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.

Screenshot 2018-10-27 20.29.15

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.

Did Hillary Clinton’s PR team solicit fake Amazon book reviews for ‘What Happened’? Part #1.

September 12, 2017, was the launch day for Hillary Clinton’s autobiographical account of the 2016 election she lost to Donald Trump, definitively entitled ‘What Happened’. By midday 1669 reviews had been written on Amazon.com. By 3pm over half of the reviews, all with 1-star ratings, had been deleted by Amazon and a new review page for the book had been set up. After Day 1, ‘What Happened’ had over 600 reviews and an almost perfect 5 rating. What happened?!

What Happened Indeed

Figure 1: Genuine support or fake reviews? Hillary Clinton’s ‘What Happened’ Amazon rating 1 day after launch (and after all the negative reviews were deleted )

There were good reasons to view the flood of negative reviews as suspicious. Only 20% of the reviews had a verified purchase and the ratio of 5-star to 1-star reviews – 44%-51% – was highly irregular; the vast majority of products reviewed on Amazon.com display an asymmetric bimodal (J-shaped) ratings distribution (see  Hu, Pavlou and Zhang, 2009), in which there is a concentration of 4 or 5 star reviews, a number of 1-star reviews and very few 2 or 3 star reviews.  The charts in Figure 2 below, originally featured in this QZ article,  show the extent to which ‘What Happened’ was initially a ratings and purchase pattern outlier.

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Figure 2: Two charts indicating the unusual reviewing behaviour for ‘What Happened’. Source: Ha, 2017 

Faced with accusations of pro-Clinton bias as a result of deleting only negative reviews, an Amazon spokesperson confirmed that the company, in taking action against “content manipulation”, looks at indicators such as the ‘burstiness’ of reviews (high rate of reviews in a short time period) and the relevance of the content –  but doesn’t delete reviews simply based on their rating or their verified status. (Hijazi, 2007).

It would appear that Amazon have taken on board the academic literature suggesting that burstiness is a feature of review spammers and deceptive reviews (e.g. this excellent paper by  Geli Fei, Arjun Mukherjee, Bing Liu et al. ) and that it is right to interpret a rush of consecutive negative reviews close to a book launch as suspicious.

But what about the subsequent burst of 600+ positive reviews? One might expect the Clinton PR machine to mobilize its own ‘positive review brigade’ in anticipation of , or in response to, a negative ‘astroturfing’ campaign against her book. One could even argue that it would be foolish not to manage perceptions of such a controversial and polarising book launch. If positive review spam is identified, should it also be deleted?

I tracked the number of Amazon reviews of ‘What Happened’ for a month after its launch on the new ‘clean’ book listing (the listings have since been merged but you can see my starting point here). Figure 3 below shows clear signs of ‘burstiness’; the rate of reviewing decreases exponentially over the first month even while the rate of 5-star reviews remained consistently high.

Reviews by day

Figure 3: Number and frequency of ‘What Happened’ reviews in the first 30 days following its launch and deletion of negative reviews. 

So, it is perfectly legitimate to ask whether the ‘What Happened’ reviews were manipulated through ‘planting’ of ‘fake’ 5 star reviews written for financial gain or otherwise incentivised e.g. in exchange for a free copy of the book, which would circumvent Amazon’s Verified Purchase requirement. With my investigative linguist hat on, I’m wondering if there are any linguistic patterns associated with this irregular – and potentially deceptive – behaviour? (If there are, these could be used to aid deception detection in the absence of – or in tandem with –  non-linguistic ‘metadata’.)

A line of fake review detection research has confirmed linguistic differences between authentic and deceptive reviews, although the linguistic deception cues are not consistent and vary depending on the domain and the audience (see my brief overview in this paper). Since we don’t know the deception features in advance and no ground truth has been established (i.e. we don’t know for sure if there was a deception), I’m going to use two unsupervised learning approaches appropriate for unlabeled data: factor analysis, to find the underlying dimensions of linguistic variation in all the reviews, followed by cluster analysis to segment the reviews into text types based on the dimensions with the hope of finding specific deception clusters.

If there is a text cluster that correlates with ‘burstiness’ – i.e. occurs more frequently in the reviews closest to the book launch date and/or occurs repeatedly within a short time frame – then that would suggest there are specific linguistic styles and/or strategies correlated with this deceptive reviewing behaviour. The existence of such a distinct deception cluster would strongly suggest that Clinton’s PR team gamed the Amazon review system (understandably, in order to counter the negative campaign against the book).  Alternatively, different reviewing strategies might be distributed randomly across the review corpus and unrelated to its proximity to the book launch date. This would weaken the argument that linguistic variation in the reviews is a potential deception cue. The two scenarios are illustrated in Figure 4 below:

Deception cluster hypothesis

Figure 4: Hypothetical illustration of how review text types (clusters) might be distributed over a 30 day period in the case of astroturfed fake reviews (top) or genuine positive reviews (bottom). 

My prediction? Surely, Hillary Clinton’s PR team would not so be so brazen as to solicit fake positive reviews in bulk and in an organised fashion. Yes, there were a disproportionate number of reviews written in the first few days but I believe this was a spontaneous groundswell of genuine support. I do expect there to be a few different types of linguistic review style, reflecting the different ways in which books can be reviewed (e.g. focus on book content; retell personal reading experience; address the reader – these are some of the review styles I presented at the ICAME39 (2018) conference in Tampere). However, if the support is spontaneous I would expect these review styles not to be correlated with burstiness or other deceptive phenomena but to occur randomly throughout the month.

Check back here in a few days for Part #2:  Results and Discussion!

 

 

 

 

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. 

language police.png

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.