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.
Martínez-CámaraEMartín-ValdiviaMTUreña-LópezLAMitkovR.Polarity classification for Spanish tweets using the COST corpus. Journal of Information Science2015; 41(3): 263–272.
2.
BrilhanteIRMacedoJANardiniFMPeregoRRensoC.On planning sightseeing tours with TripBuilder. Information Processing & Management2015; 51(2): 1–15.
3.
LiGLiuF.Application of a clustering method on sentiment analysis. Journal of Information Science2012; 38(2): 127–139.
4.
LitvinSWGoldsmithREPanB.Electronic word-of-mouth in hospitality and tourism management. Tourism Management2008; 29(3): 458–468.
5.
BronnerFde HoogR.Vacationers and eWOM: Who posts, and why, where, and what?. Journal of Travel Research2010; 50(1): 15–26.
6.
OttMChoiYCardieCHancockJT. Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Palo Alto, CA: AAAI, 2011, pp. 309–319.
7.
FusilierDHMontes-y-GómezMRossoPCabreraRG.Detecting positive and negative deceptive opinions using PU-learning. Information Processing & Management2015; 51(4): 433–443.
8.
YooKHGretzelU.Comparison of deceptive and truthful travel reviews. In: HöpkenWGretzelULawR (eds). Information and Communication Technologies in Tourism. Vienna: Springer, 2009, pp. 37–47.
9.
JindalNLiuB.Opinion spam and analysis. In: Proceedings of the International Conference on Web Search and Data Mining. New York: ACM, 2008, pp. 219–230.
HeydariAali TavakoliMSalimNHeydariZ.Detection of review spam: A survey. Expert Systems with Applications2015; 42(7): 3634–3642.
12.
VrijAEdwardKRobertsKPBullR.Detecting deceit via analysis of verbal and nonverbal behavior. Journal of Nonverbal Behavior2000; 24(4): 239–263.
13.
BondCFDePauloBM.Accuracy of deception judgments. Personality and Social Psychology Review2006; 10(3): 214–234.
14.
BanerjeeSChuaAYK. A linguistic framework to distinguish between genuine and deceptive online reviews. In: Proceedings of the International Conference on Internet Computing and Web Services. Hong Kong: IAENG, 2014, pp. 501–506.
15.
GhoseAIpeirotisPG.Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering2011; 23(10): 1498–1512.
16.
TausczikYRPennebakerJW.The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology2010; 29(1): 24–54.
17.
EkizEKhoo-LattimoreCMemarzadehF.Air the anger: Investigating online complaints on luxury hotels. Journal of Hospitality and Tourism Technology2012; 3(2): 96–106.
18.
HuaWChanAMaoZ.Critical success factors and customer expectation in budget hotel segment—A case study of China. Journal of Quality Assurance in Hospitality & Tourism2009; 10(1): 59–74.
19.
JiangJGretzelULawR. Influence of star rating and ownership structure on brand image of Mainland China hotels. Journal of China Tourism Research2014; 10(1): 69–94.
20.
ZhouLBurgoonJKTwitchellDPQinTNunamakerJFJr. A comparison of classification methods for predicting deception in computer-mediated communication. Journal of Management Information Systems2004; 20(4): 139–165.
21.
DePauloBMLindsayJJMaloneBEMuhlenbruckLCharltonKCooperH.Cues to deception. Psychological Bulletin2003; 129(1): 74–118.
22.
HarrisCG. Detecting deceptive opinion spam using human computation. In: Proceedings for the Workshops on Artificial Intelligence. Palo Alto, CA: AAAI, 2012, pp. 87–93.
23.
AfrozSBrennanMGreenstadtR.Detecting hoaxes, frauds, and deception in writing style online. In: Proceedings of the Symposium on Security and Privacy. New York: IEEE, 2012, pp. 461–475.
24.
MihalceaRBurzoM. Towards multimodal deception detection - step 1: Building a collection of deceptive videos. In: Proceedings of the International Conference on Multimodal Interaction. New York: ACM, 2012, pp. 189–192.
25.
NewmanMLPennebakerJWBerryDSRichardsJM.Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin2003; 29(5): 665–675.
26.
BurgoonJKQinT.The dynamic nature of deceptive verbal communication. Journal of Language and Social Psychology2006; 25(1): 76–96.
27.
ZakalukBLSamuelsSJ.Readability: Its past, present and future. Newark, NJ: International Reading Association, 1988.
28.
ChallJSDaleE.Readability revisited: The new Dale-Chall readability formula. Cambridge: Brookline Books, 1995.
29.
CaoQDuanWGanQ.Exploring determinants of voting for the ‘helpfulness’ of online user reviews: A text mining approach. Decision Support Systems2011; 50(2): 511–521.
30.
HancockJTCurryLEGoorhaSWoodworthM.On lying and being lied to: A linguistic analysis of deception in computer-mediated communication. Discourse Processes2007; 45(1): 1–23.
31.
RaysonPWilsonALeechG.Grammatical word class variation within the British National Corpus sampler. Language and Computers2001; 36(1): 295–306.
32.
GodinS.All marketers are liars: The power of telling authentic stories in a low trust world. New York: Penguin, 2005.
33.
HancockJTCurryLGoorhaSWoodworthM. Automated linguistic analysis of deceptive and truthful synchronous computer-mediated communication. In: Proceedings of the Hawaii International Conference on System Sciences. New York: IEEE, 2005, pp. 1–10.
34.
RavidD.Emergence of linguistic complexity in later language development: Evidence from expository text construction. In: RavidDShyldkrotH (eds). Perspectives on Language and Language Development. Dordrecht: Kluwer Academic, 2005, pp. 337–355.
35.
HuNBoseIKohNSLiuL.Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems2012; 52(3): 674–684.
36.
MaurerCSchaichS.Online customer reviews used as complaint management tool. In: LawRFuchsMRicciF (eds). Information and Communication Technologies in Tourism. Vienna: Springer, 2011, pp. 499–511.
37.
GunschMABrownlowSHaynesSEMabeZ.Differential linguistic content of various forms of political advertising. Journal of Broadcasting & Electronic Media2000; 44(1): 27–42.
38.
EkmanPFriesenWV.Nonverbal leakage and clues to deception. Psychiatry: Interpersonal and Biological Processes1969; 32(1): 88–106.
39.
ZuckermanMDePauloBMRosenthalR. Verbal and nonverbal communication of deception. In: BerkowitzL (ed.). Advances in Experimental Social Psychology (Vol. 14). New York: Academic Press, 1981, pp. 1–59.
40.
HylandK.Writing without conviction? Hedging in science research articles. Applied Linguistics1996; 17(4): 433–454.
41.
AscaniisSDGretzelU.What’s in a travel review title? In: FuchsMRicciFCantoniL (eds). Information and Communication Technologies in Tourism. Vienna: Springer, 2012, pp. 494–505.
42.
GräbnerDZankerMFliedlGFuchsM.Classification of customer reviews based on sentiment analysis. In: FuchsMRicciFCantoniL (eds). Information and Communication Technologies in Tourism. Vienna: Springer, 2012, pp. 460–470.
43.
JiangJGretzelULawR. Do negative experiences always lead to dissatisfaction? - Testing attribution theory in the context of online travel reviews. In: GretzelULawRFuchsM (eds). Information and Communication Technologies in Tourism. Vienna: Springer, 2010, pp. 297–308.
44.
BasiriMEGhasem-AghaeeNNaghsh-NilchiAR.Exploiting reviewers’ comment histories for sentiment analysis. Journal of Information Science2014; 40(3): 313–328.
45.
GerdesJJrStringamBBBrookshireRG.An integrative approach to assess qualitative and quantitative consumer feedback. Electronic Commerce Research2008; 8(4): 217–234.
46.
DawesJ.Do data characteristics change according to the number of scale points used? An experiment using 5 point, 7 point and 10 point scales. International Journal of Market Research, http://ssrn.com/abstract=2013613 (2008, accessed February 2013).
47.
GhiselliEE.All or none versus graded response questionnaires. Journal of Applied Psychology1939; 23(3): 405–415.
48.
DawesJ.Five point vs eleven point scales: Does it make a difference to data characteristics?Australasian Journal of Market Research2002; 10(1): 39–47.
49.
GretzelUYooKHPurifoyM.Online travel reviews study: Role and impact of online travel reviews. College Station, TX: A&M University, Laboratory for Intelligent Systems in Tourism, 2007.
50.
AndersonJ.LIX and RIX: Variations on a little-known readability index. Journal of Reading1983; 26: 490–496.
51.
BjornssonCH.Readability of newspapers in 11 languages. Reading Research Quarterly1983; 18(4): 480–497.
52.
KorfiatisNGarcía-BariocanalESánchez-AlonsoS. Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications2012; 11(3): 205–217.
53.
BondGDLeeAY.Language of lies in prison: Linguistic classification of prisoners’ truthful and deceptive natural language. Applied Cognitive Psychology2005; 19(3): 313–329.
54.
ZhouLZhangD.Following linguistic footprints: Automatic deception detection in online communication. Communications of the ACM2008; 51(9): 119–122.
55.
OttMCardieCHancockJT.Negative deceptive opinion spam. In: North American Chapter of the Association for Computational Linguistics. Philadelphia, PA: ACL, 2013, pp. 497–501.
56.
KeshtkarFInkpenD.A hierarchical approach to mood classification in blogs. Natural Language Engineering2012; 18(1): 61–81.
57.
TsurODavidovDRappoportA. A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In: Proceedings of the International Conference on Weblogs and Social Media. Palo Alto, CA: AAAI, 2010, pp. 162–169.
58.
PasupathiM.Telling and the remembered self: Linguistic differences in memories for previously disclosed and previously undisclosed events. Memory2007; 15(3): 258–270.
59.
BoalsAKleinK.Word use in emotional narratives about failed romantic relationships and subsequent mental health. Journal of Language and Social Psychology2005; 24(3): 252–268.
60.
LeeCCWelkerRBOdomMD.Features of computer-mediated, text-based messages that support automatable, linguistics-based indicators for deception detection. Journal of Information Systems2009; 23(1): 19–24.
61.
PennebakerJWBoothRJFrancisME.Linguistic Inquiry and Word Count [Software]. Austin, TX: LIWC.net, 2007.
62.
KleinDManningCD.Accurate unlexicalized parsing. In: Proceedings of the Annual Meeting on Association for Computational Linguistics. Philadelphia, PA: ACL, 2003, pp. 423–430.
63.
MenardS.Applied logistic regression analysis: Quantitative applications in the social sciences. Beverley Hills, CA: Sage University Press, 2002.
64.
PallantJ.SPSS survival manual: A step by step guide to data analysis using SPSS for Windows version 10. Buckingham: Open University Press, 2001.
65.
SarkarTD.Impact of online interactivity dimensions on library website quality. Annals of Library and Information Studies2013; 59(4): 231–239.
66.
OtterbacherJ.Gender, writing and ranking in review forums: A case study of the IMDb. Knowledge and Information Systems2013; 35(3): 645–664.
67.
BloomaMJGohDHLChuaAYK. Predictors of high-quality answers. Online Information Review2012; 36(3): 383–400.
68.
MatzatU.Disciplinary differences in the use of internet discussion groups: Differential communication needs or trust problems?Journal of Information Science2009; 35(5): 613–631.
69.
OhOAgrawalMRaoHR.Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly2013; 37(2): 407–426.
70.
LichtMH.Multiple regression and correlation. In: LaurenceGPaulYR (eds). Reading and understanding multivariate statistics. Seattle, WA: APA, 1995, pp. 19–64.
71.
O’BrienRM.A caution regarding rules of thumb for variance inflation factors. Quality & Quantity2007; 41(5): 673–690.
72.
FormanCGhoseAWiesenfeldB.Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research2008; 19(3): 291–313.
73.
WuPFvan der HeijdenHKorfiatisN. The influences of negativity and review quality on the helpfulness of online reviews. In: Proceedings of the International Conference on Information Systems, http://epubs.surrey.ac.uk/7533/2/icis2011.final.pdf (2011, accessed June 2015).
74.
BullerDBBurgoonJK.Interpersonal deception theory. Communication Theory1996; 6(3): 203–242.
75.
HuntKM. Gaming the system: Fake online reviews v. consumer law. Computer Law & Security Review2015; 31(1): 3–25.
76.
PetzGKarpowiczMFürschußHAuingerAStříteskýVHolzingerA. Computational approaches for mining user’s opinions on the Web 2.0. Information Processing & Management2014; 50(6): 899–908.
