conspiracy, complaints, and fraud: the language of reasons
TRANSCRIPT
The language of reasons
Tyler Schnoebelen
Conspiracy, complaints, and fraud
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1) Showing how computational linguistics solves business problems2) Identifying markers of fraud using language data
For company-internal fraud/compliance investigatorsFor government/regulatory/consumer advocacy
3) Detecting and using rationalization and reason-givingThe importance of emotionThe case of because in
Consumer complaintsConspiracy forum posts
Hi! Welcome to the slides for this talk—also check out the Notes. Basically this talk is about:
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Fraud
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The Association of Certified Fraud Examiners looked at 1,483 fraud cases reported in 2014
They estimate global fraud loss is at least 5% of revenue for companies
Estimate of losses to fraud, worldwide
$3.7 trillion
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Financial statement fraud is much more expensive
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By industry
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For the big dollars, look to the top
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What are the red flags for fraudsters?
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Losses are rarely recovered
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Detecting deception
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Prior work tends to be “word lists” or experiments
L&Z used 29,663 transcribed quarterly earnings calls
16,577 CEO Q&A responses14,462 CFO Q&A responses
L&Z keep track of when quarterly financial statements were later restated (during first call they knew something was amiss)
Depending on strictness of restatement, 14%, 7% or 5% of the calls had deception in them.
Larcker & Zakolyukina (2010)
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Larcker & Zakolyukina (2010)
CEOs CFOsReferences to general knowledge (you know) more more
Non-extreme positive emotion words fewer fewer
References to shareholder value/value creation fewer fewer
Self-references fewer
3rd person plural/impersonal pronouns more fewer
Extreme negative emotion words fewer
Extreme positive emotion words more
Certainty words fewer more
Hesitation words fewer more
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Text analytics
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Text analytics
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Linguistics:Scientific study of language
Machine Learning:Automatically train computers to make human-like decisions● Compliance monitoring
● Enterprise search● E-Communications surveillance● Technology assisted review● Sentiment analysis● Deception detection● Text summarization
Natural Language Processing:Enable machines to automatically derive meaning from natural language input
Fraud and compliance in digital communications
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Early case assessment
Relevancy filtering
Risk Scoring
Key entitiesStrategic communications
Spam, NewslettersNear de-duplication
Fraud diamondSentimentPersonal communications
Investigation stage Models100% Data volume
30%
10%
< 1%
Top-down vs. bottom-up text analytics
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“Bribe” …
“Tea money”
“Facilitation payment” “Backhande
r”
Top-down (Search)● Rules-based● False positives/negatives● Brittle
Bottom-up (Discovery)● Statistical● Highly accurate● Adaptive
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Comparing rules vs. machine learning
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High accuracy on complex task after only 1 day of work
Project goal: Uncover key documents relevant to Energy Regulation out of 200,000 messages that matched raw keywords
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Flexible Ontology
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Develop rich ontology for investigative analytics and insights at scale
Cline
Cline
1Client’s
QuestionsKnown Areas of Interest
Pressure
Rationalization
Names
Opportunity
Capability
Emotions
Topic Modeling
Themes
?
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Data gets smarter and more accurate through adaptive system
Adaptive System Structured Data Reports
Action• Annotation suggestions• Document priority• Shortest path for coverage• Error detection
Machine Learning
Optimization Prediction Engine
Human Review
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Idibon’s models drive more accurate, scalable investigations of fraud
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Identify indicative language• Identify and extract indicators correlated with fraud• Gather data from disparate structured,
unstructured, public, and private data sourcesModel fraud within the organization
• Score and rank individual custodians by likelihood of fraud
• Summarize indicators of fraud by department or scheme
Scale across people and clients• Model fraud using documents from multiple
custodians• Build replicable models for different client types
Monitor and track risk• Model on-going risks in client interactions• Track known liability or non-compliance issues
1.
2.
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4.
Detecting fraud requires a variety of modelsStrategic Communications: Automatically identify important communications based on the language used in emails with a BCC recipient
Fraud Triangle and Fraud Diamond: Identify messages containing indicators of Motive, Opportunity, Rationalization and Capability to risk-rank actors and their communications
Key Entities: Discover people, places, organizations, and other entities mentioned in communications to uncover hidden relationships
Personal Messages: Flag messages that are intimate in nature and that may contain evidence of illicit behavior or collusion
Sentiment Analysis: Categorize communications as positive, neutral, or negative
Taboo Words and Obscenity: Identify emotionally charged language that may reflect behaviors and events of interest
enron report merger (Corporate communications about mergers that you probably DON’T care about)
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Find needles in haystacks: quickly hone in on relevant areas of the data
legal f&j citizens “I also find the advance ethical waiver language repugnant, but could agree to it if the other modifications mentioned could be made.”
employees enron bankruptcy“Michelle, here is a suggested revision to Section 3.4 B … If a terminated employee who is entitled to receive a severance benefit … the severance benefit payable under the Plan shall be reduced and offset”
time good back(Lots of irrelevant stuff about home, weekends, Thanksgiving, etc.)
Sentiment analysis and automatic topic discovery reveal significant communications
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Negative: Antitrust issues, M&A, Insider TradingPositive: Product Releases, Employment
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The Fraud Triangle (and briefly, the Fraud
Diamond)
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The Fraud Triangle
Rationalization
Opportunity
FRAUD SCORE
Pressure
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The Fraud Triangle
Rationalization
Opportunity
FRAUD SCORE
Pressure
Pressure:
Incentives, wants, needs (e.g., gambling debts)
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The Fraud Triangle
Rationalization
Opportunity
FRAUD SCORE
Pressure
Opportunity:
Weaknesses in the system that allow fraud to happen
Image Placeholder
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Capability(Makes it a Fraud Diamond)
Personal traits and abilities
• Effective lying• Immunity to stress• Intelligence• Confidence
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But let’s return to the peaky point
Rationalization
Opportunity
FRAUD SCORE
Pressure
Rationalization:
Committing fraud is worth the risk
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When and how do people give reasons?
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Because because because because
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Conditions:• “Excuse me, I have (5 or 20) pages. May I use the Xerox machine?”
(no-because)
• “Excuse me, I have (5 or 20) pages. May I use the Xerox machine, because I’m in a rush? (because)
• “Excuse me, I have (5 or 20) pages. May I use the Xerox machine, because I have to make copies?” (because-empty)
The idea here is that the because-empty clause offers no information. For 5 pages: because = because-empty >> no-becauseThough when stakes are higher (20 pages): because > because-empty > no-because
Langer, Blank and Chanowitz (1978)
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• “Given” information comes before “new”—so usually people say “such and such happened because of X” rather than “Because of X, such and such happened”
• Given: what’s been said already, inferable, familiar, expected• Easier to process new information when it’s framed• See Chafe (1984) and lots of others
• “causal clauses are primarily used to back up a previous statement that the hearer may not accept or may not find convincing” (Diessel 2006)
• Conversation analysts find becauses offered by either speaker right before a disagreement
• In English speech, they are surrounded by pauses, hesitations, excuses, mitigations, indirectness, partial agreement, polarity reversals (see Ford & Mori 1994)
Quick lit review
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Two main coherence relations: cause-consequence and argument-claimCausality and Subjectivity are keyConsider:
The sun was shining CONNECTIVE the temperature rose quickly
Causality
The neighbors’ lights are out CONNECTIVE they are not at home
Subjectivity
Some languages use different connectives
Sanders (2003)
Causality Subjectivity
Dutch doordat want
French parce que puisque
German weil denn
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Children learning English learn things in this order (Bloom et al 1980):Additive < Temporal < Causal < Adversative
and < and then < because < so < but
That is, causal connectives are seen as more complex (see also Piaget 1924/1969, Katz & Brent 1968, Clark 2003, Vers-Vermeul 2005)BUT causally connected information is remembered better
And causal relations are read fasterReading time decreases when causality increases
More Sanders (2003)
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Digression! A new construction!
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24k “because X” tweets
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Because X is mostly playful but has strong affective underpinnings
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Because and emotions
In soap operas, guess what the word most associated with because is?
In the British Parliamnet, one of the words most associated with because…
Affect and emotion are bound up in discussions of reasoning and cognition • Damasio (1994)• Kahneman (2003)• Matthews and Wells (1994)• Zajonc (1980)• Loewenstein et al. (2001)• LeDoux (1998)
Reasoning needs emotions
“A sophisticated well-being monitor and guidance system that serves both attention-regulatory and motivational functions” (Smith and Kirby 2000: 90).
What are emotions?
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The need to convey and assess feelings, moods, dispositions, and attitudes is as critical as describing events.
We don’t just need to know predications, we need to know affective orientation to the predication.
(See the appendix for lots of ways that other languages encode emotional information)
Emotions are expressed in language
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Consumer complaints about banks and credit
agencies
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An act or practice is unfair when:
(1) It causes or is likely to cause substantial injury to consumers;
(2) The injury is not reasonably avoidable by consumers;
(3) The injury is not outweighed by countervailing benefits to consumers or to competition.
An act or practice is deceptive when
(1) The act or practice misleads or is likely to mislead the consumer;
(2) The consumer’s interpretation is reasonable under the circumstances;
(3) The misleading act or practice is material.
UDAAP (Unfair, Deceptive, or Abusive Acts or Practices)
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Whoa.
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Consumers detect fraud, tooData source: Consumer Financial Protection Bureau
21,206 consumer narratives
About banks and credit agencies
25% have the word “because” in it
(Limiting this study to because; also worth looking at are becuase, cuz, since, therefore etc.)
Companies/governments want to detect fraud
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Complaints with-because are much longer
Because-narratives No-because narratives
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Median word count
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Becauses per complaint
Three or more
Two becauses
Single "because"
11%
21%
68%
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Becauses happen much more in:• Bank account or service• Mortgage
And less often (proportionally) in:• Credit reporting• Debt collection
The categories most/least because-y
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Result: We strongly suggest someone look into Citimortgage’s business practices,
Cause: because at best they are completely incompetent, and at worst they are committing acts of fraud
Both Result-Cause and Cause-Result can happenBut as in most studies, Result-Cause accounts for the vast majority (here ~95%)
Structure of becauses
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“Verifiable if you just had a transcript”Objective-Result / Objective-CauseThey said I owed $10,000because I didn’t pay my bill for 3 months
“Not-verifiable even if you had a transcript”Subjective-Result / Subjective-CauseI am near tearsbecause I don’t know what to do
Krippendorff’s alpha (inter-annotator agreement): 0.85That’s very good agreementHighest for Objective-CauseLowest for Objective-Result (exactly what is the scope)All easily distinguishable—collapsing categories does not
result in higher alpha value
3 annotators, 4 annotation types
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The Idibon team! Thanks to Jason and Nick
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40% are Subjective-Result + Subjective-Cause33% are Objective-Result + Objective-Cause17% are Objective-Result + Subjective-Cause10% are Subjective-Result + Objective-Cause
A preference for matching types
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If you talk about your home, you aren’t objective
Subjective-Causes vs. Objective-Causes
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There’s really no difference between Subjective Results and Objective Results
There’s also no difference between Subjective Results and Objective Causes
Each of these tends to have a median of about 66 characters
But Subjective Causes are quite a bit different—a median of 84 characters (significant, p = 0.009303 by Wilcox test)
Affective information gets length
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because I found they have dealt fraudulently with many, many consumers
because the matter has not been handled in accordance with the law
BECAUSE NOW SPRINGLEAF FINANCIAL WOULD NOT WORK WITH THE NEW TRUSTEE OF THE TRUST
because Nationstar has dragged its feet in the face of its SIGNIFICANT error
Some examples of Subjective-Causes
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• Breakdown in process (repeated attempts, again, for more than, once again, over and over, again and again)
• Unresponsiveness (nothing happened, did not respond)• Misrepresentation (deceived, lied, misled, scam, told me
that)• Omission (did not tell me, failed to reveal, failed to bring to
my attention)• Emotion (my fear is that, i am angry that, frustrating)
• Subjective terms (patiently, unfair, not fair, unreasonable, struggling, sickening, absurd, allowed to do this, tedious)
• Dialogue acts (request, deny, thank, complain, refuse, accept)
• Mortgage processes (refinance, modification, refer, appeal, assistance)
Concepts in the cause and result clauses
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Fraud: How do people treat their companiesComplaints: How do companies treat their consumers?Now: How do people treat each other?
Finding healthy communities (supportive)
And unhealthy ones (toxic)
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Basically all of Reddit, Jan - May 2015266m posts96k forums (“subreddits”)
Most popular:• /r/AskReddit (21m posts)• /r/leagueoflegends (5m)• /r/funny (4m)• /r/pics (3m)• /r/nfl (3m)• /r/nba (3m)
Data details
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Median % of posts with because across subreddits with 50k+ posts (758 subreddits)
Top quartile
Bottom quartile
Distribution of “because” across subredits
5.44%
7.25%
3.95%
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/r/changemyview (21%)/r/DebateAChristian (19%)/r/PurplePillDebate (18%)/r/DebateReligion (17%)/r/AgainstGamerGate (17%)/r/truegaming (17%)/r/DebateAnAthiest (17%)/r/philosophy (16%)/r/raisedbynarcissists (16%)/r/PoliticalDiscussion (16%)/r/listentothis (15%)/r/relationship_advice (15%)/r/relationships (15%)/r/Anxiety (14%)/r/ADHD (14%)
Examples of most-because-y subreddits
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/r/podemos (0%)/r/newsokur (0%)/r/sweden (0%)/r/gonewild (1%)/r/randomsuperpowers (1%)/r/ACTrade (1%)/r/GlobalOffensiveTrade (1%)/r/millionairemakers (1%)/r/SVExchange (1%)/r/PercyJacksonRP (1%)/r/YamkuHighSchool (1%)/r/XMenRP (2%)/r/hardwareswap (2%)/r/rwbyRP (2%)/r/thebutton (2%)
Examples of the least because-y
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This presentation is helped out by some insights by Jana Thompson one of our NLP Engineers and Charissa Plattner, one of our summer interns
Co-conspirators!
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385k posts30k have “because” (7.81%)
Posts with “because” tend to score higher for “controversiality”
They are also significantly longer (p < 2.2e-16 by Wilcoxon rank sum test)
/r/conspiracy
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Counting "deleted" and "AutoModerator" as real users, then there are 32,024 different users who post in conspiracy from Jan-May 2015.
1,064 of them have 50 or more posts.
The median % of posts with because is 7.19% • Top quartile: 11.43%• Bottom quartile: 4.02%
A view of authors
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Those who pay decent rent are doing so because they've been living in a rent controlled area for a LONG time.• This is preceded by a paragraph all about rent prices• All Caps Evaluative
So, because it's minor at first that would possibly embolden them? You can't be serious...• So vs. oh, the importance of questions and rhetoric• Preposed because (given/new)
Slaves? Are we literally whipped bloody when we don't do as master says (or just because he wants to).
• Adversative: ends with, “Do you have any clue what slavery really is?”
Some examples from big-because users
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There are 384,839 posts in this time frame. They roll up to 222,818 "parent_id" threads.
For threads that have 50+ posts (there are only 144 of them), the median % of posts with "because" is 5.61%.• Top quartile: 8.14%• Bottom quartile: 3.33%
For threads that have 15-49 posts (1,181 of them), the median % of posts with "because" is 5.88%.• Top quartile: 10.53%• Bottom quartile: 0%
A view of threads
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• JFK (head autopsy paper wound jfk)• 9/11 buildings (building collapse steel fire wtc)• aliens (humans earth life evolution aliens)• 9/11 (9 11 bin laden attacks) • space (earth moon nasa gravity apollo)They avoid…• media (conspiracy media news government propaganda) • US politics (law vote obama federal president congress)• More JFK (don't kennedy)• moderation (reddit post comments mods banned)• family/harm (children school kids mother abuse)
Where do authors who like because go?
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The because-irrific authors use a median 901 characters per post
The least-because-y use 615 characters per post
Within because posts…
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Are because users just wordy?
Or is it that because users hang out in threads where there’s just a lot more because?
Answer: Basically some topics are just wordier than some others (see next two slides about length)
What is driving length?
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Length of posts by topic/author disposition (longest)
Everyone Prolific becausers Because avoiders
1 JFK (2089 char) 9/11 (2747 char) More JFK (2157 char)
2 9/11 (1834 char) JFK (2464 char) aliens (1321 char)
3 More JFK (1784 char) More JFK (2130 char) reality (1270 char)
4 9/11 buildings (1489 char)
9/11 buildings (1962 char)
9/11 (1113 char)
5 aliens (1313 char) aliens (1800 char) religion (917 char)
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Length of posts by topic/author disposition (shortest)
Everyone Prolific becausers Because avoiders
25 criticism (534 char) moderation (695 char) climate change (392 char)
24 moderation (564 char) criticism (744 char) moderation (439 char)
23 media (653 char) meta-conspiracy (816 char)
criticism (440 char)
22 meta-conspiracy (666 char)
media (900 char) race (459 char)
21 race (721 char) internet (913 char) food/health (489 char)
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7.8% of posts in /r/conspiracy have “because”
16,069 of the posts in /r/conspiracy have language around fraud (21.7%)
So we’d expect about 1,255 posts to have both “because” and fraud/etc.
Instead we find 3,491.
What about claims about fraud, illegality, bamboozlement, etc?
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Wrapping up
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1) Showing how computational linguistics solves business problems2) Identifying markers of fraud using language data
For company-internal fraud/compliance investigatorsFor government/regulatory/consumer advocacy
3) Detecting and using rationalization and reason-givingThe importance of emotionThe case of because
Your thoughts on next steps?
Reviewing where we’ve been
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There are links between rationalization and because usage that can help with applications of the fraud diamond/triangle
The different ways people use/don’t use because can help us understand the psychological state of fraudsters and the information of people who may be encountering it
On because
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Fraud and compliance in digital communications
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Early case assessment
Relevancy filtering
Risk Scoring
Key entitiesStrategic communications
Spam, NewslettersNear de-duplication
Fraud diamondSentimentPersonal communications
Investigation stage Models100% Data volume
30%
10%
< 1%
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Processing millions of SMS in 12 African languages
Intent of sender(i.e. report a problem,
ask a question or make a suggestion)
Categorization(i.e. orphans and
vulnerable children, violence against children, health,
nutrition)
Language detection
(i.e. English, Acholi, Karamojong, Luganda, Nkole, Swahili, Lango)
Location(i.e. village names)
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• Given-then-new information (result-then-cause in his small corpus, too)
• Given as what’s been said• Inferable, familiar, expected
• New as unfamiliar, unexpected, unpredictable• The rare times that because is initial, it acts as a guidepost for
information flow• Like however, anyway, for example, on the other hand• “A guidepost par excellence is ‘meanwhile, back at the rank’.”• People as orienting the information for upcoming clauses• A more general strategy of giving a frame
• Third case (That in itself was scary, cause I never fainted before) is sequential and meant to add to the first assertion
• An “afterthought”
Chafe (1984)
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Ordering is about functional and cognitive pressures (draws on Hawkins 1994, 2004):• Syntactic parsing• Discourse pragmatics• SemanticsResult-then-clause order violates iconicity of sequence, yet they are the most attested• “causal clauses are primarily used to back up a previous
statement that the hearer may not accept or may not find convincing” (Diessel 2006)
Diessel (2008)
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Because occurs when agreement is at-issue (Ford 1993)Instead of focusing on information flow, they focus on speaker interaction and see it as occurring where there is actual/incipient disagreement
Thus, conversation analysts find becauses offered by either speaker right before a dispreferred turn
In English, they are surrounded by pauses, hesitations, excuses, mitigations, indirectness, partial agreement, polarity reversals
Ford and Mori (1994)
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The real point of their paper is that there are two Japanese becauses, but the function differently:• datte: glossed as ‘no for the reason that’, is immediate and
clear—strong disagreement—it isn’t about getting information but about getting a justification
• kara: more like English, shifts towards alignment; also used if a reference is unclear, a term is unknown, or if the speaker is assuming something of the recipient that they don’t actually know
If you want to give someone a datte response in English, you have to use turn onset, stress, intensifiers, choice of evaluative language, directness of disagreeing, and non-verbal expressions
Ford and Mori (1994), cont’d
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John came back because he loved her.One event causes another
John loved her, because he came back.Illustrates the speaker’s reasoning, “epistemic”; English since, French puisque, German denn
What are you doing tonight, because there’s a good movie on.A “speech act”
Subjective relations are often derived from objective relations (see also Traugott 1995)
Sweetser (1990)
Tongan
si’i and si’a
Different determiners express sympathy to the DP they head (Hendrick, 2005)
Navajo
=go
Emotional evaluation in narrative (Mithun 2008)
Korean
Evidentials and psych predicates
Non-evidential sentences are more assertive/informational, evidential sentences about the speaker are more “expressive” and “spontaneous” (Chung 2010)
East Caucasian lgs
Case for emotion experiencers ≠ perception experiencers
Van den Berg (2005)
Thai thîi Complementizer for verbs of emotion/evaluation (Singhapreecha, 2010)
For strangers on the phone, because is used mostly for vices, holidays, money, travel, wars
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1.4%
Top 3 categories in Nigeria
Employment
U-report support
Health
9.69%
17.68%
39.44%
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Are becausers drawn to different topics more than others
O/E big becausers O/E because-avoiders
JFK 69 posts by big becausers in this topic / 56 posts expected
0 posts by because-avoiders in this topic / 13 posts expected
9/11 buildings 408 / 366 44 / 86
media 357 / 394 130 / 93
moderation 442 / 489 154 / 114
aliens 133 / 123 19 / 29
food/health 231 / 214 33 / 51
More JFK 38 / 41 13 / 10
internet 103 / 112 35 / 26
vaccines 263 / 247 43 / 59
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Basically the same list is top, except vaccines pop up a few spots and aliens drop down a few spots
• More JFK (don't kennedy)• JFK (head autopsy paper wound jfk)• 9/11 (9 11 bin laden attacks)• vaccines (vaccines children disease
autism polio)• 9/11 buildings (building collapse steel
fire wtc)
Let’s remove the authors who like because