Prince Andrew Newsnight Interview: Deception Analysis

Prince Andrew + Emily MThe Duke of York interviewed by Emily Maitlis on Newsnight (©BBC 2019)

HRH Prince Andrew, the Duke of York, was interviewed by Emily Maitlis in a BBC Newsnight special broadcast on 16 November. The topic of the interview was Prince Andrew’s relationship with Jeffrey Epstein, a billionaire and convicted pedophile who died in prison whilst being investigated for multiple sex trafficking charges. Prince Andrew was also asked about allegations made by Virginia Giuffre (neé Roberts), one of the women who claimed to have been trafficked and abused by Epstein and his partner Ghislaine Maxwell. Giuffre testified under oath in a 2015 court deposition that she had been trafficked to Prince Andrew by Epstein and his partner Ghisliane Maxwell, and that she had “engaged in sexual activities” with Prince Andrew on three occasions.

Andrew Virginia photo depositionandrew epstein
Top: Extract from Virginia Roberts Giuffre 2015 court deposition. Below: Prince Andrew and Jeffrey Epstein in Central Park in 2010.

Newsnight negotiated the interview with Buckingham Palace over a six-month period. After initially refusing an interview with Newsnight in May due to a reluctance to talk about Epstein, Prince Andrew and Buckingham Palace agreed to the interview after Epstein’s death in August (see GQ interview with Newsnight producer Sam McAlister ).

Prince Andrew and the palace agreed that no questions regarding the Epstein-related allegations would be off limits; neither were questions agreed in advance. Considering these circumstances of the interview, it seems possible that Prince Andrew was motivated by a desire to clear his name. It is also likely that Buckingham Palace were convinced that the allegations made against Prince Andrew were false. These circumstances point to a strong desire to appear credible and justify a presumption of truth.

Outliar™ ‘Linguistic Polygraph’ Methodology

OutliarLinguistic Polygraph is based on principles of deceptive communication drawn from Information Manipulation Theory (McCornack et al. 2014): that lies are built on truth and therefore deception most often produces texts that are a strategic mixture of truth and lies. Using this insight, the Outliar methodology utilizes the most sensitive linguistic deception cues (LDCs) drawn from the academic literature (see Hauch et al. 2015 for a good overview), as well as LDCs used on investigator training programmes, in order to identify and separate credible and suspicious content (see Popoola (2017) for a case study). Disclaimer: Outliar is not a lie detector. It is an investigative linguistic tool that highlights credible text segments and identifies suspicious text segments as ‘points of interest’ deserving further investigation i.e. loci of potential deception.

Prince Andrew’s Interview

princeandrewoutliaranalysis

Credible interview segments

Segment 3: This is Prince Andrew at his most reflective. He admits that he thought visiting Epstein after his conviction was honourable rather than inappropriate (“I felt that doing it over the telephone was the chicken’s way of doing it” lines 167-168); he notes that he took the decision to visit Epstein himself and against the advice of at least some of his team (“I had a number of people counsel me in both directions…“). Reflective engagement with different perspectives and self-questioning is a cognitively complex stance that is difficult to maintain during deception.

interview segment 3

Segments 6 and 7: These contain a host of ostensibly verifiable details –  information about a Pizza Express visit (lines 371-375); details of a medical condition that prevents sweating (lines 387-393), the kinds of clothes he usually wears when traveling (lines 470-472). In the age of ‘deep fakes’, even his skepticism as to the provenance of the photograph with a 17-year old Virgina Roberts (see above) comes across as reasonable (lines 476-477). As well as verifiable facts, Prince Andrew provides reasons and explanations; all this adds to the credibility

interview segment 6

interview segment 7

Suspicious interview segments

Prince Andrew’s language register shifts significantly in Segment 8; his coherence disappears and he becomes increasingly vague. Up until this point, he has been able to somewhat plausibly deny specific occasions of meeting Virginia Roberts; however, he is unable to convincingly deny knowing Virginia Roberts at all. Prince Andrew doesn’t offer any reasons for not knowing her or concessions towards the fact that people might think he has met her. This runs counter to the reflective and conciliatory register of the remainder of the interview.

interview segment 8

In general, liars don’t like to directly speak lying words. Here, each of Prince Andrew’s propositions in lines 577-580 – ‘I don’t remember meeting her, ‘I don’t remember a photograph being taken’, ‘I’ve said [many times] that we never had…sexual contact’ – can be taken literally as true (i.e. not remembering, saying something frequently). However, this is a key difference between lying – speaking falsehoods – and deception i.e. creating false belief; deception is often executed through exploiting presupposition and perceived credibility cues – without explicitly stating false facts. A more credible answer would include a concession (e.g. “I wish I could remember”) or explanation (e.g. “I rarely, if ever, have met young girls in a casual setting so it’s extremely unlikely…”).

This segment of the interview is particularly awkward for two further reasons. Firstly, Prince Andrew pattern of register change indicates a strong distancing strategy; Andrew literally puts a barrier between himself and Virginia Roberts (“I don’t have a message for her because I have to have a thick skin”, line 589), and quite disparagingly refers to Roberts as just “somebody making allegations”(lines 589-90). This negativity is in stark contrast to the tone of the interview up until now. Liars are more likely to express unmoderated negativity when omitting pertinent information (whereas they become more verbose and personal when exaggerating or falsifying). Secondly, Prince Andrew’s suggestion that a man always remembers having sex because it is a “positive act” is vague and unconvincing; Andrew is trying to emphasize the extent to which he doesn’t remember; it is difficult to prove a negative i.e. that you don’t remember something (just as it is difficult to disprove a negative).

It is most suspicious that Prince Andrew does not address the second half of Maitlis’ double-question: “Is there any way you could have had sex with that young woman or any young woman trafficked by Jeffrey Epstein in any of his residences?” (line 594) . In answer to this question, Prince Andrew’s persistent use of the ‘it’ pronoun to refer to the Virginia Roberts alleged incident than more general allegations is clear avoidance of the second part of Maitlis’ question.

Summary and Postscript

Andrew’s denial of any knowledge of the existence of Virginia Roberts is unconvincing. Questions about her motivation are  denied vociferously but incoherently and with negativity and lack of engagement. This is out of step with the general register of the interview which has a reflective and considered (prepared?) tone. Although Prince Andrew may not have had sex with Virginia Roberts, he is likely to have more information on why she might be making these allegeations.

The photograph below, of Prince Andrew and Jeffrey Epstein on a yacht with a number of scantily-clad young females, indicates that the aforementioned unanswered question may be a key to the Prince Andrew – Epstein mystery.

andrew epstein yacht

Prince Andrew with Jerry Epstein. Phuket, 2001. Credit: Jason Fraser

Prince Andrew Newsnight Interview with Emily Maitlis – Transcript

 

 

 

 

 

 

What do fake reviews and fake news have in common? Textual cohesion strategies.

 

fake everything

Automated fake news detection is something of a holy grail at the moment in deception research, machine learning and AI in general. Yet, despite all the high profile committees and investigations dedicated to it, fake news is not an isolated problem; it is part of the general epistemic malaise that has caused us to refer to our current era as ‘post-truth’. With this in mind, I have approached the problem of fake news detection building on my work on fake review detection. This is not to trivialise the problem; despite its greater social and political impact, the production of fake news is an equally commercial operation (complete with its own writing factories eg. Macedonia, Kosovo and Maine).

In 2018 I presented a paper on fake book review detection at Stanford University’s Misinformation and Misbehaviour Mining on the Web workshop. One of my key findings was that authentic reviews were significantly more likely to contrast positive and negative aspects of a book, even in 5-star reviews; positive reviewers often hedge their praise and include caveats (see examples 1 and 2 below). Fake reviews were significantly less likely to display such a balance – basically, deceivers were unable to suggest good points and bad points about a book they hadn’t read. Instead, deceptive reviewers would make a single point and then continue on – elaborate –  in the same vein, sometimes in a rambling or waffly manner (for example, 3 below).

1. You’re not going to find endless action, shocking plot-twists, or gut-busting comedy. What you will find is a simple beautiful poetic story about life, desire and happiness.

2. Sometimes things happen a bit too conveniently to ring true, sometimes it is predictive, but in the end you won’t care.

3. This story is extremely interesting and thought provoking.  It raises many questions and brings about many realizations.  As you read it becomes increasingly clear we really are not so different after all.  Great read!

Figure 1: Extracts from Amazon book reviews used in Popoola (2018)

Contrasting is most often (although certainly not always) signalled with ‘but’ – as in example 2  above – so a rough and ready technique for testing whether Contrasting is more common in truth than deception is to compare the frequency of ‘but’ in known real and fake reviews. I followed up my initial findings by analysing 1570 true and fake book reviews and found authentic reviews do use ‘but’ substantially more than fake reviews and that authentic reviews are more likely to use ‘but’ to signal Contrast relations (see Figures 8 and 9 below; full findings, along with data source, can be found in Popoola (2018).)

                                     USE OF ‘BUT’ IN TRUE VS. FAKE REVIEWS

screenshot 2019-01-28 08.43.10

What does this have to do with fake news? Presenting all sides of a case or argument, in the name of objectivity and balance, is a conventional feature of the news story genre because it is fundamental to journalism ethics. Balancing and Contrasting are not the same but linguistically they can be performed  with similar language – contrastives. Contrastives include conjunctions such as ‘but’, ‘either’ and ‘or’, conjunctive adverbs such as ‘however’ and prepositions like ‘despite’. This can be contrasted with the use of additives – e.g. ‘and’, ‘also’, ‘in addition – for Elaborating. Contrastives and additives are two of four general linguistic strategies for connecting texts  – cohesion devices.

My hypothesis is that there will be variation between the different news sources in the proportion of additives vs. contrastives used  – and, just like the book reviews, authentic news sources will use more contrastives.  Since additives are the most common way of connecting textual information (‘and’ is the third most common word in English, six times more frequent than ‘but’ – good word frequency list here if you are into that kind of thing), I calculated the relative use of contrastives compared to additives

I piloted this approach on a 1.7million word corpus of political news stories downloaded from 15 news sources in Spring 2017. The 15 sources were a representative mix of legacy and contemporary news media from acrosss the political spectrum: Bipartisan Report; Breitbart; Freedom Daily; The Daily Caller; The Daily Mail; Addicting Info; Alternative Media Syndicate; The Daily Beast; Think Progress;  BBC; CBS; CNBC; CNN; The Huffington Post; The New York Times.

I used the following definitions for the cohesion strategies:

  • contrastives = ‘but’|’either’|’or’
  • additives = ‘and’|’also’|’in addition’.

Figure 2 is a scatterplot of each news outlet’s proportion of additive and constrastive relation cues. It shows substantial variation in text cohesion strategies with six news sources lying over one standard deviation from the mean (i.e. outside of the yellow rectangle); additive cohesion is particularly frequent for The Daily Mail, Breitbart, Bipartisan Report and The Daily Caller , while contrastive cohesion is particularly frequent for The Daily Beast and the BBC.

 

additives contrastives map

Figure 2: Scatterplot of variation in text cohesion strategies in 15 online news sources. x=Contrastive /  [Contrastive+Additive]; y=Additive / [Contrastive+Additive]. Coloured rectangle represents 1 SD from mean.

Example of additive textual cohesion from Breitbart

breitbart additive norm size

Full article here: https://www.breitbart.com/politics/2017/03/31/h1b-move-funded-cheap-labor-lobbies/

Example of contrastive textual cohesion from The Daily Beast

daily beast contrastive norm

Full article here: https://www.thedailybeast.com/nikki-haley-steps-up-in-syria-crisis

So, we can see that the textual cohesion strategies can differentiate articles within the genre. My hypothesis says that the news articles using more additive strategies are more likely to be fake, in this case that The Daily Mail and Breitbart are more likely to produce fake news than the BBC and The Daily Beast. How do we know what is fake? Since we are a looking at the overall source rather than individual articles, we can use a general scoring system. For now, we’ll use the simple ‘failed a factcheck’ test. All the news sources that have ever failed a factcheck are marked in red in Figure 4 below.

cohesion map

Figure 4: Scatterplot of variation in text cohesion strategies in 15 online news sources. News sources with failed factchecks marked red (source: mediabiasfactcheck.com)

As can be seen, 9 of the 15 news sources have failed a factcheck recently; factchecking by itself is not the most sensitive discriminator. However, 6 of the 15 news sources tend towards additive cohesion strategies and all 4 of the highest additives have failed factchecks whilst neither of the prototypical contrastive texts are ‘fake by this definition.

So, it would seem that just like with fake book reviews, there is a tendency for fake news to lack shades of contrast. Perhaps deceivers are less likely to contrast their lies with the truth because it dilutes their deception. As you read, I’ve been adding more news sources to the analysis and refining the cohesion strategy specifications. Stay tuned!

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The Fake Review Challenge

Fake amazon

Can you tell a real book review from a fake book review? Below are five tips that I have learned from my research. It is likely to be fake if:

1) It reads like a press release

2) It reads like the blurb on the back of the book

3) It is either extremely positive or extremely negative. Authentic reviews are more nuanced and tend to mention possible negatives even in 5-star reviews.

4) Fake reviewers are more likely to talk about themselves

5) Fake reviewers are more likely to address you the reader

The more of these signs you find in a review, the more likely it is to be fake. Now take the Fake Review Challenge and see how you get on!

 

Did Hillary Clinton’s PR team solicit fake Amazon book reviews for ‘What Happened’? Part #2

In the first post of this two-part linguistic investigation, we set up an unsupervised analytical approach; factor analysis to identify latent dimensions of linguistic variation in the ‘What’s Happened’ reviews then feeding these dimensions into a cluster analysis in order to identify a small number of distinct text types. We know that the reviewing patterns for ‘What Happened’ displayed ‘burstiness’ i.e. a high frequency of reviews within a short period of time (see Figure 1 below).  As Figure 2 below hypothesises, if there is a text type cluster that displays similar ‘burstiness’, we can infer that there there was probably some level of coordination of reviewing behaviour and identify linguistic features associated with less-than-authentic reviews.

Screenshot 2018-10-28 11.00.04

Figure 1: Quantity, frequency and rating of ‘What Happened’ book reviews in the first month after launch.

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Figure 2: Hypothesis for fake review detection using cluster analysis with time series. 

The factor analysis found four dimensions of language variation in the ‘What Happened’ reviews: Engagement; Reflection; Emotiveness; Commentary.

Dimenson 1: Engagement

One linguistic dimension of these reviews describes levels of Engagement. In engaging reviews, writers directly address (using ‘you’ pronouns) either the reader or Hillary Clinton. The style is conversational and persuasive with exclamations, questions and hypotheticals used to interact with the reader.

THANK YOU for telling your story Secretary Clinton! You have accomplished so much and are a genuine inspiration. If they weren’t so afraid of you, they wouldn’t work so hard to shut you up. Keep fighting and I will too!

It’s her side of the story. That’s what it claims to be, and that’s what it is. For those who don’t like it because you disagree with her, you’re missing the point. After reading it, did you get a better feel for who the candidate was, what she was thinking, and even what her biases were and are? If so, then the book does what it claims to do.

Non-engaging reviews are more  linguistically dense, using longer words and giving complex descriptions of the content.

The second chapter describes the days after the election, when she first isolated herself from the deluge of texts and emails from well-wishers. Eventually, however, she threw herself back into the fray, writing letters of thanks to supporters, attending galas, and spending time with her family.

Dimension 2: Reflection

A second linguistic dimension sees reviewers reflect on their personal experience of reading the book. This may include autobiographical elements, narratives related to the book purchase and reading occasions as well as feelings had while reading. The key linguistic features here are ‘I’-pronoun and past tense:

Like many other people, I wondered if this book would really be worth reading. I voted for Clinton but I wondered how much value there could be in her account of the 2016 Presidential election campaign. Luckily, this book is so much more. It hit my Kindle on Tuesday and as it happens I had three airplane flights (including two very long ones) on Wednesday and Thursday, so I made it my project for those flights. I didn’t have to force myself to keep going; once I started, her narrative and the force of her ideas and anecdotes kept me reading.

Dimension 3: Emotiveness

Reviews with a high Emotiveness score were extremely positive in their praise of the book and, especially, Hillary Clinton. This was signalled by use of long strings of positive adjectives that might reasonably be considered excessive:

A funny, dark, and honest book by one of the truest public servants of her generation. Her writing on her marriage was deeply heartfelt and true. The sad little haters will never keep this woman down, and history will remember her as a trailblazer and a figure of remarkable courage.

The People’s President, Hillary delivers her heartfelt, ugly cry inducing account about What Happened when she lost the Electoral College to the worst presidential candidate in modern history. Politics aside, America lost when they elevated Russia’s Agent Orange to the presidency. Think what you will, but America missed the chance to have a level headed, intelligent and resilient leader, and yes the first female president.

Hillary’s a smart, insightful, resilient, inspiring, kind, caring, pragmatic human being. This book is a journey through her heart and soul.

Dimension 4: Commentary

Reviews with high Commentary focused on Hillary Clinton and the other actors in the election story (high use of third person pronouns). The reviews analyse and evaluate Clinton’s perspective and explanation of what happened in 2016, in a conversational manner much like a TV commentator or pundit.

I disagree with the reviewers who says Hillary doesn’t take responsibility for her mistakes. She analyzes all the reasons she thinks she lost the election–yes, she talks about Russian interference, malpractice by the FBI, and false equivalence by the mainstream press IN ADDITION TO missteps she thinks she made. My own take is that she doesn’t pay enough attention to the reasons why Bernie Sanders was able to command so strong a following with so few resources; but that is part and parcel of who she is.

Historical memoir from the first female candidate for a major political party…a unique perspective and platform to write from. She does recount her successes as well as her failures…she was mostly shut down during the campaigns by repetitious questions and by over-coverage of Trump by the media. She is intelligent and well-informed and states her case without self-pity.

Having the identified these four linguistic functions in the ‘What Happened’ reviews, the trick is to see how they combined to form clusters of review text types – and whether any one of these clusters is more strongly correlated with the high frequency and early reviews.

As Figure 4 shows, hierarchical cluster analysis identifed four review text types: ‘Tribute’ reviews, the largest cluster, have high Emotiveness; ‘Pundit’ reviews have high levels of Commentary and Engagement; Content descriptive’ or ‘spoiler’ reviews talk about what’s in the book in an objective manner i.e. without Reflection or Engagement; ‘Experiential’ reviews narrate the writer’s personal Reflection on the experience of reading the book.

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Figure 4: 4-Cluster solution with mean factor loadings, interpretations and percentage of total reviews.   

So, we have these four review text types…do any of these correlate with the bursty reviewing patterns identified? Figure 5 below shows that the actual linguistic pattern of ‘What Happened’ reviews appears to correlate with the burstiness pattern; a large proportion of the first day reviews are Tribute reviews and most of this review type occurs within the first week before tailing off during the rest of the month. The fact that no other review type is particularly time sensitive suggests that, at the very least, Tribute reviews are correlated with early reviewing and are potentially evidence of  coordinated recruitment of Hillary Clinton’s ‘fans’ as book reviewers.

What Happened Cluster Time Series

Figure 5: Distribution of ‘What Happened’ review text types during first month following book launch, compared to hypothetical deceptive and non-deceptive distributions. 

If Hillary Clinton’s PR team did solicit positive reviews in the early days of the book launch, perhaps it is not surprising; they would have been responding to an extensive negative campaign against her book which included manipulating review helpfulness metrics (i.e. massive upvoting of low-rated reviews) as well writing fake negative reviews.

From an investigative linguistic perspective, this analysis shows that: a) suspicious activity can be detected using linguistic data as well as network or platform metadata; b) unqualified praise and intense positive emotions are deception indicators in the online review genre; and c) cluster analysis is an effective way of recognising linguistic deception features in an unsupervised learning setting.

Who is ‘we’? Investigating pronouns for deception.

If you are ever arrested and asked to make a statement by the police on US soil, be careful with your pronouns. Law enforcement officers in the US are likely to have received training in analysing statements for deception from Mark McClish or Don Rabon, which means the pronouns you use will be inspected very closely. McClish and Rabon come in for a lot of stick from forensic linguists working in academia, due to the lack of citations and over-generalisations in their blogs and best-selling books. 

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But to be fair, many of the leading academics working on lie detection have said similar things about the use of first person pronouns being an indicator of veracity. Newman, Pennebaker and colleagues, in their seminal work ‘Lying Words: Predicting Deception From Linguistic Styles’ provided some empirical support for the correlation between low self-reference and deception; subsequently it has been found to broadly hold in online dating profiles, business communication and criminal narratives (but, interestingly, not the case in consumer reviews which use ‘reader engagement’ as a deception strategy).

It should be noted, however, that Newman and Pennebaker’s prediction rate was 67% – that is wrong 1 in 3 times. Consequently, simply counting the use of any first person pronouns is not by itself the magic cue for deception detection. There are a number of different first person pronouns – I, me, my, mine, myself, we, us, our, ours, ourselves. Not only do these have different strengths in terms of socio-psychological ideas of distance and commitment (compare ‘I was hit’ with ‘The car hit me’) but they work differently linguistically. For example,  ‘I’ and ‘me’ will correlate with verbs, ‘my’ with nouns; also ‘I’ correlates with stance and modal verbs (‘I thought’, ‘I tried’, ‘I would’) so is a more ‘active’ first person pronoun than the passive ‘me’.

The above also applies to ‘we’, ‘us’ and ‘our’. In addition first person plural pronouns have the additional pragmatic parameter of clusivity to distinguish who exactly is included in the ‘we’ (see Figure 1 below). And there is the linguistic phenomenon of nosism which includes royal and editorial ‘we’ (see Ben Zimmer’s excellent 2010 article to fully appreciate the complexities of ‘We’).

clusivity we.png

Figure 1: Referential parameters of ‘we’: inclusive (left), exclusive (right). By LucaLuca. Reproduced under Creative Commons licence

A case in point is ex-UK Prime Minister David Cameron’s response to questions about his alleged use of off-shore tax havens to avoid paying tax, as revealed in the Panama Papers leak. In April 2016, 10 weeks before the EU Referendum and his subsequent resignation, Cameron was taking questions about the upcoming referendum and speaking in support of remaining in the EU at a town-hall style Q&A event held at PriceWaterhouseCoopers’ Birmingham offices.

Figure 2 is a transcript of David Cameron’s response to an unexpected question from Sky News journalist Faisal Islam regarding the controversy over his connection to an offshore investment company (Blairmore Holdings) owned by Cameron’s late father. Cameron denied owning any shares or offshore investments but was roundly criticised for his evasive answer (Cameron restricts his answer to the present tense, despite Faisal Islam’s specific temporal reference to the past and the future – lines 4-5). Five days later, under public pressure to resign, Cameron was forced to admit that he had owned shares in his father’s business (which he sold at a profit shortly before taking office as Prime Minister).

Cameron Panama response 1

Figure 2: Transcript of David Cameron’s first public response on the Panama Papers allegations. Given during a Q&A with workers at accountancy firm PWC in Birmingham, 5 April 2016.

David Cameron’s use of pronouns is an excellent example of linguistic duplicity, shifting between inclusive ‘we’ (yellow), exclusive ‘we’ (red) as well as ‘I’, all in reference to himself; the identities referred to by ‘I’ are split between David Cameron as a UK citizen and his role as Prime Minister.

Furthermore, the scope of ‘we’ varies within the text and is sometimes unclear. In answer to a question about “you and your family” with regard to the financial business of one’s late father, one might expect ‘we’ to refer to some aspect of family. However, Cameron initially moves to include the whole audience (and viewers) in his personal financial affairs by referring to  ‘we’ as a nation with the contextual reference “our tax authority” and later “our own country” (line 8). In the middle of his response, a different (exclusive) ‘we’ appears mid-sentence – “I have a house, which we used to live in, which we now let out while we are living in Downing Street” (lines 16-17). There is no explicit reference to the scope of this ‘we’ but the reference to personal property ownership means one can assume that is not the national ‘we’.

If one assumes that it is the ‘we’ originally asked for in the reporter’s question – i.e. “you and your family” – then Cameron has violated the right frontier constraint (Webber, 1988), which stipulates that anaphoric elements such as pronouns are interpreted in ambiguous cases by reference to information at the end of the previous discourse unit i.e. the right frontier (for languages with left-to-right scripts). That Cameron returns to ‘we as a nation’ for the remaining text further highlights his dynamic use of pronominal reference.

The linguistic duplicity displayed by David Cameron above is in stark contrast to the language he used when owning up to his involvement with Blairmore Holdings in a hastily-arranged national TV interview. As the transcript shows, Cameron doesn’t use ‘we’ at all in response to similar questions. This case study shows that assessing veracity and potential deception by tracking pronoun use is valid but more complex than simply counting; the inherent capacity for linguistic duplicity is contained within a complex system of deceptive pragmatics.

What does honesty look like (statistically)?

Certain linguistic features (e.g  reference, modality) facilitate deception because they are malleable to context and flexible to interpretation. My first blog post showed that deceptive communication contains ‘outliars’, portions of texts with an unusually high concentration of these linguistic features; in the second post we saw that the linguistic hotspots where these features cluster can be taken as ‘points of interest’ worthy of further investigation. Of course, liars do not have a monopoly on the use of modals! Furthermore, truth-tellers can sometimes be mistaken for liars due to nervousness, fear of disbelief, or perceptions of powerlessness (known as the ‘Othello error’). So what does honesty (non-deceptive) communication look like?

sharapova mistake

In my Standford Decepticon 2017 conference paper I tested the ‘Outliar’ investigative linguistic methodology on honest admissions of doping – true confessions – by the following five sports persons and professionals:

true doping confessions.png

The Maria Sharapova case took the tennis world by surprise (she was the first high-profile female tennis player to fail a drug test). In 2016, Sharapova was banned from competition after testing positive for meldonium during the Australian Open in January of that year. Meldonium is a heart medication that was found by the World Anti-Doping organisation (WADA) to be particularly popular amongst sports persons from Russia and Eastern Europe, perhaps due to its ability to block the body’s conversion of testosterone to oestrogen. Having placed meldonium on a watch list in 2015, WADA had fully prohibited the substance from January 1 2016, two weeks before the Australian Open. Following the failed drug test, Sharapova admitted she had been taking meldonium as medication since 2006 and stated that she had negligently and inexcusably missed the communications from WADA prohibiting its use.

Linguistic analysis of the explanation Sharapova gave to fans via her Facebook page shows two ‘outliars’ at the beginning and end of the post (see Figure 4 below).

Sharapova outliar graph

[1] I want to reach out to you to share some information, discuss the latest news, and let you know that there have been things that have been reported wrong in the media, and I am determined to fight back. You have shown me a tremendous outpouring of support, and I’m so grateful for it.

[13] I have been honest and upfront. I won’t pretend to be injured so I can hide the truth about my testing. I look forward to the ITF hearing at which time they will receive my detailed medical records. I hope I will be allowed to play again. But no matter what, I want you, my fans, to know the truth and have the facts.

Figure 3: Outliar analysis of Maria Sharapova’s 2016 Facebook post and outlier extracts.

Sharapova begins her post by suggesting she has been a victim of unjust media coverage. It had been widely reported that she had received five ‘warnings’ about the upcoming change to the WADA regulations. Sharapova agreed that she had received newsletters with links to the WADA rule changes but argued that these were ‘communications’ rather than warnings through which one had to “hunt, click, hunt, click, hunt, click, scroll and read” in order to find information about the prohibition. Sharapova ends her post by strongly maintaining that she is being honest about her genuine mistake (of using Meldonium as medication after the ban).

These anomalous extracts are particularly emotional when compared to the main body of this post, in which Sharapova gives specific details about all the communications she did receive (see yellow highlighted text in Figure 4 below). There is a lot of literature that suggests specific details are a strong indicator of veracity in legal genres such as witness statements. (Professor Aldert Vrij’s research on Criteria Based Content Analysis is a good place to start.) These anomalous extracts could just be ‘Othello errors’ that are confusing emotional intensity for deception.

Sharapova FB 1cSharapova FB 2c

Figure 4: Maria Sharapova Facebook post, March 2016. Last accessed 21/7/2018

Accounting for the ‘Othello error’ is one reason a full ‘Outliar’ analysis uses an additional measure of language change within a text – intratextual language variation – when assessing text veracity. Texts can range from having a uniform style with consistent use of features throughout – a stable text – to displaying marked changes in language style at several points – variable or ‘spiky’ text.  Outliar captures this by summing the amount of change shown in a text.

Figure 5 is an example of this. It compares ‘Outliar’ analysis of Sharapova’s Facebook post (left) one of a Lance Armstrong TV interview in whch he falsely denied doping (see previous blog for more discussion of Armstrong’s deception). Visually, you can see that  Lance Armstrong’s language use displayes high variability in comparison to which Sharapova’s language is relatively stable.

Figure 5: Comparison of the ‘Outliar’ analysis of Maria Sharapova’s Facebook post (left) and Lance Armstrong’s ESPN interview (right) .

Figure 6 below shows a statistical measure of intratextual language variation for five false doping denials vs. five true doping confessions (see p7 here for the formula). It can be seen that the deceptive communications show more language change than the honest ones. So, combining outlier text detection with an overall measure of language variability can be helpful in distinguishing honesty from dishonesty. Frequent and marked language style change is a signal of potential deception.

intratext analysis edit 2

Figure 6: Analysis of intratextual variation. Y-axis = total intratextual variation measured as aggregate z-score for each text; X-axis represents ten texts in total –  five deceptive texts (false denials by: 1) Barry Bonds; 2) Linford Christie; 3) Lance Armstrong; 4) Alex Rodriguez; 5) Marion Jones) and five honest texts (true confessions by: 1) Maria Sharapova; 2) Dwain Chambers; 3) Victor Conte; 4) Floyd Landis; 5) Levi Leiphemer)

In Sharapova’s case, the tribunal were satisfied she had not intended to cheat (although she was found to have also taken the drug to enhance her performance) and her relatively light ban (reduced from two years to 15 months on appeal) reflected the fact that she had been negligent but not deceptive. I would argue that the (relatively) stable language of both Sharapova’s Facebook post and the initial press conference where she announced her drug test failure support the tribunal finding. The press conference video is below – judge for yourself.

 

 

The case of the asthmatic cyclists: deception detection as investigative linguistics

Did you know that Sir Bradley Wiggins, Sir Mo Farah and I-thought-he-was-a-sir Chris Froome all suffer from asthma? As do a number of other succesful sports persons accused of using performance-enhancing drugs?  What I call ‘investigative linguistics’ led me down this particularly rabbit hole. My investigative linguistic approach examines texts for ‘points of interest’ (POIs). It uses deception detection tools on communications with unknown veracity in order to automatically identify POIs. One benefit is that you can approach a topic without any prior knowledge or biases and quickly find avenues that are objectively worth exploring.

After analysing the known deceptions of Lance Armstrong (see previous blog), I collected a bunch of statements made by sports people admitting or denying their use of perfomance-enhancing drugs. Based on currently available evidence these statements were divided into three categories: a) false denials, b) true confessions and c) presumed-to-be-true denials (see my Stanford Decepticon 2017 presentation for full details). For the ‘true’ denials category, I picked five recent high-profile cases: two relating to the cycling controversy around Sir Bradley, Sir David Brailsford and Team Sky (video explainer), and three connected to the controversy around the infamous athletics coach Alberto Salazar and his Nike Oregon Project which engulfed Sir Mo Farah, the Canadian Cameron Levins and, indirectly, Paula Radcliffe MBE.

true denials dataset

It was a surprise to me that each of the interviews (graphed below) mentions asthma and related issues. If I had done some prior research I would have realised that the provision and use of asthma medication during and around major sport events was a key issue in sports doping. Still, it shows that deception detection techniques can be used for exploration i.e. to find the ‘points of interest’ worthy of further investigation. Furthermore, a ‘naive’ approach helps to avoid unconscious bias affecting the analysis

Slide1

(‘Outliar’ analysis of four ‘true denials’. Interview responses are represented as a time series on the x-axis (c.30-60 second chunks). Green shading indicates ‘outliar’ text. Asthma mentions marked with ∇)

In Bradley Wiggins’ Guardian interview (given to counter suggestions of illegal doping during the 2012 Tour de France), the analysis highlights inconsistencies in Wiggins’ stance towards his asthma allergy:

[14] I was paranoid about making excuses: “Ah, my allergies have kicked in.” I’d learned to live with this thing. It wasn’t something I was going to shout from the rooftops and use as an excuse and say, “my allergies have started off again”. That’s convenient isn’t it Brad, your allergies started when you got dropped.

[17] I didn’t mention it in the book. I’d come off a season of … I’d won everything that year. When I was writing the book I wasn’t sat there thinking, “I’d better bring my allergies up”. I was flying on cloud nine after dominating the sport all year. It wasn’t something that I brought to mind.

In these two extracts, asthma is simultaneously a big deal and a non-issue for Wiggins. While this does not in any way confirm deception or guilt, it does indicate a defensive stance that is worth investigating. This can be contrasted with Mo Farah’s discussion of his own asthma:

[4] “This picture has been painted of me. It’s not right. I am 100% clean. I love what I do. I want to continue winning medals. But I want people to know that I am 100%, I am not on any drugs, I am not on thyroids, I am not on any other medication. The only medication that I am on, I am on asthma and I have had that since I was a child. That’s just a normal use. I am on TUE [therapeutic use exemption] where you have … it’s just the normal stuff. And that’s it.” – Sky Sports interview, 2015

In contrast to Wiggins, Mo Farah volunteers information in a non-defensive fashion about his asthma and use of Therapeutic Use Exemptions (TUEs – a doctor’s note and prescription). Canadian runner Cameron Levins’ response to questions about his use of prescription drugs registers as more ‘interesting’ than Farah’s although not as high as Wiggins:

Interviewer: No prescription drugs?

Levins: I have some medication I take for my asthma, but that is something that is wrong with me. I’m asthmatic.

Interviewer: Was that before you came on with the (Nike Oregon) Project?

Levins: Yeah, I was dealing with it before I joined the project actually. A little bit after the London Olympics I started having quite a bit of difficulty with it. So it was before I joined the project.

Levins later goes on to explain that “adult onset asthma is pretty common”. Obviously, having asthma since childhood is easier to defend which may explain the higher ‘interestingness’ score of Levins response compared to Farah.

In a 2016 interview with Sky Sports news David Brailsford, director of Team Sky (whose riders included Bradley Wiggins and Chris Froome), offered the following highly ‘interesting’ reply when asked about his team’s covert use of Therapeutic Use Exemptions to obtain otherwise prohibited drugs:

[5] We’ve reviewed this over the years as we’ve moved forward. We have changed our policy, we’ve changed the way we do it, and in the future going forward, I think we’re going to take the next step, which has been debated on a wider basis across the whole of the TUE process, and look at having the consent of the riders to make all TUEs transparent

In this segment, Brailsford tries draw a line under anything that may have occurred in the past, using many words related to looking to the future. In no way presuming anything illegal on Brailsford or Team Sky’s part, previous policy would clearly be a ‘point of interest’.

So, this analysis suggests that cyclists use of asthma medication is more ‘interesting’ than that of athletes. (In Farah’s interview, the analysis flags his comments about missed drug tests rather than specific doping allegations.) Understanding the reasons for this can then provide a focus for further investigation. As Chris Froome’s recent successful appeal shows, the asthma issue may be due to faulty regulations based on models with a tendency to generate ‘false positives’ – itself a form of deviance if not deception.

froome inhaler

(picture © BBC/Getty Images, 2018)

For this naive analyst, investigative linguistics revealed an important connection between asthma and sports doping that is clearly ‘interesting’. Application of the same techniques to the domains of business, politics and finance will definitely be interesting…