Leveraging Financial Social Media Data for Corporate Fraud Detection

References

• Abbasi, A.; Albrecht, C.; Vance, A.; and Hansen, J. Metafraud: A meta-learning framework for detecting financial fraud. MIS Quarterly, 36, 4 (2012), 1293–1327.
   Web of Science ®Google Scholar
• Abbasi, A.; and Chen, H. CyberGate: A design framework and system for text analysis of computer-mediated communication. MIS Quarterly, 32, 4 (2008), 811–837.
   Web of Science ®Google Scholar
• Abrahams, A.S.; Fan, W.; Wang, G.A.; Zhang, Z.; and Jiao, J. An integrated text analytic framework for product defect discovery. Production and Operations Management, 24, 6 (2015), 975–990.
   Web of Science ®Google Scholar
• Antweiler, W.; and Frank, M.Z. Is all that talk just noise? The information content of Internet stock message boards. Journal of Finance, 59, 3 (2004), 1259–1294.
   Web of Science ®Google Scholar
• Argamon, S.; Whitelaw, C.; Chase, P.; Hota, S.R.; Garg, N.; and Levitan, S. Stylistic text classification using functional lexical features. Journal of the American Society for Information Science and Technology, 58, 6 (2007), 802–822.
   Web of Science ®Google Scholar
• Beneish, M.D. Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, 16, 3 (1997), 271–309.
   Google Scholar
• Beneish, M.D. The detection of earnings manipulation. Financial Analysts Journal, 55, 5 (1999), 24–36.
   Google Scholar
• Blei, D.M.; Ng, A.Y.; Jordan, M.I.; and Lafferty, J. Latent dirichlet allocation. Journal of Machine Learning Research, 3, 4/5 (2003), 993–1022.
   Web of Science ®Google Scholar
• Borgatti, S.P.; and Foster, P.C. The network paradigm in organizational research: A review and typology. Journal of Management, 29, 6 (2003), 991–1013.
   Web of Science ®Google Scholar
• Brazel, J.F.; Jones, K.L.; and Zimbelman, M.F. Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, 47, 5 (2009), 1135–1166.
   Web of Science ®Google Scholar
• Brown, N.C.; Crowley, R.M.; and Elliott, W.B. What are you saying? Using topic to detect financial misreporting. In P. Mohanram and L. Yang (eds.), Proceedings of the 27th Annual Conference on Financial Economics and Accounting Paper. Toronto, 2016, pp. 1–67.
   Google Scholar
• Brunnermeier, M.K. Information leakage and market efficiency. Review of Financial Studies, 18, 2 (2005), 417–457.
   Web of Science ®Google Scholar
• Buller, D.B.; and Burgoon, J.K. Interpersonal deception theory. Communication Theory, 6, 3 (1996), 203–242.
   Web of Science ®Google Scholar
• Castillo, C.; Mendoza, M.; and Poblete, B. Information credibility on twitter. In S. Sadagopan, K. Ramamritham, A. Kumar, and M. P. Ravindra (eds.), Proceedings of the 20th International Conference on World Wide Web. Hyderabad, 2011, pp. 675–684.
   Google Scholar
• Cecchini, M.; Aytug, H.; Koehler, G.J.; and Pathak, P. Detecting management fraud in public companies. Management Science, 56, 7 (2010), 1146–1160.
   Web of Science ®Google Scholar
• Cecchini, M.; Aytug, H.; Koehler, G.J.; and Pathak, P. Making words work: Using financial text as a predictor of financial events. Decision Support Systems, 50, 1 (2010), 164–175.
   Web of Science ®Google Scholar
• Chen, H.; De, P.; Hu, Y.; and Hwang, B.-H. Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies, 27, 5 (2014), 1367–1403.
   Web of Science ®Google Scholar
• Dechow, P.M. Accounting earnings and cash flows as measures of firm performance: The role of accounting accruals. Journal of Accounting and Economics, 18, 1 (1994), 3–42.
   Web of Science ®Google Scholar
• Dechow, P.M.; Ge, W.; Larson, C.R.; and Sloan, R.G. Predicting material accounting misstatements. Contemporary Accounting Research, 28, 1 (2011), 17–82.
   Web of Science ®Google Scholar
• Dong, W.; Liao, S.; Fang, B.; Cheng, X.; Chen, Z.; and Fan, W. The detection of fraudulent financial statements: An integrated language model. In K. Siau, Q. Li, and X. Guo (eds.), Proceedings of the 18th Pacific Asia Conference on Information Systems. Chengdu, 2014, pp. 1–15.
   Google Scholar
• Dyck, A.; Morse, A.; and Zingales; L. Who blows the whistle on corporate fraud?. The Journal of Finance, 65, 6 (2010), 2213–2253.
   Web of Science ®Google Scholar
• Elliott, R. K.; and Willingham, J. J. Management fraud: Detection and deterrence. New York: Petrocelli Books, 1980.
   Google Scholar
• Fanning, K.M.; and Cogger, K.O. Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance and Management, 7, 1 (1998), 21–41.
   Google Scholar
• Federal Bureau of Investigation (FBI). Financial crimes report to the public (Fiscal years 2010–2011). 2012. https://www.fbi.gov/stats-services/publications/financial-crimes-report-2010-2011.
   Google Scholar
• Fu, T.; Abbasi, A.; and Chen, H. A hybrid approach to Web forum interactional coherence analysis. Journal of the American Society for Information Science and Technology, 59, 8 (2008), 1195–1209.
   Web of Science ®Google Scholar
• Glancy, F.H.; and Yadav, S.B. A computational model for financial reporting fraud detection. Decision Support Systems, 50, 3 (2011), 595–601.
   Web of Science ®Google Scholar
• Goel, S.; and Gangolly, J. Beyond the numbers: Mining the annual reports for hidden cues indicative of financial statement fraud. Intelligent Systems in Accounting, Finance and Management, 19, 2 (2012), 75–89.
   Google Scholar
• Goel, S.; Gangolly, J.; Faerman, S.R.; and Uzuner, O. Can linguistic predictors detect fraudulent financial filings? Journal of Emerging Technologies in Accounting, 7, 1 (2010), 25–46.
   Google Scholar
• Halliday, M.; Matthiessen, C.M.; and Matthiessen, C. An Introduction to Functional Grammar. London: Hodder Education, 2004.
   Google Scholar
• Hobson, J.L.; Mayew, W.J.; and Venkatachalam, M. Analyzing speech to detect financial misreporting. Journal of Accounting Research, 50, 2 (2012), 349–392.
   Web of Science ®Google Scholar
• Humpherys, S.L.; Moffitt, K.C.; Burns, M.B.; Burgoon, J.K.; and Felix, W.F. Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50, 3 (2011), 585–594.
   Web of Science ®Google Scholar
• Hyland, K. Genre and Second Language Writing. Ann Arbor: University of Michigan Press, 2004.
   Google Scholar
• Joachims, T. Training linear SVMs in linear time. In L. Ungar (ed.), Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia, 2006, pp. 217–226.
   Google Scholar
• Kaminski, K.A.; Wetzel, T.S.; and Guan, L. Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19, 1 (2004), 15–28.
   Google Scholar
• Kothari, S.; Li, X.; and Short, J.E. The effect of disclosures by management, analysts, and business press on cost of capital, return volatility, and analyst forecasts: A study using content analysis. Accounting Review, 84, 5 (2009), 1639–1670.
   Google Scholar
• Larcker, D.F.; and Zakolyukina, A.A. Detecting deceptive discussions in conference calls. Journal of Accounting Research, 50, 2 (2012), 495–540.
   Web of Science ®Google Scholar
• Lazarus, R. S. Thoughts on the relations between emotion and cognition. American Psychologist, 37, 9 (1982), 1019–1024.
   Web of Science ®Google Scholar
• Li, F. Textual analysis of corporate disclosures: A survey of the literature. Journal of Accounting Literature, 29 (2010), 143–165.
   Google Scholar
• Li, Q.; Liu, X.; Fang, R.; Nourbakhsh, A.; and Shah, S. User behaviors in newsworthy rumors: A case study of Twitter. In K. P. Gummadi and M. Strohmaier (eds.), Proceedings of the 10th International AAAI Conference on Web and Social Media. Cologne, 2016, pp. 627–630.
   Google Scholar
• Liou, F.-M. Fraudulent financial reporting detection and business failure prediction models: A comparison. Managerial Auditing Journal, 23, 7 (2008), 650–662.
   Google Scholar
• Loughran, T. I. M.; and McDonald, B. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66, 1 (2011), 35–65.
   Web of Science ®Google Scholar
• Luo, X.; Zhang, J.; and Duan, W. Social media and firm equity value. Information Systems Research, 24, 1 (2013), 146–163.
   Web of Science ®Google Scholar
• Manning, C.D.; Raghavan, P.; and Schütze, H. Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008.
   Google Scholar
• Manning, C.D.; Surdeanu, M.; Bauer, J.; Finkel, J.; Bethard, S.J., and McClosky, D. The Stanford CoreNLP natural language processing toolkit. In K. Bontcheva and Z. Jingbo (eds.), Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics System Demonstrations. Baltimore, 2014, pp. 55–60.
   Google Scholar
• Merkl-Davies, D. M., and Brennan, N. Discretionary disclosure strategies in corporate narratives: incremental information or impression management?. Journal of Accounting Literature, 26, (2007), 116–196.
   Google Scholar
• Miller, G.A.; Beckwith, R.; Fellbaum, C.; Gross, D.; and Miller, K.J. Introduction to WordNet: An on-line lexical database. International Journal of Lexicography, 3, 4 (1990), 235–244.
   Google Scholar
• Moffitt, K.; Felix, W.; and Burgoon, J.K. Using lexical bundles to discriminate between fraudulent and non-fraudulent financial reports. In C. Ferran (ed.), Proceedings of the SIG-ASYS Pre-ICIS 2010 workshop. St. Louis, MO, 2010, pp. 1–22.
   Google Scholar
• Newman, M.L.; Pennebaker, J.W.; Berry, D.S.; and Richards, J.M. Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin, 29, 5 (2003), 665–675.
   PubMed Web of Science ®Google Scholar
• Ngai, E.; Hu, Y.; Wong, Y.; Chen, Y.; and Sun, X. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50, 3 (2011), 559–569.
   Web of Science ®Google Scholar
• Oh, O.; Agrawal, M.; and Rao, H.R. Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly, 37, 2 (2013), 407–426.
   Web of Science ®Google Scholar
• Pak, J.; and Zhou, L. Social structural behavior of deception in computer-mediated communication. Decision Support Systems, 63 (2014), 95–103.
   Web of Science ®Google Scholar
• Pang, B.; and Lee, L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1–2 (2008), 1–135.
   Google Scholar
• Pennebaker, J.W.; Francis, M.E.; and Booth, R.J. Linguistic Inquiry and Word Count (LIWC): A Computerized Text Analysis Program. Austin: LIWC.net, 2001.
   Google Scholar
• Phua, C.; Lee, V.; Smith, K.; and Gayler, R. A comprehensive survey of data mining-based fraud detection research. 2010. https://arxiv.org/abs/1009.6119.
   Google Scholar
• Purda, L.; and Skillicorn, D. Accounting variables, deception, and a bag of words: Assessing the tools of fraud detection. Contemporary Accounting Research, 32, 3 (2014), 1193–1223.
   Web of Science ®Google Scholar
• Qazvinian, V.; Rosengren, E.; Radev, D.R.; and Mei, Q. Rumor has it: Identifying misinformation in microblogs. In P. Merlo (ed.), Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh, 2011, pp. 1589–1599.
   Google Scholar
• Rai, A. Editor’s comments: Avoiding Type III errors: Formulating IS research problems that matter. MIS Quarterly, 41, 2 (2017), iii–vii.
   Web of Science ®Google Scholar
• Rajaraman, A.; Ullman, J.D.; Rajaraman, A.; and Ullman, J.D. Data Mining of Massive Datasets. Cambridge: Cambridge University Press, 2011.
   Google Scholar
• Rosemann, M., and Vessey, I. Toward improving the relevance of information systems research to practice: The role of applicability checks. MIS Quarterly, 32, 1 (2008), 1–22.
   Web of Science ®Google Scholar
• Rutherford, B.A. Genre analysis of corporate annual report narratives a corpus linguistics–based approach. Journal of Business Communication, 42, 4 (2005), 349–378.
   Google Scholar
• Sack, W. Conversation map: An interface for very large-scale conversations. Journal of Management Information Systems, 17, 3 (2000), 73–92.
   Web of Science ®Google Scholar
• Schumaker, R.P.; Zhang, Y.; Huang, C.-N.; and Chen, H. Evaluating sentiment in financial news articles. Decision Support Systems, 53, 3 (2012), 458–464.
   Web of Science ®Google Scholar
• Siering, M.; Koch, J.-A.; and Deokar, A. V. Detecting fraudulent behavior on crowdfunding platforms: The role of linguistic and content-based cues in static and dynamic contexts. Journal of Management Information Systems, 33, 2 (2016), 421–455.
   Web of Science ®Google Scholar
• Summers, S.L.; and Sweeney, J.T. Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73, 1 (1998), 131–146.
   Web of Science ®Google Scholar
• Teo, P. Racism in the news: A critical discourse analysis of news reporting in two Australian newspapers. Discourse and Society, 11, 1 (2000), 7–49.
   Web of Science ®Google Scholar
• Williamson, S.; Wang, C.; Heller, K.A., and Blei, D.M. The IBP compound Dirichlet process and its application to focused topic modeling. In S. Wrobel (ed.), Proceedings of the 27th International Conference on Machine Learning. Haifa, 2010, pp. 1151–1158.
   Google Scholar
• Yang, F.; Liu, Y.; Yu, X.; and Yang, M. Automatic detection of rumor on Sina Weibo. In Y. Ding, J. Han, J. Tang, and P. Yu (eds.), Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. Beijing, 2012, pp. 1–7.
   Google Scholar
• Yang, Y.; and Pedersen, J.O. A comparative study on feature selection in text categorization. In D. H. Fisher (ed.) Proceedings of the 14th International Conference on Machine Learning. Nashville, TN, 1997, pp. 412–420.
   Google Scholar
• Yue, D.; Wu, X.; Wang, Y.; Li, Y.; and Chu, C.-H. A review of data mining-based financial fraud detection research. In L. Cuthbert, W. Huang, and C. Rubenstein (eds.),Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing. Shanghai, 2007, pp. 5519–5522.
   Google Scholar
• Zhang, D.; Zhou, L.; Kehoe, J.L.; and Kilic, I.Y. What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. Journal of Management Information Systems, 33, 2 (2016), 456–481.
   Web of Science ®Google Scholar