2. Today’s Discussion Linguistic feature mining of 2 contrasting corpora: Text from Financial Statements Transcripts of 911 Homicide Calls TextVerbal communication transcribed to text Carefully written and edited over weeks to months Unrehearsed Formal: conforms to genre for financial communiqués Informal: includes slang

3. Financial Statement Fraud: Problem and Motivation Investors look for credibility, transparency, and clarity of financial documents to make investment decisions and to maintain confidence in companies Management’s Discussion and Analysis (MD&A) is among the sections of 10-Ks that is read most often Auditors need innovative ways to assess risk based on not only financial and nonfinancial measures but also financial statement texts

4. Deception Is Strategic (Buller and Burgoon, 1996) FOOTNOTE 16. RELATED PARTY TRANSACTIONS In 2000 and 1999, Enron entered into transactions with limited partnerships (the Related Party) whose general partner’s managing member is a senior officer of Enron. The limited partners of the Related Party are unrelated to Enron. Management believes that the terms of the transactions with the Related Party were reasonable compared to those which could have been negotiated with unrelated third parties…Subsequently, Enron sold a portion of its interest in the partnership through securitizations.” (Enron 2000)

5. Leakage Theory Applied to Fraudulent Financial Reporting (Ekman 1969) Managers engaging in fraud cannot completely match behavior exhibited when truthful – Cues leak out unintentionally – Language usage should leave clues to deception

6. Mining Linguistic Features for Detecting Obfuscation in Financial Reports Do MD&A sections of fraudulent 10-Ks have a higher level of obfuscation? Based on the research in deception detection and obfuscation, we can look for the following (among other cues) in fraudulent MD&As: More complex words More complex sentences More causation words More achievement words

7. Our Methodology Linguistic Extraction and Classification Tools Linguistic Cues for Deception Classified as Deceptive Classified as Not Deceptive 101 MD&As with fraud problems 101 MD&As with no fraud problems

8. Example of Results Greater in Fraudulent MD&As Rate of Three Syllable Words** Conjunctions** Causation Words** Achievement Words* ** = p <.05, * = p <.10

9. Application of Automated Linguistic Analysis to Transcripts of 911 Homicide Calls for Deception Detection Caller from Orange County, Florida Caller from Columbia, Missouri

10. 911 calls are a potentially rich source of verbal deception indicators  911 calls are unrehearsed, high-stakes communications Motivation: Identify if linguistic content of truthful vs. deceptive 911 calls differs 911 Calls: Problem & Motivation

11. Can automated linguistic analysis techniques accurately classify deceptive vs. truthful callers in transcripts of 911 homicide calls? Based on the research in deception detection, we can look for the following (among other cues) in deceptive 911 calls: Higher use of they Higher use of we More suppressed answers, using as few words as possible --- the opposite of obfuscation! – Negation – Assent than truthful callers. Question

12. Methodology Linguistic Extraction and Classification Tools Linguistic Cues for Deception Classified as Deceptive Classified as Not Deceptive Twenty-five 911 Calls Labeled as Deceptive Twenty-five 911 Calls Labeled as Truthful

13. Examples of Results Variable nameDirectionExample 1st person plural D>TWe don't know. 3rd person plural D>TYes, they said, they said if they heard anything they were going to my house. NegationD>TNo nothing, he's gone. AssentD>TOkay, they're here.

14.  Truthtellers:  Display more negative emotion (including emotion-filled swearing) and anxiety than deceivers.  Refer to singular others (she or he).  Use more numbers to ensure responders find address as quickly as possible or know phone number.  Use more generic names of locations, such as ‘apartment’ or ‘garage’ to give more accurate, helpful information to responders. Discussion: Truthtellers

15.  Deceivers:  ‘Distance’ themselves from what is said by referencing others in the 3 rd person (they).  Try to ‘share the blame’ by referring to self as plural (we) rather than as singular.  Use more negation and assent words because they are trying to subdue, constrain, or suppress answers/affect.  Tell the operator to ‘wait’ or ‘hold on’ if the operator is asking them to do something, such as CPR, that they are reluctant to do. Discussion: Deceivers

16. Questions?