Authentic versus fictitious online reviews: A textual analysis across luxury, budget, and mid-range hotels

Extant literature suggests that authentic and fictitious online reviews could be distinguished by leveraging on their textual characteristics. However, nuances in textual differences between authentic and fictitious reviews across different categories of hotels remain largely unknown. Therefore, this paper analyzes textual differences between authentic and fictitious reviews across three hotel categories, namely, luxury, budget, and mid-range. It leverages on four possible textual characteristics – comprehensibility, specificity, exaggeration, and negligence – that could offer clues to ascertain review authenticity. Using a dataset of 1800 reviews (900 authentic + 900 fictitious), the results suggest that differences between authentic and fictitious reviews are largely inconsistent across hotel categories. This generally points to the difficulties in ascertaining review authenticity, which in turn offer implications for both research and practice.

[1]  E. Ghiselli.  All or none versus graded response questionnaires. , 1939 .

[2]  Geoffrey Leech,et al.  Grammatical word class variation within the British National Corpus sampler , 2002 .

[3]  Ulrike Gretzel,et al.  Do Negative Experiences Always Lead to Dissatisfaction? - Testing Attribution Theory in the Context of Online Travel Reviews , 2010, ENTER.

[4]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[5]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[6]  K. Hyland,et al.  Writing Without Conviction? Hedging in Science Research Articles , 1996 .

[7]  Ling Liu,et al.  Manipulation of online reviews: An analysis of ratings, readability, and sentiments , 2012, Decis. Support Syst..

[8]  Hans C. Jessen,et al.  Applied Logistic Regression Analysis , 1996 .

[9]  Adrienne Y. Lee,et al.  Language of lies in prison: linguistic classification of prisoners' truthful and deceptive natural language , 2005 .

[10]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  A. Chua,et al.  A Linguistic Framework to Distinguish between Genuine and Deceptive Online Reviews , 2014 .

[12]  J. Dawes.  Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales , 2008 .

[13]  J. Burgoon,et al.  The Dynamic Nature of Deceptive Verbal Communication , 2006 .

[14]  Claire Cardie,et al.  Negative Deceptive Opinion Spam , 2013, NAACL.

[15]  Paolo Rosso,et al.  Detecting positive and negative deceptive opinions using PU-learning , 2015, Inf. Process. Manag..

[16]  Kate Hunt.  Gaming the system: Fake online reviews v. consumer law , 2015 .

[17]  Shirley Dowdy,et al.  Multiple Regression and Correlation , 2005 .

[18]  B. Pan,et al.  A retrospective view of electronic word-of-mouth in hospitality and tourism management , 2017 .

[19]  Diana Inkpen,et al.  A hierarchical approach to mood classification in blogs , 2011, Natural Language Engineering.

[20]  Rachel Greenstadt,et al.  Detecting Hoaxes, Frauds, and Deception in Writing Style Online , 2012, 2012 IEEE Symposium on Security and Privacy.

[21]  J. Burgoon,et al.  Interpersonal Deception Theory , 2015 .

[22]  Terry O'Sullivan,et al.  All Marketers Are Liars: The Power of Telling Authentic Stories in a Low-Trust World , 2006 .

[23]  Chih-Chen Lee,et al.  Features of Computer‐Mediated, Text‐Based Messages that Support Automatable, Linguistics‐Based Indicators for Deception Detection , 2009, J. Inf. Syst..

[24]  Anindya Ghose,et al.  Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets , 2008, Inf. Syst. Res..

[25]  Woo-Hyoung Lee,et al.  Bibliometrics for advancement R&D Planning : Detecting Emerging Trends in Scientific Literatures , 2009 .

[26]  Markus Zanker,et al.  Classification of Customer Reviews based on Sentiment Analysis , 2012, ENTER.

[27]  Dongsong Zhang,et al.  Following linguistic footprints: automatic deception detection in online communication , 2008, CACM.

[28]  Christopher G. Harris.  Detecting Deceptive Opinion Spam Using Human Computation , 2012, HCOMP@AAAI.

[29]  Dorit Ravid,et al.  Emergence of Linguistic Complexity in Later Language Development: Evidence from Expository Text Construction , 2005 .

[30]  Andrew M. Chan,et al.  Critical Success Factors and Customer Expectation in Budget Hotel Segment — A Case Study of China , 2009 .

[31]  J. Dawes.  Five Point vs. Eleven Point Scales: Does It Make A Difference To Data Characteristics ? , 2002 .

[32]  H. Raghav Rao,et al.  Community Intelligence and Social Media Services: A Rumor Theoretic Analysis of Tweets During Social Crises , 2013, MIS Q..

[33]  Ulrike Gretzel,et al.  What's in a Travel Review Title? , 2012, ENTER.

[34]  Alton Yeow-Kuan Chua,et al.  Predictors of High-Quality Answers , 2012, Online Inf. Rev..

[35]  J. Pennebaker,et al.  Lying Words: Predicting Deception from Linguistic Styles , 2003, Personality & social psychology bulletin.

[36]  Raffaele Perego,et al.  On planning sightseeing tours with TripBuilder , 2015, Inf. Process. Manag..

[37]  Nasser Ghasem-Aghaee,et al.  Exploiting reviewers’ comment histories for sentiment analysis , 2014, J. Inf. Sci..

[38]  Elena García Barriocanal,et al.  Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content , 2012, Electron. Commer. Res. Appl..

[39]  Fei Liu,et al.  Application of a clustering method on sentiment analysis , 2012, J. Inf. Sci..

[40]  Christian Maurer,et al.  Online Customer Reviews Used as Complaint Management Tool , 2011, ENTER.

[41]  James J. Lindsay,et al.  Cues to deception. , 2003, Psychological bulletin.

[42]  Tanmay De Sarkar.  Impact of online interactivity dimensions on library website quality , 2012 .

[43]  J. Chall,et al.  Readability revisited : the new Dale-Chall readability formula , 1995 .

[44]  M. Pasupathi.  Telling and the remembered self: Linguistic differences in memories for previously disclosed and previously undisclosed events , 2007, Memory.

[45]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[46]  C. Bjornsson.  Readability of Newspapers in 11 Languages. , 1983 .

[47]  Kyung Hyan Yoo,et al.  Comparison of Deceptive and Truthful Travel Reviews , 2009, ENTER.

[48]  Catheryn Khoo-Lattimore,et al.  Air the anger: investigating online complaints on luxury hotels , 2012 .

[49]  S. Jay Samuels,et al.  Readability: Its Past, Present, and Future , 1988 .

[50]  Jonathan Anderson.  Lix and Rix: Variations on a Little-Known Readability Index. , 1983 .

[51]  U. Gretzel,et al.  Influence of Star Rating and Ownership Structure on Brand Image of Mainland China Hotels , 2014 .

[52]  S. Brownlow,et al.  Differential Forms Linguistic Content of Various of Political Advertising , 2000 .

[53]  Julie Pallant,et al.  SPSS survival manual : a step by step guide to data analysis using SPSS for Windows , 2001, Behaviour Change.

[54]  Jeffrey T. Hancock,et al.  On Lying and Being Lied To: A Linguistic Analysis of Deception in Computer-Mediated Communication , 2007 .

[55]  Jay F. Nunamaker,et al.  A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication , 2004, J. Manag. Inf. Syst..

[56]  K. Klein,et al.  Word Use in Emotional Narratives about Failed Romantic Relationships and Subsequent Mental Health , 2005 .

[57]  Marcia K. Johnson,et al.  Reality Monitoring , 2005 .

[58]  Victoria Talwar,et al.  An analysis of childrens deceptive statements using Linguistic Inquiry and Word Count Software , 2009 .

[59]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

[60]  Jahna Otterbacher,et al.  Gender, writing and ranking in review forums: a case study of the IMDb , 2013, Knowledge and Information Systems.

[61]  John H. Gerdes,et al.  An integrative approach to assess qualitative and quantitative consumer feedback , 2008, Electron. Commer. Res..

[62]  P. Ekman,et al.  Nonverbal Leakage and Clues to Deception †. , 1969, Psychiatry.

[63]  B. Depaulo,et al.  Accuracy of Deception Judgments , 2006, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[64]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .

[65]  R. Bull,et al.  Detecting Deceit via Analysis of Verbal and Nonverbal Behavior , 2000 .

[66]  M. Zuckerman.  Verbal and nonverbal communication of deception , 1981 .

[67]  M. H. Licht,et al.  Multiple regression and correlation. , 1995 .

[68]  Uwe Matzat,et al.  Disciplinary differences in the use of internet discussion groups: differential communication needs or trust problems? , 2009, J. Inf. Sci..

[69]  Fred Bronner,et al.  Vacationers and eWOM: Who Posts, and Why, Where, and What? , 2011 .

[70]  Bing Liu,et al.  Opinion spam and analysis , 2008, WSDM '08.

[71]  Luis Alfonso Ureña López,et al.  Polarity classification for Spanish tweets using the COST corpus , 2015, J. Inf. Sci..

[72]  Qing Cao,et al.  Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach , 2011, Decis. Support Syst..

[73]  Nikolaos Korfiatis,et al.  The Influences of Negativity and Review Quality on the Helpfulness of Online Reviews , 2011, ICIS.

[74]  Jeffrey T. Hancock,et al.  Automated Linguistic Analysis of Deceptive and Truthful Synchronous Computer-Mediated Communication , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[75]  Michal Karpowicz,et al.  Computational approaches for mining user's opinions on the Web 2.0 , 2014, Inf. Process. Manag..

[76]  Naomie Salim,et al.  Detection of review spam: A survey , 2015, Expert Syst. Appl..

[77]  Rada Mihalcea,et al.  Towards multimodal deception detection -- step 1: building a collection of deceptive videos , 2012, ICMI '12.