Man vs machine – Detecting deception in online reviews

Amazon (2022). About Amazon Vine. Retrieved from https://www.amazon.com/vine/about Accessed June 15th, 2022. Banerjee, 2017, Authentic versus fictitious online reviews: A textual analysis across luxury, budget, and mid-range hotels, Journal of Information Science, 43, 122, 10.1177/0165551515625027 Cardoso, 2018, Towards automatic filtering of fake reviews, Neurocomputing, 309, 106, 10.1016/j.neucom.2018.04.074 Chakraborty, 2018, The effects of credible online reviews on brand equity dimensions and its consequence on consumer behavior, Journal of Promotion Management, 24, 57, 10.1080/10496491.2017.1346541 Chatterjee, 2021, Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application, Journal of Business Research, 131, 815, 10.1016/j.jbusres.2020.10.043 Choi, 2017, The role of power and incentives in inducing fake reviews in the tourism industry, Journal of Travel Research, 56, 975, 10.1177/0047287516677168 Corley, 2011, Building theory about theory building: What constitutes a theoretical contribution?, Academy of Management Review, 36, 12, 10.5465/amr.2009.0486 Crane, 2018, Quants and poets: Advancing methods and methodologies in business and society research, Business and Society, 57, 3, 10.1177/0007650317718129 DeAndrea, 2012, When do people misrepresent themselves to others? The effects of social desirability, ground truth, and accountability on deceptive self-presentations, Journal of Communication, 62, 400, 10.1111/j.1460-2466.2012.01646.x De Bakker, 2019, Publishing interdisciplinary research in business & society, Business and Society, 58, 443, 10.1177/0007650319826188 De Langhe, 2016, Navigating by the stars: Investigating the actual and perceived validity of online user ratings, Journal of Consumer Research, 42, 817, 10.1093/jcr/ucv047 Dellarocas, 2006, Strategic manipulation of internet opinion forums: Implications for consumers and firms, Management Science, 52, 1577, 10.1287/mnsc.1060.0567 DePaulo, 2003, Cues to deception, Psychological Bulletin, 129, 74, 10.1037/0033-2909.129.1.74 Feldman, 2002, Self-presentation and verbal deception: Do self-presenters lie more?, Basic and Applied Social Psychology, 24, 163, 10.1207/S15324834BASP2402_8 Gehman, 2018, Finding theory–method fit: A comparison of three qualitative approaches to theory building, Journal of Management Inquiry, 27, 284, 10.1177/1056492617706029 Gentina, 2021, Development of theory of mind on online social networks: Evidence from Facebook, Twitter, Instagram, and Snapchat, Journal of Business Research, 124, 652, 10.1016/j.jbusres.2020.03.001 Gioia, 2013, Seeking qualitative rigor in inductive research: Notes on the Gioia Methodology, Organizational Research Methods, 16, 15, 10.1177/1094428112452151 Gioia, 2022, A curated debate: On using “templates” in qualitative research, Journal of Management Inquiry, 31, 231, 10.1177/10564926221098955 Gössling, 2018, The manager’s dilemma: A conceptualization of online review manipulation strategies, Current Issues in Tourism, 21, 484, 10.1080/13683500.2015.1127337 Hajek, 2022, Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection, Technological Forecasting and Social Change, 177 Hartwig, 2011, Why do lie-catchers fail? A lens model meta-analysis of human lie judgments, Psychological Bulletin, 137, 643, 10.1037/a0023589 Humphreys, 2018, Automated text analysis for consumer research, Journal of Consumer Research, 44, 1274, 10.1093/jcr/ucx104 Heydari, 2015, Detection of review spam: A survey, Expert Systems with Applications, 42, 3634, 10.1016/j.eswa.2014.12.029 Hu, 2011, Manipulation in digital word-of-mouth: A reality check for book reviews, Decision Support Systems, 50, 627, 10.1016/j.dss.2010.08.013 Hu, 2012, Manipulation of online reviews: An analysis of ratings, readability, and sentiments, Decision Support Systems, 52, 674, 10.1016/j.dss.2011.11.002 Ivanova, 2017, How can online marketplaces reduce rating manipulation? A new approach on dynamic aggregation of online ratings, Decision Support Systems, 104, 64, 10.1016/j.dss.2017.10.003 Kashy, 1996, Who lies?, Journal of Personality and Social Psychology, 70, 1037, 10.1037/0022-3514.70.5.1037 Kavlakoglu, E. (2020). AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference? Retrieved from https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks Accessed January 15th, 2022. Krishen, 2019, Feelings and functionality in social networking communities: A regulatory focus perspective, Psychology and Marketing, 36, 675, 10.1002/mar.21204 Leary, 1995 Lee, 2018, Sentiment manipulation in online platforms: An analysis of movie tweets, Production and Operations Management, 27, 393, 10.1111/poms.12805 Luca, 2016, Fake it till you make it: Reputation, competition, and yelp review fraud, Management Science, 62, 3412, 10.1287/mnsc.2015.2304 Malbon, 2013, Taking fake online consumer reviews seriously, Journal of Consumer Policy, 36, 139, 10.1007/s10603-012-9216-7 McCornack, 2014, Information Manipulation Theory 2: A propositional theory of deceptive discourse production, Journal of Language and Social Psychology, 33, 348, 10.1177/0261927X14534656 Mc Cornack, 1992, When the alteration of information is viewed as deception: An empirical test of information manipulation theory, Communication Monographs, 59, 17, 10.1080/03637759209376246 McCornack, 1996, Speaking of information manipulation: A critical rejoinder, Communication Monographs, 63, 83, 10.1080/03637759609376376 McCornack, 1992, Information manipulation theory, Communication Monographs, 59, 1, 10.1080/03637759209376245 McCornack, 1990, When lies are uncovered: Emotional and relational outcomes of discovered deception, Communication Monographs, 57, 119, 10.1080/03637759009376190 Moon, 2021, Content analysis of fake consumer reviews by survey-based text categorization, International Journal of Research in Marketing, 38, 343, 10.1016/j.ijresmar.2020.08.001 Mukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. WWW’12 - Proceedings of the 21st Annual Conference on World Wide Web, 191–200. https://doi.org/10.1145/2187836.2187863. Munzel, 2016, Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus, Journal of Retailing and Consumer Services, 32, 96, 10.1016/j.jretconser.2016.06.002 Munzel, 2015, Malicious practice of fake reviews: Experimental insight into the potential of contextual indicators in assisting consumers to detect deceptive opinion spam, Recherche et Applications en Marketing (English Edition), 30, 24, 10.1177/2051570715604155 Newman, 2003, Lying words: Predicting deception from linguistic styles, Personality and Social Psychology Bulletin., 29, 665, 10.1177/0146167203029005010 Ott, 2011, 309 Ott, M., Cardie, C., & Hancock, J. (2012). Estimating the prevalence of deception in online review communities. WWW’12 - Proceedings of the 21st Annual Conference on World Wide Web, 201–210. https://doi.org/10.1145/2187836.2187864. Ott, Myle, Cardie, Claire, & Hancock, Jeffrey T. (2013). Negative Deceptive Opinion Spam. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 497–501, Atlanta, Georgia. Association for Computational Linguistics. Peng, 2016, Consumer perceptions of online review deceptions: An empirical study in China, Journal of Consumer Marketing, 33, 269, 10.1108/JCM-01-2015-1281 Pennebaker, 2014, When Small Words Foretell Academic Success: The Case of College Admissions Essays, PLoS ONE, 9, 10.1371/journal.pone.0115844 Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development andpsychometric properties of LIWC2015. Austin, TX: University of Texas at Austin. Petrescu, 2018, Mitigating extremism: The intersection of radicalization and marketing, 267 Petrescu, 2018, Incentivized reviews: Promising the moon for a few stars, Journal of Retailing and Consumer Services, 41, 288, 10.1016/j.jretconser.2017.04.005 Petrescu, 2022, Innocent until proven guilty: Suspicion of deception in online reviews, European Journal of Marketing, 56, 1184, 10.1108/EJM-10-2019-0776 Plotkina, 2020, Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews, Journal of Business Research, 109, 511, 10.1016/j.jbusres.2018.12.009 Riquelme, 2016, Consumers’ perceptions of online and offline retailer deception: A moderated mediation analysis, Journal of Interactive Marketing, 35, 16, 10.1016/j.intmar.2016.01.002 Sahut, 2021, The age of digital entrepreneurship, Small Business Economics, 56, 1159, 10.1007/s11187-019-00260-8 Schau, 2003, We are what we post self presentation in personal web space, Journal of Consumer Research, 30, 385, 10.1086/378616 Schoenmueller, 2020, The polarity of online reviews: Prevalence, drivers and implications, Journal of Marketing Research, 57, 853, 10.1177/0022243720941832 Smith, 2006, Evaluation of unsupervised semantic text mapping, Behavior Research Methods, 38, 262, 10.3758/BF03192778 Smith, 2022, The cost of looking natural: Why the no-makeup movement may fail to discourage cosmetic use, Journal of the Academy of Marketing Science, 50, 324, 10.1007/s11747-021-00801-2 Steward, 2020, Credible effects: The impact of disclosure of material connections within online product reviews, Journal of Public Policy and Marketing, 39, 353, 10.1177/0743915619864543 Tausczik, 2010, The psychological meaning of words: LIWC and computerized text analysis methods, Journal of Language and Social Psychology, 29, 24, 10.1177/0261927X09351676 Textalyzer (2022). Textalyzer: Keyword Density + Word Count Tool. Retrieved from https://seoscout.com/tools/text-analyzer Accessed June 15th, 2022. Toma, 2008, Separating fact from fiction: An examination of deceptive self-presentation in online dating profiles, Personality and Social Psychology Bulletin, 34, 1023, 10.1177/0146167208318067 van Eck, 2010, Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics, 84, 523, 10.1007/s11192-009-0146-3 Waltman, 2010, A unified approach to mapping and clustering of bibliometric networks, Journal of Informetrics, 4, 629, 10.1016/j.joi.2010.07.002 Whetten, 2009, The practice of theory borrowing in organizational studies: Current issues and future directions, Journal of Management, 35, 537, 10.1177/0149206308330556 Whetten, 1989, What Constitutes a Theoretical Contribution?, 14, 490 Wu, 2020, Fake online reviews: Literature review, synthesis, and directions for future research, Decision Support Systems, 132 Zhuang, 2018, Manufactured opinions: The effect of manipulating online product reviews, Journal of Business Research, 87, 24, 10.1016/j.jbusres.2018.02.016