Identification of fraudulent financial statements using linguistic credibility analysis

By Sean L. Humpherys, Kevin C. Moffitt, Mary B. Burns, Judee K. Burgoon, William F. Felix. Decision Support Systems, 2011

327 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/296db7217a811e8c87e96767deda2a8aff8250a2

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Citations in the corpus

Intelligent financial fraud detection: A comprehensive review (2016, unknown): “Measuring factors such as expression, complexity, and specificity within the text can be used to identify subtle differences between samples [26].”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, unknown): “They offer valuable information to auditors because past research has revealed that auditors often have little real experience of fraud (Humpherys, Moffitt, Burns, Burgoon, & Felix, 2011).”
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research (2016, methodology): “Humpherys et al. (2011) used NLP output, processed by Naïve Bayes and C4.5 classifier algorithms, to classify fraudulent versus non-fraudulent management discussion and analyses (MD&As), attaining 67.3% accuracy.”
The role of internet-related technologies in shaping the work of accountants: New directions for accounting research (2019, methodology): “…studies have used text-mining techniques to analyse company disclosures and conference call transcripts to estimate the probability of misstatement (Humpherys, Moffitt, Burns, Burgoon, and Felix, 2011; Larcker and Zakolyukina, 2012), but other opportunities arise as more data becomes available.”
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures (2014, methodology): “Taking risk disclosure text analysis for example, Kothari et al. (2009) use dictionary methods to calculate the number of positives and negatives in each disclosure text, for each of the six business categories defined, by business quarter, by source.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “…[Li et al. 2014; Mukherjee et al. 2013b; Ott et al. 2011; Popoola 2018; Shojaee et al. 2013; Zhang et al. 2016], statements [Fuller et al. 2009; Humpherys et al. 2011], essays and short text [Afroz et al. 2012; Braud and Søgaard 2017; Pérez-Rosas and Mihalcea 2014, 2015], images and videos…”
Linguistic Obfuscation in Fraudulent Science (2016, background): “What is the mechanism that leads to obfuscation in science writing when researchers publish fake data? The obfuscation literature suggests that fraudulent financial companies dissimulate their reports to make them more costly to analyze (Humpherys et al., 2011).”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “, instant messaging and online chat) [17, 18, 25, 68] have provided preliminary evidence for the feasibility and importance of discovering nonverbal behaviors from online communication and incorporating them into online deception detection.”