reason for doing this might be to use this incident to discredit the ‘conspiracy theorists’ by showing that Omar was at a different location at the time this video was shoot. This would then allow her PR team to leverage the false match and use it to repudiate other successful searches that positively confirmed her second husband as her brother.
This also suggests that someone on the Omar team seems to be concerned about the previous usage of video analytics to identify her dubious family linkage and is trying to develop some counter narrative to address it.
Despite numerous interviews, media write-ups, and multiple biographical sources, Ilhan Abdullahi Omar AKA “Ilhan Nur Said Elmi” remains an enigma. As strange as it sounds, there is almost no verifiable information about her nor her family in the public domain. This is bordering on the fantastic considering the fact that she is a ±34 year old (she has no birth certificate) sitting congresswoman. In an era where comprehensive digital background searches can be executed with a single mouse click, Omar’s fluid and ever shifting identity and invisible past are a puzzle.
The majority of the biographical information that she disclosed has multiple non-reconcilable versions. This includes significant inconsistencies about basic facts such as:
Her family’s actual size
Their real names
The family wealth and real-estate ownership in the US and Somalia
Any evidence that the family spent years in the Dadaab refugee camp instead of the resort city of Mombasa
The real reason for their escape from Somalia
The problem with Omar’s exceptionally poor recollection of facts and propensity for embellishments isn’t limited to the period between 1990-2011. Just a few weeks ago, on May 28, Omar delivered the following speech in the Richfield high school about her first hand experience with a “sweet old African lady”:
“sweet, old . . . African American lady” who had been arrested for stealing a $2 loaf of bread to feed her “starving 5-year-old granddaughter.”
After spending the weekend in jail, the woman was led into the courtroom and fined $80—a penalty she couldn’t pay. I couldn’t control my emotions, because I couldn’t understand how a roomful of educated adults could do something so unjust.”
If you haven’t noticed, this plot-line suspiciously resembles Jean Valjean’s crime in Victor Hugo’s Les Miserables.
As with most of her claims and recollections, this story too is unverifiable. She doesn’t remember where or when it took place, what court handled the case, or who the “sweet old. . . African American lady” was.
Image 1: Ilhan Omar delivering the speech about the “sweet, old . . . African American lady” who had been arrested for stealing a $2 loaf of bread to feed her “starving 5-year-old granddaughter
The fluidity of the Oma’rs background suggests that most of the details that we know about her have been engineered. It is also clear that over the past 27 years, core components of the Omar family dossier have gone through multiple revisions at the hands of someone who has a decent grasp of covert intelligence operations.
For example, between Ilhan, her father, current husband, and one of her sisters, they have 32 name variations that are being used interchangeably in various official registries.
Ilhan Omar Ilhan Ilhan Omar Ilham Umar Omar Ihan Omar Ijhan Ilham Omar Ilham A Omar Ilhan Abdullahi Omar Father Abdullahi O Mohamed Nur Mohamed Nur Omar Mohamed Nur Said Elmi Mohamed
Current Husband Ahmed A Hirsi Ahmed Aden Ahmed Hirsi Ahmed Abdisalan Hirsi Ahmed A Aden Ahmed Hrisri Ahmed Hersi Ahmed Haji Ahmed Hassan
Sister (US Based) Sahra Abdullahi Omar Noor Sahra Omar Sahra Sahra Noor Sahra Omar Sahra A Omar Sahra Abdullahi Omar Sara Omar Sarah Omar Kadra Omar Zahra
Ilhan’s perpetual reenactment of the comedy of errors of family name/identity mismatches is not an English as a second language issue. The same pattern appears when she communicates in her native Somali tongue. During her 2016 visit to Somalia and a meeting with president Mohamed Hassan Sheikh, she told the local media, in Somali, that the man by her side was her husband Ahmed Elmi. Running face recognition on the images taken by a Somali state media service photographer shows that the person accompanying her was in fact Ahmed Hirsi.
Image 2: Photographing Ilhan Omar and her husband Ahmed Hirsi visit with the Somali president. Images taken by a Somali state media service photographer.
As can be seen in Image 3, shortly after her marriage issue surfaced the photos in the article were deleted from the server. I contacted “Hassan Istiila”, the author of the article and asked him why he removed the images, he denied doing so himself (see email exchange in the references). The post edit history and server logs show that an admin user by the name of Farsamada AKA Liibaan Mohamoud Yusuf Nabiil was the one who removed the images from the WP CMS used by Radio Dalsan. It also turns out that Farsamada is a dedicated Ilhan Omar supporter/promoter in Somalia (see Image 5).
Image 3: The Mogadishu Radio Dalsan article about Ilhan Omar’s 2016 visit to Somalia and her meeting with the Somali president Mohamed Hassan Sheikh. The article identifies her husband as Ahmed Elmi but in fact it is Ahmed Hirsi.
Image 4: The Mogadishu Radio Dalsan 2016 article with some of the recovered images showing that the person accompanying Ilhan Omar was in fact Ahmed Hirsi traveling under the name Ahmed Elmi (Ilhan Omar only divorced Ahmed Elmi in 2017).
Image 5: Farsamada AKA Liibaan Mohamoud Yusuf Nabiil, the Somali news editor who purged Ilhan Omar’s images from the Mugadisu Radio Dalsan article that identified Ahmed Hirsi as Ahmed Elmi.
Omar’s 2011-2016 trips to Somalia also raise questions about potential FARA violations, her funding sources, and the actual purpose of her visit with the Somali president and local business leaders. From her itinerary it is clear that these were expensive state functions that took place years before Ilhan was elected to Congress. So who exactly paid for these visits? And why did the Somali president “requested her presence” at his villa? Unless, of course, Omar was there on a crypto-Elmi family financial/political expedition or as a private US/Somali power broker for promoting the interests of terrorist sponsors like Ahmed Nur Ali Jim’ale.
Another good illustration for this steady stream of name/identity shell games and biographical revisionism is her famous ‘militia assassination’ story. This tale which forms the foundation of Ilhan’s Somalia fairytale escape narrative (and likely the basis for the US asylum application) details a traumatic night in Mogadishu when her family’s secure compound was attacked by an unknown rebel militia who tried to “assassinate” her entire family.
The following are two versions of this story as told by Omar in 2016. The first version is from her interview with Cory Zurowski from CityPages News. The second version—which she told a month later—is from an Omar propaganda featurette that was produced by Daniel Cummings.
1. Ilhan Omar’s Improbable Journey from Refugee Camp to Minnesota Legislature CityPages News – Published 26 October, 2016 “Nighttime fell as about 20 people milled about the compound in Mogadishu, the Somali capital. Bad noises outside announced unwelcome visitors. Men with big guns demanded to be let in. The group tried to bust down the front door, but it was unbreakable. Omar and her family fell to the floor moments before the militiamen let go a staccato of gunfire. Once they were satisfied with the evening’s damage, the attackers left.
Everyone survived. Omar hasn’t forgotten the sight of bullet pockmarks in the building’s cinderblock walls.
Shellshock turned to grief for Omar’s grandpa. “That was a hard realization for my grandfather, that our family was no longer welcome,” she says. “Even after the attack, he struggled with this new reality.”
Days later, they put their familiar world in the rearview mirror. They split into groups, 20 people in total fleeing. Omar’s headed for the coast where Abukar [grandfather] had connections. Alongside her father, she hopped a plane for Kenya.”
[Go to 55:14 in the recording to hear the description below]
“Think my earliest memory of when the war actually felt real was waking up you know midnight… to militiaman surrounding our house. My aunt and older sister having a conversation through the window, sort of a negotiation with them because these were classmates of theirs, umm… and… and…and… negotiating them to sort of back down… and umm…and trying to break into our house and assassinate us.
We ended up surviving that night then my grandfather said that we like had to leave. We all left thinking that we’ll come back…that umm… you know in a few months the war would end, so we sort of fled I think like at 4 AM.
And walking through this… this…streets and actually, umm… walking over dead bodies and seeing debris and buildings that have fallen apart umm…sort of land marks of like hotels that you know, umm… that had movie theaters and all of this that we used to go by and… and… see that they no longer were standing tall…”
Both of these versions have major structural flaws. This includes discrepancies in the timeline, the sequence of events, the number of individuals involved, and the plot line. As can be seen from the table below, there are embellishment and missing elements in each version. For example, the claim that the militia opened fire at the house vs. that they retreated peacefully after the aunt and sister negotiated with them or that the militiaman were classmates of her aunt and or sister.
20 men in the militia
Compound in Mogadishu
Militia outside the compound
Militia surrounded compound
Aunt & older sister talking to militia
Militia classmates of aunt and sister
Man with big guns
Trying to bust down the door
Militia tried to assassinate them
House door was unbreakable
Omar and family fell to the ground
Militia opens fire at the house
Memory of bullet pockmarks on wall
Family not welcome in Somalia
20 family members fleeing
Catching a flight to Kenya
Dead bodies & destroyed buildings
Grandfather said we had to leave
Table 1: A sample of the variations in several tales of the Somali rebel militia storming the Omar compound in Mogadishu
Both versions of the story make little sense, especially the preposterous assertions Omar makes about the ‘magic door’ that could not be breached and how her family, consisting of 20 individuals (who weren’t connected to the brutal dictator Said Berra) managed on short notice to get hard currency, valid passports, visas, airline tickets, transportation to the airport, and a safe passage through multiple checkpoints controlled by Mohamed Farah Aideed’s militia to catch an international flight to Kenya.
Following this and many other Omar biographical dichotomies, I decided to examine her public statements regarding her family history. For any other US elected official, these types of gaping holes in a personal and family history would normally trigger a lynch mob of angry MSM investigative reporters. But as it has been already demonstrated, Congresswoman Omar seems to have managed to somehow acquire the ring of Gyges and will not answer questions on these subjects because she deems them too offensive and an invasion of her privacy.
Image 6: Somali refugees and their political affiliation with the regime of the Somali dictator Mohammed Siad Barre
So instead of hoping that someone will finally wake up and bring her in for questioning, I figured why not do the next best thing–take some of her video appearances and run them through a software based polygraph analysis.
The Evaluation Process I wanted to conduct the evaluation in an impartial manner and eliminate the Christine Blasey Ford effect and the observer bias. To achieve this, I’ve only used movies of Omar that had her speaking clearly and audibly and featured a close-up view of her face at a sufficient resolution. This was important for facial feature extraction. I also automated the image optimization and evaluation process.
In assembling the virtual polygraph system, I used several Artificial Intelligence platforms to perform the speech and facial expression evaluation. For the facial and speech analytics I used:
For the speech analysis I used several algorithms to identify conversational dynamics and the detection of deceit through functions and patterns such as:
Usage of filler words like “Umm”, “and”, “Like”
Cut out contractions such as using “did not” rather than “didn’t”
Increase/decrease in speech speed, pitch, and tone
Prolonging word vocalization to buy time
Repeating or rephrasing questions
Voice pitch changes
I calibrated the facial expression engine and established a baseline using several hours of Omar’s speeches on a variety of ‘neutral’ subjects. I then established her ‘truth’ baseline using over 1200 factual statements (i.e. statement known to be true). I also supplemented her baseline facial expressions with catalogue of over 230 combinations of her customized Microexpression.
Following the creation of a unique database of her facial expressions, I proceeded with the ML training using her unique data sets. The whole training process took about 32 hours.
After the training was completed, I fed several movies into the engine and evaluated the accuracy of the system performance in terms of false detection and missed detection using a manual ground truth evaluation. During that evaluation, I examined several hundred individual video frames, correlating them to what Omar was saying and the AI detections of face expression.
The results of the ground truth successfully confirmed the accuracy of the training data and the precision of the speech and facial expression algorithms.
Image 7: Sample of the facial analysis results for one video frame out of about 120K frames analyzed.
Image 8: Sample of a few of the over 4000 frames analyzed from the ‘Militia Assassination’ sequence
Image 10: Sample images showing rapid eye flickering and fidgeting (soothing behavior) correlated to the ‘Militia Assassination’ sequence
Image 11: Sample of an image that has gone through the optimization and enhancement process prior to using it in facial analysis
Analysis Results The textual analysis shows that when Omar is talking about the gray parts of her biography her first person singular pronouns decrease, her negative emotion words increase, and her complex word choice decrease. This is most likely attributed to an increased cognitive overload that is associated with creative thinking. She also uses many more action verbs then average in these segments which suggests that she want’s to keep the story moving and discourage a challenge on the part of the listener.
The speech and facial sentiment analysis strongly suggests that Omar was deceitful when discussing her family Mogadishu history. She exhibited all of the typical facial and body mechanics indicators for dishonesty including:
Omar’s analytic ‘lie’ score consistently exceeds her ‘truth’ baseline by 30%-80%. Her patterns also exhibit many indications of a compulsive fibber. She is a a great storyteller; her tales tend to be very detailed and colorful and she seems very convincing. She is a natural performer, she is eloquent, and she knows how to successfully engage her audience. When providing historical information about her family, she never does so matter-of-factly (the way most people retell their family histories). Rather, Omar’s narrative is always epic and geared at acquiring the listener’s admiration and sympathy.
The following are a few examples that suggest that she could be either a pathological liar or is working with well written PR scripts:
She often talks about experiences and accomplishments in which she and her family play heroic parts
She is often a victim (of physical violence) in her stories and is habitually looking for sympathy and protection
She is always brave, virtuous, better then her adversaries, and beyond reproach
Her stories tend to be very detailed but lack any verifiable data
She responds elaborately and quickly to questions but her responses are always vague and almost never answer the question
She has different versions of the same story and uses all of these versions at times
Confirmed facts about her and family don’t support her version of the events
Image 12: Omar’s sentiment chart showing her emotional state when discussing the ‘Militia Assassination’ event
Omar habitually exhibits this pattern of evasiveness and embellishment in all of her narratives. For example, we know from multiple interviews that the count of her siblings changes and varies at any one time from 3-7. This begs the question of what her family’s real story is and why are they so reluctant to disclose it.
Omar is a naturalized US citizen with a very ambiguous/suspicious background that doesn’t seem to match her official biography. As a public figure and congresswoman, she has or will have access to sensitive and classified state secrets, as such, it may not be a bad idea for DHS and INS to run her record in order to eliminate any potential security breaches downstream.
Anyone working for any three letter agency must go through a background screening and a detailed character evaluation. Some even are mandated to take periodic polygraph tests. There is no reason why a top lawmaker like Ilhan should be exempt. If her biographical details match her official story, then she should get the royal stamp of approval and be done with all the speculation. If it does not, then there should be full public disclosure of the details and a congressional hearing regarding the discrepancies.
In 1995, when Ilhan was about 12, she finally got her wish and arrived to New York. While being driven to her new suburban home in Arlington Virginia she recalled the following memory:
“Staring out a car window, felt ripped off. This wasn’t the same country as the one showed to her on the INS orientation videos. Instead, it was a headache of car horns and homelessness, an eyesore of trash and graffiti.”
Image 13: Parts of the Hirsi Omar enterprise in Minnesota
Image 14: Ilhan Omar and Tim Mynett her strategist at a CAIR event hosted by Hassan Shibly, Ilhan holding hands with Mynett, dinning out with him, and spending a lot of ‘personal’ time together while she is still married to Ahmed Hersi.
Image 15: Ilhan Omar’s 2018-19 financial records showing over $250K in payments to Tim Mynett’s E Street Group LLC for his fundraising and consulting services.
Image 16: March 23rd-24th 2019, Ilhan Omar and Tim Mynett holding hands and dinning at the Café Penguini Italian restaurant in Playa Del Ray.
Image 17: One of the image sources used to establish Ilhan Omar baseline of facial expressions associated with deceptive behavior. In this interview she denies her affair with Tim Nynett and separation from her husband Ahmed Hirsi (both of which were confirmed)
Image 18: Ilhan Omar and her friend Hassan Shibly, the CAIR-Florida Chief Executive Director. Shibly is the one who wrote Ilhan’s speech at the CAIR fund raiser in LA where she stated that “Some people did something”. Shilby is on the US Terror Watchlist, CAIR and it’s members are also designated a terrorist organization by the UAE government.
From their financial enterprise, it seems that the Omars were taking careful notes while watching the INS movie about the freedom of religion and economic opportunities in America. It also looks like they have been binging on Dallas and Beverly Hills, 90210 style shows. It’s a shame, that they never got to watch any episodes of Law and Order.
References: Email exchange with Hassan Istiila From: Hassan Istiila <email@example.com> Sent: Sunday, August 04, 2019 5:54 AM To: <Apelbaum> Subject: Re: Question about one of your articles
Thank you for reaching out, i left the Radio Dalsan a year ago so i don’t know why the images was being removed and i don’t have the images now.
Former Editor, Former freelancer with News Agencies. Blogger, social media trainer, media consultant, researcher and Human Rights Activist.
On Fri, Aug 2, 2019 at 7:22 AM Y A <Apelbaum> wrote:
Hi Mr. Istiila,
I am writing an article on Ilhan Omar’s visits to Somalia in 2016 referencing your article below and was hoping that you can help me.
I’ve noticed that five images that came with the article were just recently removed. Would you be able to share these images? Also, any reason why you identified her husband in the article as Ahmed Elmi instead of Ahmed Hirsi?
وَيْلٌ لِّكُلِّ أَفَّاكٍ أَثِيمٍ A fake refugee hatched out a plan And perched on it like a crafty hen: This self righteous adulteress maid, Has made an art of this charade.
A congresswoman on a hate crusade Should carefully watch her vile tirade: Appreciate the kindness done to you, forget not you owe your life to a Jew.
No matter how crafty a liar is Allah is mightier than she is: He raises up the righteous to a bliss, And brings deceivers to their knees.
Copyright 2019 Yaacov Apelbaum, All Rights Reserved.
On July 24th at 8:32 AM EST, all eyes and ears were turned to the former special counsel, the honorable Robert Mueller. Going into the hearings, the Republicans hoped to expose multiple structural cracks in the report. The Democrats, on the other hand, tried to get just one conclusive evidence of collusion and election tampering to justify impeachment.
Just like other interested citizens, I have been following the Russian collusion and DNC email hacking saga since 2016, so naturally, I expected that Mueller would address some of the key findings in the report, but alas, my hopes for insight and clarity were dashed. What promised to be simple Q&A session turned out to be a painful, 454 minute game of charades where you never get to figure out any of the answers.
At 8:54 AM, 48 minutes and 18 seconds into his sworn testimony, Robert Mueller–the consummate DC political bureaucrat–activated his industrial strength fog machine and deployed a force field deflector shield. This set in motion a reoccurring pattern of ducking, dodging, and sidestepping direct and specific questions about his pet project report.
Despite the lack of clarity in his answers and his alarming unfamiliarity with his own work (e.g. not knowing who Fusion GPS was), I found the session to be insightful and a veritable treasure trove in terms of body language, image artifacts, and audio content worthy of analysis. Mueller spoke for about 7 hours and provided a rare opportunity to capture his conversational patterns, facial characteristics, and behavioral fingerprint when under duress while in a single continual homogeneous session–and all of this in a well lit environment in front of high resolution cameras. For video analytics, It don’t get no better than this!
A Note About Lie Detection Nonverbal queues or AKA body language is a form of communication. It is similar to verbal communications expect that it’s done through facial expressions, gestures, touching, physical movements, posture, bling, tone, timbre, and various speech and voice characteristics. Nonverbal behavior comprises a large percent of all interpersonal communication and can provided insight into a person’s thoughts and feelings.
The theory behind the ability to detect lies from body language is that most people who are lying find it difficult to maintain physical and mental comfort under ongoing questioning. The result is observable distress in their speech and appearance. This is because disguising the truth requires significant amount of left brain creative processing, that in turn, increases cognitive load as the person struggles to ‘make up’ answers to what would otherwise be fast memory recollection responses.
That said, there is no such thing as an accurate lie detector. Polygraphs or professional body language readers can only spot person’s discomfort and stress as they relate to certain topics of conversation and then focus on these areas for further analysis. If the annals of polygraph testing teach us anything, it is that professional liars like Aldrich Ames, Robert Hanssen, and Kim Philby (who ironically wrote the chapter about catching double agents), were resistant to lie detection.
It is also relevant to note that criminal courts usually don’t accept polygraph tests or body language reading as evidence because they are considered unreliable by academic psychologists (Christine Blasey Ford may disagree with this finding) and by reputable scientists. In addition, the person who administers and assesses the test has a great deal of control over how the test is conducted and its outcome. This, by itself, can completely skew or invalidate the test.
An Experienced Counter Intelligence Officer When evaluating Mueller’s testimony, it is important to remember that he is a professional with years of experience in debriefings (over 80 congressional testimonies), legal depositions, interrogations, and counter intelligence work. This was evident in his testimony. With a few exceptions, he avoided taking the bait from hostile questioners’ and utilized common counter-interrogation techniques such as draining the clock by asking for questions to be repeated (18 times), requesting the speaker to cite and point to the specific references in his copy of the documents (9 times), endlessly paging though his folder without finding or reading any of the referenced content (7 times), and answering at length about unrelated issues.
Mueller’s most frequent deflection tactic was to use I-phrases such as “I can’t get into…” or “I’m not going to…”. The former special special counsel declined to answer all relevant questions about topics such as the Steele Dossier, Fusion GPS, the usage of paid informants, and the genealogy of the FISA applications. As can be seen in Table 1, out of about 230 total questions, Mueller dodged about 198 and only provided vague non-committal responses to 10 others. This amounted to failing to answer about 87% of all questions.
This was quite a performance for the shining knight of justice, especially if you consider the DOJ mission statement of:
“To enforce the law and defend the interests of the United States according to the law; to ensure public safety against threats foreign and domestic; to provide federal leadership in preventing and controlling crime; to seek just punishment for those guilty of unlawful behavior; and to ensure fair and impartial administration of justice for all Americans.”
The key operative word here is “ensure”, not try, attempt, or do your best, but to verify and confirm.
Chart 1: The distribution of Mueller’s instances of dodging or refusing to answer questions during his testimony
Mueller’s Response Algorithm Mueller was selective in what questions he deflected. To the casual observer, it may have seemed that he was laconic across the board, but that wasn’t’ the case. In multiple non–sequential instances, he provided elaborate and definitive responses to questions but these were almost exclusively from Democratic Congress and Intelligence Oversight Committee members. With a few exceptions, most of his verbose responses could be categorized as being damaging to President Trump.
Image 1: Mueller’s Tag Cloud of the types of words and phrases that he used to avoid answering the questions. The operative sentence that proceeded most of these words was ”I’m not going to…”
As can be seen in Table 1, the taxonomy of his answers contains a large variation of the first person “I”, “I’m”, and “my”. This suggests that Mueller felt a strong affinity to the document. He never used the form “we”, “our”, or “the team” which would have been more appropriate considering his repeated assertions that the report was a large team effort and that no single individual has mastered its content.
Response to Question
I stick with the language that is in front of you
I will leave the answer to our report
I’m not going to discuss other matters
I’ll refer to the report
I can’t say I understand the statistics
I direct you to the report for how its characterized
I rely on the language in the report
This is one of those area which I decline to discuss and will direct you to the report
Again, I send you to the report
I have to pass on that
I rely on the report
This is outside my purview
That is outside my purview
Outside my purview
I refer you to the report
This is still outside my purview
I will refer you to the report on that episode
I’m going to ask you rely on what we wrote about that incident
I’m again would refer you to the report and the way its characterized in the report
I’m not going to get into that
I can’t get into that. That’s internal deliberation of the justice department
I direct you again to the report
Whatever was said will be in the report
I can’t answer that questions
That’s not in my purview
I can’t get into that
I can’t get into that
I am not going to get into it
I would refer you to the coverage of this in the report
I would refer you to the report
I send you back to the report
I refer you to the write-up of this in the report
I can’t beyond what’s in the report
I can’t get into internal deliberations
I can’t get into the evidentiary findings
Can’t get into that
I will leave it as it appears in the report
I’m just going to have to refer you to the report if I could
I don’t want to speculate
I rely on the wording of the report
With regards to Steele, that beyond my purview
It’s not within my purview
As I said before and said again, it’s not within my purview
I refer you to the report on that
That’s an area in which I cannot get into
I’m not going to get into what we may or may not have included in our investigation
I’m not going to get into subsidiary details. I refer you again to the page 91-92
I can’t speak to that
I am under orders that don’t allow me to give you an answer to that particular question
I can’t get into the discussion on that
I’m not going to be involved in the discussion on that…
I’m not going to go further in terms of discussion…
I can’t get into our investigative moves
I’m not going to get into that any further than I already have
I can’t speak to that
I would say I rely on what’s in the report
That letter speaks for itself
I’m not going to go beyond that
I refer you to the court proceedings on that issue
I’m not going to get into that
I can’t speak to that
I’m not going to talk to that
I’m not going to speak that
I’m not going to get into what was in Mr. Comey’s mind
I’m not going to delve more into the details of what happened
I’ll leave that to the attorney General
I’m not going to get into ta discussion on that
Again, I refer you to the report
I refer you to the lengthy dissertation on exactly whose issues that appears in the report
I can’t speak to that
That was outside out purview
I’m not going to speak to that
And I am not going to answer that question, sir
I’m not going to speak anymore to that
I’m not going to answer that
I have nothing to add
I’m not going to add to what I have stated before
I feel uncomfortable discussing anything to do with the Stone indictment
I’m not going to speculate
I’m not going top discuss that
Not going to talk about that
I’m not going to answer that
I’m not going to talk about that issue
I’m not going to get into that. It’s a little of track
I have to say the letter itself speaks for itself
I go back to the latter. The letter speaks for itself
I can’t answer that question in a vacuum
We have not specified the persons mentioned
I’m not going to speculate
I’m going to pass on that
I’m not going to comment
I’m not going to go into details of the report
Those areas, I’m going to stay away from
I’m not going to get into those matters to which you refer
I’m not going to speak to the series of happenings as you explained them
I’d have to refer you to the reports on that one
I’m not going to speculate
I can speak to the half of the half of your question that’s on the screen being accurate
I’m not going to speak to that
Again, I’m not going to discuss the issues related to Mr. Steele
Again, I pass on answering that question
That’s about all I’ll say on this aspect of it
I’m going to pass on that
I take your question
I’m not going to speculate along those lines
I’m not going to opine on that. I don’t have the expertise in that arena to opine
I cannot agree with that. Not that it’s not true, but hat I can’t agree with it…
That portion or that matter does not fall within our jurisdiction
I direct you to the report for how its characterized
I’m not going to discuss any other alternatives
I can’t speak to that. That would be in levels of classification
I’m going to stay away from one particular or two particular situations
I’m not going to talk about specifics
I’m not going to speak to that
I’m not going to get into that. It goes into internal deliberations
Again, I’m going to pass on that
As I said before, this is an area that I cannot speak to
Again, I’m not going to speak to that issue
Questions such as that should go to the FBI
And I’m not going to discuss that
I’m not going to get into that
And again, I’m not going to respond to that
Again, I can’t respond
Again, I can’t speak to it
Again, I can’t answer that
Again, I’m not going to go there
I think you understand I cannot get into either classified or law enforcement information
I can’t respond to that question, it’s outside my jurisdiction
Again, I can’t speak to that
I can’t go into it
I’m no longer in the Federal government, so I’ll pass
I don’t want to wade into those waters
I defer to the report on that
I can’t get into a discussion on it
I can’t answer that
I can’t get into that
Again, it’s the same territory that I’m loath to get into
I’m not going to talk to that
I’m not going to talk to that
That I can’t get into
And I can’t get into that area
I can’t answer that question
I’m not going to get into that
I cannot get into that
I will not get into that
I leave that to you
Table 1: Sampling of reasons from about 200 instances for Mueller’s refusal to answer questions
The Evaluation Process Mueller’s testimony consisted of over 750,000 video frames. Evan a trained interrogator could only process a small percentage of this data. Add to this the observer’s distraction, blinking, and fatigue and it becomes virtually impossible for a human to be able to accurately capture the fine nuances of all of these these frames or sequences for content. At best, a person would be able to provide a summarized ‘gut feeling’ about the overall session and reference some vague (and often inaccurate) actions such as ‘he touched his nose’ which could suggest that he was lying.
AI based video analytics on the other hand, can easily process each video frame in a consistent, repeatable manner, and with no observer bios. The objective of my evaluation of Mueller’s testimony was not to determine if he was lying with certainty, but rather to identify recurring patterns of stress that are associated with deception and correlate them to the topics of conversation.
Mueller did a great job obfuscating the report details but the large high quality volume of video and audio in his testimony made it possible to analyze the session and find anomalies and various patterns that could provide insight into his mindset.
In this project just as in several of my previous posts (1, 2,3), I used AI based video analytics, text, and speech analysis platforms. These included:
For the text/speech, I used a hybrid approach to word and phrase speech pattern analysis. The textual analysis evaluated these types of speech categories:
I-words (I, Me, My, I’m)
For the video analytics, I established Mueller’s facial and other video objects baseline using several on-line sources and the main testimony video. The baseline cataloging included his unique facial expressions such as Microexpressions and other visually detectable actions like use of hand gesture, hand related activities, head motion, mouth movement, gaze, etc.
Image 2: Sampling of Mueller’s Microexpressions such as (L-R): loathing/anger, surprise, fear, happiness
Image 3: Sampling of Mueller’s’ body dynamics as related to left hand usage
Following the creation of a facial baseline catalog, I proceeded with the ML training using his unique data sets for non-facial activity such as paging through the report folder, eye blink rate, gaze, etc.
Image 4: Sampling of image set used to train the machine learning (ML) to identify Mueller flipping pages through his report folders
After the training was completed, I ran the first 15 minutes of Mueller’s testimony through the engine and performed a search for known classified objects such as him ‘reading the report’.
Imager 5: Sample search results of instances of Mueller looking at the report
I noted the detections and examined several thousand video frames prior, during, and after the detections to capture the actual ground truth. The visual search results of the 15 minute video segment correlated to within a 83% match rate against the baseline catalog created with the ML training set. I then used the missed detections to re-train the ML again and repeated this cycle several times on random video segments of his testimony until the match rate stabilized at about 94%.
In addition to creating a catalog of Mueller’s microexpressions I also created a library of sequences of his composite facial expressions. These sequences were close consecutively spaced combinations of microexpressions and other body activity that were 0.5-3.5 seconds long. One example for these types of composite expressions was eye flutter combined with ‘lip twitching’ or some other mouth movement.
In this sequence, Mueller typically stared at the speaker while his bottom lip would involuntary twitch or quiver several times or his lips would tighten; he would then break eye contact with the speaker and rotate his head downwards, recompose, then bring his head upwards and re-establish eye contact with the speaker.
Image 6: Sample of a typical Mueller sequence showing mouth activity and breaking eye contact with the speaker. The context here is Rep Jim Jordan’s asking Mueller to confirm if Joseph Mifsud was interviewed, did he lie, and is he Russian or Western Intelligence
Once I completed calibrating Mueller’s video object catalog and the library of sequential expressions, I conducted searches for facial anomalies. Anomalies are defined as any variations from his standard single image or sequence patterns such as unusual cycle of head, eye, or mouth movements.
For example, based on his standard detection for “blinking”, Mueller’s blinking interval baseline was established to be 3–7 seconds with a blinking duration of approximately 1/10th-1/3rd of a second (see Image 7-8).
Image 7: Sample of one baseline feature in Mueller’s visual object catalog showing his normal blink pattern.
Image 8: Sample detections of Mueller normal blinking pattern throughout his testimony. Mueller blinking follows a pattern of a full single closure of the eyelid at a 3-7 second interval
Any blinking variation form this base line generated an anomaly that was then evaluated manually before becoming certified as a new pattern of interest. This exception was then further evaluated in the context of the topic of conversation and the microexpressions involved.
One such anomaly was associated with Mueller’s unusual blinking pattern. On closer examination, it turned out that what on the surface appeared to be unusual blinking was in fact a reoccurring cycle of rapid flutter of the eyelids. This unusual sequence was also at times accompanied by certain head, tongue, and lip movements.
After mapping this ‘Flutter Cycle” to the topic that was being discussed at the time of the event, it became clear that this was some sort of an involuntary display of distress and/or fear. It was so prevalent that it could even be used to predict what questions were being discussed.
Some of the subjects that triggered this ‘Flutter Cycle’ were:
DOJ and FBI media leaks
Christopher Steele, the dossier and its funding sources
Fusion GPS and its work with the DNC, HRC, and foreign governments
Glen Simpson and Natalia Veselnitskaya
The meeting at the Trump Tower
Informants and surveillance (i.e. Mifsud, Downer, Halper, etc.)
The FISA warrants
DOJ and FBI leaks
Image 9: An illustration of Mueller’s typical Flutter Cycle.
The Flutter Cycle sequence was characterized by 2-5 rapid flutters of the eyelids and an upward eye roll, head, mouth, and accompanying tongue movements. This Flutter Cycle sequence seen in the left side of the collection in Image 10 (also, see 1:26:00 in the video) corresponds to questions by Rep Steve Chabot of Mueller’s investigation of the relationship between Glen Simpson, Natalia Veselnitskaya, and the latter’s visit to Trump Tower.
The same type of events were observed during other pointed inquires such as Rep Louie Gohmert’s challenging Mueller’s credibility due to his refusal to answer basic questions (see 1:33:30 in the video).
Image 10: (L-R) A sampling of three anomalies a complex facial flutter, lip twitching, and simple eye flutter sequences
Several other interesting anomalies that turned out to be repeating patterns in Mueller’s facial expression and composite sequences were:
Lip Twitching – Associated with microexpressions such as fear and surprise
Downward Head Nodding – Associated with other defensive posture the was triggered by breaking eye contact with the speaker
Flattened Mouth or Lips – Associated with signs of frustration as in ‘Iwant to answer this question, but I really shouldn’t’
Prolonged Blinkless Stare – Associated with angry and combative response to some question
Imager 11: Samples of Mueller’s dozens of “flutter cycle” episodes during the Q&A
The Jolly Affable Mueller Not all of Mueller’s testimony was marked by doom and gloom. On a number of occasions (mostly when talking to Democratic representatives), he showed himself to be charming, in high spirits, engaged, and animated. Mueller had no inhibitions about making remarks regarding the report’s failure to exonerate Trump and the possibility of persecuting Trump after he left office. He freely cited legal sources and DOJ procedures and protocols and provided detailed rationale for his team’s action and conclusions.
Image 12: The suave, charming, engaged, and animated Mueller in action
Mueller’s predictable patterns of distress were almost always associated with ‘difficult’ questions on topics such as the role of Fusion GPS, spying on Trump, and Christopher Steele. Images 13 and 14 show a typical triggering events of a Flutter Cycles.
Image 13: Samples of Mueller’s Flutter Cycle episodes during Q&A session dealing with him leaking report details to the media Image 14: Sample of Mueller’s Flutter Cycle episodes during Q&A session dealing with separating the grand jury materials from the report
Analysis Results Mueller’s body language and facial sentiment analysis shows high levels of discomfort and tension when discussing certain parts of the report. He exhibited many facial signs of distress that included:
Multiple Flutter cycles
Mouth quivering cycles
Self shooting and fidgeting behavior
Sudden breaking of eye contact
Rapid downward head movement
Tightening of the mouth and lips
I didn’t have a baseline for incidents where Mueller was being untruthful so I can’t explicitly call out potential incidents of lying during his testimony. However, the baseline of his normal conversational dynamics vs. the ones he exhibited show signs of clear distress which strongly suggest that at least from Mueller’s perspective, not all questions were equal and not all of his answers were factual.
Mueller distress patterns consistently overlapped with certain trigger topics and his verbal response to almost all of these interactions was a variation on the “I’m not going to…”. He deviated from this pattern only a handful of times and actively engaged the questioner. One of these back alley knife fight sessions involved Rep Ben Cline’s stating that Andrew Weissmann was running a rogue investigation that was based on flawed legal theory that was overturned unanimously by the Supreme Court.
As the question was being asked, Mueller became defensive; he shifted uncomfortably in his chair, exhibited his Flutter Cycle, and replaced his poker face and laconic I-word response pattern with a passionate and verbose defense of Weissmann (see 3:19:40 in the video or sound file below).
Image 15: Sample of one of Mueller’s distress patterns that includes his Flutter Cycle and uneasy shifting in his seat
Recording 1: Exchange between Rep Cline and Mueller about Weismann’s legal foundation of his obstruction of justice investigation
During this segment which lasted about two minutes, Mueller argued, spoke over Cline, and attempted several times to repeat his assertions about Weismann. This continued even after the subject of the questions changed to Obama’s culpability in Obstruction of Justice when he announced publicly that the HRC private email server did not pose any threat to national security. Mueller, without much difficulty, exhibited a decent mastery of the report’s content, cited specific areas in it that included the “lengthy discussion” and “lengthy dissertation”, and in general tried to rehabilitate himself and his team.
Conclusion The overwhelming majority of Mueller’s testimony failed to illuminate any of the big questions about the DNC email hack, the genesis of the Steele Dossier, the DNC/Fusion GPS relationship with Russian state actors, and the 2016 surveillance on the Trump campaign. In fact, his answers raised even more questions about the real power behind the throne and R&R within the special counsel team.
If it is indeed the case, as Mueller confirmed in multiple answers, that no single individual on his vast team had intimate familiarly with the whole report, then who compiled the final version of the document? Was this just a collation of multiple taskforce reports that were later combined into a single master? And if that is the case, who was the person that harmonized all the individual versions in order to make sure that the index, format, dates, people, places, reductions, and events were in sync?
Image 16: The Special Counsel Team and testimony attendees
It is noteworthy that Mueller continued to play the I-phrase card and refused to address any of the procedural questions about the compilation of the report. Even though, this information had little bearing on the report’s content and that there is nothing classified or proprietary about the way the DOJ writes and edits their documents.
Image 17: Mueller’s Trump Card
Even though Mueller attempted to obfuscate the report’s composition methods and authors, the writing style, document layout, context, and several other administrative clues strongly suggest that Andrew Weismann was the architect and Aaron Zebley was chief editor of the document. This is also likely the reason why Mueller insisted that Zebley be present by his side and be sworn in.
The evidence from the video analytics, speech dynamics, and the decision tree Mueller used to answer the questions (i.e. question objective vs. answer strategy) shows a decent mental agility and the ability to alternate between complete ‘radio silence’ and ‘singing like a canary’ on demand.
To those who still believe that Mueller was just a senile old man with little familiarity with the content of the report, consider the fact that his verbose answers show that he had a pretty good grasp of the document. He also artfully navigated the many minefields in the report without blowing up a leg in the process. Some experts in the MSM have been suggesting that Mueller’s poor verbal performance and optics can be attributed to some form of cognitive impairment but this argument is inconsistent with his ability to effectively deliver the following:
Selectively discuss specific topics, most of which were prejudicial towards Trump
Answer questions that almost exclusively supported the impeachment narrative with certainty and conviction
Justify and emphasize specific areas in the report that exonerated his team from claims of bias towards Trump and instances of hostile conduct by FBI senior management and its agents (i.e. Comey, Strzok, Page, agent 2, and others)
Utilize the “I’m not going to…” strategy to answer any questions about the “insurance policy”
Refuse to address the media leaks that either came from him personally, his direct reports, or his team
Exhibited great mental agility and dexterity during the May 29th, 2019 Mueller news conference
Come up with over 198 different ways of not answering a direct question
The patterns identified by the analytics strongly suggest that all of Mueller’s behavioral stress patterns matched the typical anxiety profiles and signs of internal struggle that are exhibited by a deceptive suspect during an integration. For the first time in his long bureaucratic career, he found himself at the wrong side of the table with the bright lights in his face and a real possibility of being charged with perjury. For several hours, the fearless hunter became the pray and he clearly didn’t like the experience.
Contextually, the majority of his testimony turned out to be an underhanded attempt to use the Q&A session to justify, promote, and surreptitiously inject political narrative into the public hearing. None of this should come as a surprise as it is the same circular “impeach Trump” agenda that launched this investigation in the first place. At the end of the day, despite Mueller’s big title and god-like pedigree, he turned out to be just another DC power broker who apparently placed his bets on the losing presidential candidate.
Image 18: Two pages (a total of 856 words) form the Mueller report dealing with George Papadopoulos being told by Joseph Mifsud about the Russian having “Dirt” on HRC.
Mueller’s elaborate 448 page report that took close to two years to complete, cost over 25 million dollars (that’s about $51K per page), involved 19 lawyers, 23 legal researchers, 40 FBI agents, 10 intelligence analysts, 7 forensic accountants, 25 other professional staff, and the unlimited resources of the DOJ, the State department, NSA, and the intelligence community, delivered an indefensible dud.
Reading the reports, you can’t but stop and appreciate the authors’ Kafkaesque sense of humor. In the example pages shown in Image 18, the report discusses the chain of transmission of the Russian “Dirt” from Joseph Mifsud, to Papadopoulos, to a mysteries western diplomat (Alexander Downer) who then informed the FBI, who naturally became alarmed and started this massive investigation. On the face of it, the document looks solid. It has all of the right trimmings, detailed claims, massive amount of footnotes, intelligence lingo, hush hush sources, and strategic reductions with alarming labels like “Harm to ongoing matter”. It is as convincing as a quality levitation magic act.
But, magic acts of levitating people are always predicated on the audience viewing the scene from a distance and through a carefully controlled field of view–which is exactly what the Mueller report and testimony turned out to be. It doesn’t work if you get a glimpse of the crane and the wires supporting the magician. Once you understand the mechanics of the magic, the awe gives way to a letdown.
You can test this premise by substituting any good magic act with the report and Mueller with any successful magician. Any question you ask the magician about the inner workings of his trick would be deflected using the exact same techniques Mueller used during his testimony. The most important rule in magic is NEVER tell the secret of the trick, just let the magic speak for itself.
Image 19: The incredible levitating magic act
What is ostensibly missing from these two magical pages in Image 18 is that the source of the “Dirt” was none other than Stefan Halper, a paid FBI informant who billed (using DUNS # 078459148) the Federal Government about $656,535 for his services. By the time you factor Halper and his harem of young female assistants, Mifsud and his life of luxury at his safe house, Downer’s expenses, and at least 11 other IC, CI, and State Department assets that supported Halper in fattening Papadopoulos before he was shish-ka-bobbed by bob, the cost of these two pages to the US taxpayer was probably upwards of a million dollars. So, to those of you who still think that majoring in contemporary English fiction won’t pay the bills, it clearly can! After all, what other line of work pays $1168 per word?
Image 20: Stefan Halper’s government payment record for service provided to the DoD and DOJ from 2016-2018
Summum bonum I have difficulty finding solace in Mueller’s bragging about the higher good from his recovery of about $40 million from the Paul Manafort persecution. I’m also not sure if we should laugh or cry about the concept of the DOJ becoming a profit center. The problem with the DOJ acting as a collection agency that recovers the cost of prosecution from its targets is the political nature of selecting their next victim. Each one of us including the Honorable Mr. Mueller has something in his past, present, or future that warrants jail time and property seizure. With over 3000 federal and thousands more state laws on the books, we are all guilty of some misdemeanor or a felony. Who in the DOJ then, gets to make the decisions about who/why to persecute and the ultimate greater good? Is it going to be one of the dozens of high power attorneys that regularly walk through the DOJ revolving doors to personally enrich themselves by constantly hopping between government gigs and private practice?
The problem with the whole Manafort affair is that if he was so thoroughly corrupt in 2007, then why didn’t Mueller investigate him earlier during his 11 year tenure as the director of the FBI. Why did he wait until 2018 to bring these charges?:
“…crimes arising out of payments he received from the Ukrainian government before and during the tenure of President Viktor Yanukovych.”
After all, the DOJ, FBI, and the IC had a supersized file on Manafort going back to 2007, so why wait for all these years?
Image 21: The Triumvirate or Threesome (depending on your view)
Mark Twain once wrote that:
“Anybody can tell lies: there is no merit in a mere lie, [for a good deceit] it must possess art, it must exhibit a splendid & plausible & convincing probability; that is to say, it must be powerfully calculated to deceive.”
Mueller’s report doesn’t come close to Twain’s definition of deceptive genius, but it does have a certain kitschy synthetic Disneyland feel to it. In many ways its similar to another secretive report, the Protocols of the Elders of Zion. Both, share the same conspiratorial elements, treachery, mysterious meetings, made-up events and agendas, secret societies, informants, and intrigue.
All of this hush-hush secret agent man stuff in the report seems very mysterious, but at its core, it’s really a simple criminal matter. If you’ve ever been a juror on a criminal trial, you should be familiar with the routine. If you haven’t, it goes a s follows:
The prosecution and the defense present their case with an opening statement
Both show evidence and present witnesses
Both cross-examine witnesses
Each side delivers their closing arguments
The jury goes into deliberation and comes up with a verdict
In any normal criminal trial in the US, they typically follow the Federal Rules of Evidence, there is no such thing as secret testimony that can’t be verified or evidence that can’t be shown to the jury. If the DA doesn’t want to expose his sources/methods then they get excluded from trial. If witnesses can’t be cross-examined, their testimony is inadmissible. It’s as simple as that. And all of this procedural stuff doesn’t even address the issues with Mueller whitewashing the existence of several rogue and biased agents/attorneys on his own team.
Image 22: Rep Gowdy and DOJ IG Horowitz Q&A session regarding Peter Strzok’s and Lisa Page’s involvement in the Mueller and HRC Email investigations
So, no, I wouldn’t classify Mueller’s report as a deceptive masterpiece, I would rather categorize it as more of a ‘true story’ type of a tale, blunderingly delivered by a DC swamp-raised shrimp.
The True Story It’s true… It’s true and the other thing is my sister had a baby and I took it over because she passed away.
and then the baby lost its legs and its arms and now it’s nothing but a stump but I still take care of it with my wife and it’s growing and it’s fairly happy.
But it’s difficult ’cause I’ve been working a second shift at the factory to put food on the table, but all the love I see in that little guy’s face makes it worth it in the end.
“movies take a long time to make, but they’re high impact. Twitter takes a second to do, but there’s not a lot of info there. One on one coaching is high enough bandwidth that it can change your life and make you cry, in real time, and the Mona Lisa, while less bits per second than a TV show, has enough emotional bandwidth to matter, even if it’s 400 years old.”
This is a great observation. Clearly, bandwidth and synchronicity matter. But what about the relationship between messaging media cost, the time needed to develop it, and its ability to effectively saturate a large population? Is it possible to create effective memes and circulate them widely through 140 character messages? And what about the impact of all of these factors on future social media functionality? Is social media headed towards platform convergence or towards divergence?
Messaging Cost, Development Time, and Outreach Potential Consider a typical Hollywood mega blockbuster like the Pirates of the Caribbean: At World’s End. This 169-minute long meme bomb carries an enormous amount of content. Every scene was carefully designed to keep audiences glued to their seats. Its production took two years, it required the creative talents of hundreds of individuals, and it cost about $300 million dollars to make (roughly $30K per second of movie). It grossed over $963 million in revenue, which amounts to 320% return rate—not pocket change by any means. The movie messaging medium is so well optimized that it is capable of generating a significant amount of secondary revenue and reach worldwide audiences by sheer inertia. Just think about all the spinoff industries that spring into life when a major movie like this comes out: toys, music, party accessories, food, costumes, books, etc.
If you follow a reasonably well proven formula, (standard plot, big name actors, expensive sets, and lots of special effects), you are almost guaranteed at least a 2:1 ROI ratio and a good chance of producing a number of sequels to your masterpiece.
So it does seem that there is a correlation between how much content is packed into the message and its memetic effectiveness. The movie messaging model (see quadrant 1 in the graph below), is based on a tremendous investment of time and resources. The strategy is to build the best product possible, market it well, and then fire-and-forget it. The forget part has to do with the passive nature of this medium; the audience has no interaction with the content and consumes the messaging passively.
On the other hand, social media messaging like blogs, e-mails, Twitter, etc., (see quadrant 3 in the graph above), can be produced instantly and with little investment or skill. But it’s also difficult to get a quantifiable ROI from it, (ergo, the raise of snake oil salesmen social media marketing gurus). Another observation is that synchronous social media messaging depends on an interactive and free feedback loop. Users demand the ability to interact in near-real time with their network, but they refuse to pay for the privilege.
From an evolutionary point of view, it seems that the quadrant 1 and 3 messaging mediums represent a form of r/K selection classification. Each trades between quantity and quality. The focus in quadrant 1 is to increase the quality of the content with higher expense per message. The focus in quadrant 3 is to increase the quantity of the content with a corresponding reduction in quality and lower expense per message.
Content Developers and Messaging Strategy The chart data suggests that there is an inverse relationship between the effectiveness of content distribution and the viewer’s exposure time to the message. It seems that quadrant 1 messaging mediums are the most effective in terms of meme creation, distribution speed, and outreach.
Content developers are aware of the limitations of each medium and have developed interesting coping strategies (sometimes reversing r/K selection) in order to leverage various messaging toolsets to promote their content.
For example, the K-selected movie, television, theater production, and book publishing industries leverage social media primarily to get a short term engagement with potential audiences. The ultimate objective is not to create a long term social community, but instead to lure users in to consume their product. Once the product is out on the market, most of the related social media interaction around it stops.
A good illustration of this was evident for the movie Coroline. Eight weeks before the movie release date, one of the characters, the Great Bobinsky, created a blog and started posting on a weekly basis, clearly so as to create a groundswell and buzz. Three weeks after the movie went live, official posting to the blog stopped—to the great dismay of its myriad followers—and Mr. Bobinsky announced:
Thank you for being friends of Bobinsky…Have beet and think of me. Until we meet again. Mr B. signing off.
In complete opposition to movie producers, the r-selected social media authors use content and platforms like Picasa and YouTube to build long term relationships. Their strategy is to create an intimate family environment that will foster a long term engagement with the target audience.
Social Media Box of the Future In terms of future direction, there is strong indication that just like Apple successfully consolidated GPS, MP3, gaming console, and phone into a single device, so too the social network platforms of the near future will converge on traditional services.
The future social network platforms will yield a service that offers a suite of products like IM, voice and video chat, conferencing, on-line collaboration, e-commerce, content subscription, and personal reputation management, sort of a LinkedIn/Facebook/Pandora service with hybrid Skype and PayPal/Google wallet-like capability. And all of this optimized and available on a mobile device.
The only question that remains is will this future platform be built on top of one of the current products or will it be mashed and/or assembled from existing services.
It may not be obvious, but social network (SN) data has numerous applications that go beyond simple socialization. Beside the voyeuristic and self-promoting aspects, SN data is brimming with fresh, cheap, and accurate target information. This includes age, demographics, purchasing habits, buying power, education, brand loyalty, influence, and income, just to name a few.
This is pretty powerful stuff, as the insight that can be gleaned from millions of users posting near real-time could revolutionize the way products are launched and marketing decisions are made. It’s no longer necessary to guess what buzzwords will resonate with users for your next campaign – users are already using those words in their public conversations. There’s no longer a reason to take spraying and praying advertising approach in the hopes that an add will be seen by a fraction of the right buyers. Now, you can easily determine where your target population hangs out and pursue them directly.
So with such promise to disrupt the market, why hasn’t big soft moved into this address space yet? Where are products like the Microsoft Social Media Analytics Server or the IBM Social Network BI Aggregator? After all, large volume data analytics have been around for quite some time. Over the past 20 years, giants the likes of Microsoft, IBM, and Oracle have invested hundreds of millions of dollars in developing enterprise analytics and decision support solutions. Why not adapt their existing platforms to harvest the SN analytics as a cloud solution?
The answer to these questions has a lot to do with the problem of low data quality and inconsistency. A close examination of blog, forum, Twitter, or Facebook data reveals that they are all a hodgepodge of tidbits of personal information, non-threaded conversations, and poorly typed, spelled, and formatted communications, which renders them virtually useless for structured or unstructured analytics engines.
You may argue that at least some of the SN analytics companies must be doing something right. That may bee so, but there is no quantifiable way to gauge how much of their analytics are based on real math and how much is snake oil salesmanship and slight of hand. Many of the SN analytics providers claim that they developed patented technology to sort through volume, noise, and poor data quality. Others insist that their “secret sauce” algorithms allow them to calculate engagement, find patterns, and even accurately track memetic propagation. Most of these claims are dubious at best and can’t be verified because we don’t have a ground truth data. But even if you could verity the accuracy to the results, there are also these major factors:
Most SN analytics providers don’t harvest their own SN data and those that do certainly don’t do so in real-time. Rather, they subscribe to data scraping services like Compete, comScore, Hitwise, Nielsen, Quantcast, etc. The data harvesters only collect data from a small fraction of the relevant websites, blogs, or forums. They do so on a schedule that could be as long as 2 weeks. Obviously, password protected and membership-only sites are off limits. What you get then is a tiny sliver of a weighted sample population that could be weeks old.
Companies that scrap platforms like Facebook or Twitter do it via the native platform API. Due to system performance concerns, the SN vendors throttle the amount of data they expose via this API. If you are looking at a real-time monitoring solution of any of the social networks, be prepared to have very large data gaps and timeouts in your dashboard.
Algorithms for determining text sentiment, theme, writer’s gender, age, and education are only effective on large and well-formatted compositions. They were designed to work on structured essays that are around 1000 words long. The likelihood of accurately determining any of this characteristic from a 140 char tweet or a blog posting that is riddled with expressions like LOL or OMG is as good as a coin toss.
Even the largest data providers only scrape less than 1 percent of relevant Internet data. The analytics you are viewing probably represent information found across no more than a handful of sites, blogs, or forums. Making multi-million dollar advertising decisions based on such low quality and small data sets could be risky.
Due to the growing availability of automated tools for the creation of blogs, websites, and posts, we are starting to see a significant amount of machine-generated content that is designed to pump-up SEO visibility for adware sites. Data scrapers are unable to distinguish between machine generated and human typed content, which can result in skewed analytics.
Data feeds frequently go through secondary processing before they are presented to users. This additional refinement may include the removal of partial records (i.e., missing dates, user names, etc.) or offensive message content like cursing, pornography or spam. All this data ‘massaging’ further reduces the population size and the accuracy of the results.
So what is the moral of the story? If you are on a quest for the SN analytics holy grail, you won’t find it, because it all depends on how much YOU are willing to compromise in terms of data sample size, quality, and accuracy.
If you are in the market for an SN analytics tool, don’t take any chances by committing to one solution before doing your homework. Ask the vendor to explain to you in 8th-grade level English how they address the six items mentioned above. Arrange for a trial period with at least three vendors and then compare their analytics to each other using a benchmark and a ground truth known to you. This should give you a sense of how accurate the tool is and its margin of error.