Proposal / Submission Type
Peer Reviewed Paper
Location
Henderson Welcome Center
Start Date
16-5-2017 11:00 AM
Abstract
Glancy and Yadav (2010) developed a computational fraud detection model (CFDM) that successfully detected financial reporting fraud in the text of the management’s discussion and analysis (MDA) portion of annual filings with the United States Securities and Exchange Commission (SEC). This work extends the use of the CFDM to additional genres, demonstrates the generalizability of the CFDM and the use of text mining for quantitatively detecting deception in asynchronous text. It also demonstrates that writers committing fraud use words differently from truth tellers.
Scholarly Commons Citation
Glancy, Fletcher, "Detecting Deception in Asynchronous Text" (2017). Annual ADFSL Conference on Digital Forensics, Security and Law. 2.
https://commons.erau.edu/adfsl/2017/papers/2
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Included in
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Detecting Deception in Asynchronous Text
Henderson Welcome Center
Glancy and Yadav (2010) developed a computational fraud detection model (CFDM) that successfully detected financial reporting fraud in the text of the management’s discussion and analysis (MDA) portion of annual filings with the United States Securities and Exchange Commission (SEC). This work extends the use of the CFDM to additional genres, demonstrates the generalizability of the CFDM and the use of text mining for quantitatively detecting deception in asynchronous text. It also demonstrates that writers committing fraud use words differently from truth tellers.
Comments
View the agenda session- Morning Session 3- File System Forensics