Understanding Nigerian e-mail scams: A computational content analysis approach

ABSTRACT e-mail scams have been around for a long time, and they are still remarkably successful. Understanding these scams can help combat fraud by raising public awareness and improving anti-fraud technologies. We conducted an in-depth analysis of e-mail contents with a mixed-method approach using machine-learning-based content analysis and qualitative analysis. We identified five stories (i.e., topics) scammers used in their e-mails. Further, we explored similarities between messages from the same scammers. Our findings suggest some scammers resend a message with the same story as before but with highly textual differences. We argue this might be a deceptive maneuver to avoid spam blockers.

[1]  Arjun P. Ghatule,et al.  Crop Cultivation Intelligent Decision Support Systems Using Fuzzy Logic , 2020 .

[2]  Jalindar Gandal,et al.  A Study Of Advance Fee Fraud Detection Using Data Mining And Machine Learning Technique , 2020 .

[3]  E. Creamer.  Striving for Methodological Integrity in Mixed Methods Research: The Difference Between Mixed Methods and Mixed‐Up Methods , 2018, Journal of Engineering Education.

[4]  T. Rich.  You can trust me: a multimethod analysis of the Nigerian email scam , 2018 .

[5]  Monica T. Whitty,et al.  Do You Love Me? Psychological Characteristics of Romance Scam Victims , 2017, Cyberpsychology Behav. Soc. Netw..

[6]  E. Creamer.  A Primer About Mixed Methods Research in an Educational Context , 2016 .

[7]  Karina Korpela,et al.  Improving Cyber Security Awareness and Training Programs with Data Analytics , 2015, Inf. Secur. J. A Glob. Perspect..

[8]  Patrice Bellot,et al.  Accurate and effective latent concept modeling for ad hoc information retrieval , 2014, Document Numérique.

[9]  J. Froyd,et al.  Systematic Literature Reviews in Engineering Education and Other Developing Interdisciplinary Fields , 2014 .

[10]  Dan Siroker,et al.  A/B Testing: The Most Powerful Way to Turn Clicks Into Customers , 2013 .

[11]  Aurélien Francillon,et al.  Inside the scam jungle: a closer look at 419 scam email operations , 2013, 2013 IEEE Security and Privacy Workshops.

[12]  Cormac Herley,et al.  Why do Nigerian Scammers Say They are From Nigeria? , 2012, WEIS.

[13]  David B. Dunson,et al.  Probabilistic topic models , 2011, KDD '11 Tutorials.

[14]  Jiwoo Park,et al.  ‘I’m Sister Maria. Please help me’: A lexical study of 4-1-9 international advance fee fraud email communications , 2012 .

[15]  Hosseini Ali,et al.  A Critical Analysis of Financial Fraud Spam in English in Terms of Persuasive Strategies: Personalization, Presupposition, and Lexical Choices , 2011 .

[16]  O. Tade,et al.  Social Organization of Internet Fraud among University Undergraduates in Nigeria , 2011 .

[17]  K. Hornik,et al.  topicmodels: An R Package for Fitting Topic Models , 2011 .

[18]  Jude Oboh,et al.  Nigerian Advance Fee Fraud in Transnational Perspective , 2010 .

[19]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[20]  Sheng Tang,et al.  A density-based method for adaptive LDA model selection , 2009, Neurocomputing.

[21]  Andrew D Smith.  NIGERIAN SCAM E-MAILS AND THE CHARMS OF CAPITAL , 2009 .

[22]  Derek G. Ross.  ARS Dictaminis Perverted: The Personal Solicitation E-Mail as a Genre , 2009 .

[23]  Joshua Chang,et al.  An analysis of advance fee fraud on the internet , 2008 .

[24]  N. Denzin,et al.  The SAGE handbook of qualitative research , 2005 .

[25]  Wendy L. Cukier,et al.  Genre, Narrative and the "Nigerian Letter" in Electronic Mail , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[26]  Harvey Glickman,et al.  The Nigerian “419” Advance Fee Scams: Prank or Peril? , 2005 .

[27]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[29]  Valerie J. Caracelli,et al.  Toward a Conceptual Framework for Mixed-Method Evaluation Designs , 1989 .

[30]  Steve Mansfield-Devine,et al.  Bad behaviour: exploiting human weaknesses , 2017 .

[31]  Ron Kohavi,et al.  Online Controlled Experiments and A/B Testing , 2017, Encyclopedia of Machine Learning and Data Mining.

[32]  Kenneth Benoit,et al.  quanteda: Quantitative Analysis of Textual Data (R package) , 2015 .

[33]  W. Underwood.  What Can Topic models of PMLA Teach Us About the History of Literary Scholarship , 2012 .

[34]  A. Adogame.  The 419 Code as Business Unusual: Youth and the Unfolding of the Advance Fee Fraud Online Discourse , 2009 .

[35]  D. C. Graves,et al.  A Qualitative Analysis of Advance Fee Fraud E-mail Schemes , 2007 .

[36]  Edoardo M. Airoldi,et al.  Technologies to Defeat Fraudulent Schemes Related to Email Requests , 2005, AAAI Spring Symposium: AI Technologies for Homeland Security.

[37]  M. D. C. Ruiz de Villa,et al.  [Repeated measures analysis]. , 2004, Medicina clinica.

[38]  J. C. Oates.  Do You Love Me , 2004 .

[39]  I CASTILLO ODENA,et al.  [Social organization]. , 1956, Prensa medica argentina.

[40]  Bilge Mutlu,et al.  Qualitative Analysis , 1928, Nature.

[41]  S. Niwattanakul,et al.  Using of Jaccard Coefficient for Keywords Similarity , 2022 .