Signaling Authenticity: Verification Cues and Brand-Impersonation Scams in U.S. Beauty and Personal-Care Social Commerce

Social commerce lets consumers buy from brands inside feeds, profiles and advertisements that impersonators can copy, and reported losses to scams that began on social media reached $2.1 billion in the United States in 2025, most often through purchases that never arrived. This study asks which U.S. beauty and personal-care brands publish verification cues that are costly to imitate, and whether those cues are associated with less brand-impersonation infrastructure. Drawing on signalling theory and information asymmetry, we measure nine publicly observable cues (SPF, DMARC enforcement, BIMI, DNSSEC, CAA, registry lock, corporate registrar, HSTS preload and TikTok verification) for 245 brands assembled from Wikidata, and count registered look-alike domains generated from each brand's name across seven top-level domains and six commerce keywords, distinguishing third-party from brand-controlled registrations. Negative binomial, hurdle and logistic models with robust standard errors show that third-party look-alike registrations rise steeply with web reach (incidence rate ratio 2.67 per log10 unit of Tranco reach) and domain age, that cue adoption rises just as steeply with reach, and that conditional on reach no cue or cue index is associated with fewer look-alikes. Two-thirds of brands have at least one third-party look-alike, and shop, store and official variants are almost always third-party. Verification cues therefore function as signals for consumers rather than deterrents for impersonators, and the brands least likely to signal are the small brands that social commerce multiplies. An Authenticity Signalling Framework assigns complementary roles to brands, platforms, regulators and consumers.

Brand impersonation; signalling theory; verification cues; social commerce; combosquatting; email authentication; consumer protection; negative binomial regression

1. Adesokan, O. Z., Bamsa, A. O., Obioha-Val, O. A., Ejoh, C. I., & Akeke, M. A. (2026). Adversarial robustness of machine learning-based fraud detection systems: An empirical evaluation of attack impact and mitigation in fintech environments. Journal of Engineering Research and Reports, 28(7), 261–279. https://doi.org/10.9734/jerr/2026/v28i71957 [Google Scholar] [Crossref]

2. Agten, P., Joosen, W., Piessens, F., & Nikiforakis, N. (2015). Seven months' worth of mistakes: A longitudinal study of typosquatting abuse. In Proceedings 2015 Network and Distributed System Security Symposium. Internet Society. https://doi.org/10.14722/ndss.2015.23058 [Google Scholar] [Crossref]

3. Akerlof, G. A. (1970). The market for "lemons": Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500. https://doi.org/10.2307/1879431 [Google Scholar] [Crossref]

4. Anderson, R., & Moore, T. (2006). The economics of information security. Science, 314(5799), 610–613. https://doi.org/10.1126/science.1130992 [Google Scholar] [Crossref]

5. BBB Institute for Marketplace Trust. (2025). 2024 BBB Scam Tracker risk report. [Google Scholar] [Crossref]

6. https://BBBMarketplaceTrust.org/RiskReport [Google Scholar] [Crossref]

7. Bitaab, M., Cho, H., Oest, A., Lyu, Z., Wang, W., Abraham, J., Wang, R., Bao, T., Shoshitaishvili, Y., & Doupé, A. (2023). Beyond phish: Toward detecting fraudulent e-commerce websites at scale. In 2023 IEEE Symposium on Security and Privacy (SP) (pp. 2566–2583). IEEE. [Google Scholar] [Crossref]

8. https://doi.org/10.1109/SP46215.2023.10179461 [Google Scholar] [Crossref]

9. Bitaab, M., Karimi, A., Lyu, Z., Oest, A., Kuchhal, D., Saad, M., Ahn, G.-J., Wang, R., Bao, T., Shoshitaishvili, Y., & Doupé, A. (2025). ScamMagnifier: Piercing the veil of fraudulent shopping website campaigns. In Proceedings 2025 Network and Distributed System Security Symposium. Internet Society. https://doi.org/10.14722/ndss.2025.240763 [Google Scholar] [Crossref]

10. Button, M., Lewis, C., & Tapley, J. (2014). Not a victimless crime: The impact of fraud on individual victims and their families. Security Journal, 27(1), 36–54. https://doi.org/10.1057/sj.2012.11 [Google Scholar] [Crossref]

11. Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139013567 [Google Scholar] [Crossref]

12. Carpineto, C., & Romano, G. (2017). Learning to detect and measure fake ecommerce websites in search-engine results. In Proceedings of the International Conference on Web Intelligence (pp. 403–410). ACM. https://doi.org/10.1145/3106426.3106441 [Google Scholar] [Crossref]

13. Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766. [Google Scholar] [Crossref]

14. https://doi.org/10.1037/0022-3514.39.5.752 [Google Scholar] [Crossref]

15. Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. https://doi.org/10.1177/0149206310388419 [Google Scholar] [Crossref]

16. Dhamija, R., Tygar, J. D., & Hearst, M. (2006). Why phishing works. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 581–590). ACM. [Google Scholar] [Crossref]

17. https://doi.org/10.1145/1124772.1124861 [Google Scholar] [Crossref]

18. Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277. [Google Scholar] [Crossref]

19. https://doi.org/10.1509/jmkr.38.2.269.18845 [Google Scholar] [Crossref]

20. Durumeric, Z., Adrian, D., Mirian, A., Kasten, J., Bursztein, E., Lidzborski, N., Thomas, K., Eranti, V., Bailey, M., & Halderman, J. A. (2015). Neither snow nor rain nor MITM…: An empirical analysis of email delivery security. In Proceedings of the 2015 Internet Measurement Conference (pp. 27–39). ACM. https://doi.org/10.1145/2815675.2815695 [Google Scholar] [Crossref]

21. Edgerly, S., & Vraga, E. K. (2019). The blue check of credibility: Does account verification matter when evaluating news on Twitter? Cyberpsychology, Behavior, and Social Networking, 22(4), 283–287. https://doi.org/10.1089/cyber.2018.0475 [Google Scholar] [Crossref]

22. Ejiofor, V. O. (2025). Open data for cyber resilience: An analysis of public-private collaboration in AI-supported threat intelligence sharing. Asian Journal of Research in Computer Science, 18(6), 403–423. [Google Scholar] [Crossref]

23. https://doi.org/10.9734/ajrcos/2025/v18i6710 [Google Scholar] [Crossref]

24. Ejoh, C. I., Akeke, C. U., Oladoyinbo, O. B., Henry, O., & Archibong, U. N. (2026). Ethical and secure deployment of generative AI: Balancing innovation, data privacy, and enterprise risk governance. Journal of Engineering Research and Reports, 28(7), 48–68. [Google Scholar] [Crossref]

25. https://doi.org/10.9734/jerr/2026/v28i71943 [Google Scholar] [Crossref]

26. Federal Trade Commission. (2024, April 1). Impersonation scams: Not what they used to be [Consumer Protection Data Spotlight]. https://www.ftc.gov/news-events/data-visualizations/data-spotlight/2024/04/impersonation-scams-not-what-they-used-be [Google Scholar] [Crossref]

27. Federal Trade Commission. (2026, April 27). Reported losses to scams on social media eight times higher than in 2020 [Consumer Protection Data Spotlight]. https://www.ftc.gov/news-events/data-visualizations/data-spotlight/2026/04/reported-losses-scams-social-media-eight-times-higher-2020 [Google Scholar] [Crossref]

28. Gbadebo, O. A., & Gbadebo, M. O. (2025). Strengthening cyber resilience: Policy and governance responses to global cyber threats. Asian Journal of Research in Computer Science, 18(10), 183–198. https://doi.org/10.9734/ajrcos/2025/v18i10772 [Google Scholar] [Crossref]

29. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519 [Google Scholar] [Crossref]

30. Hu, H., Peng, P., & Wang, G. (2018). Towards understanding the adoption of anti-spoofing protocols in email systems. In 2018 IEEE Cybersecurity Development (SecDev) (pp. 94–101). IEEE. https://doi.org/10.1109/SecDev.2018.00020 [Google Scholar] [Crossref]

31. Hu, X., Wu, G., Wu, Y., & Zhang, H. (2010). The effects of Web assurance seals on consumers' initial trust in an online vendor: A functional perspective. Decision Support Systems, 48(2), 407–418. [Google Scholar] [Crossref]

32. https://doi.org/10.1016/j.dss.2009.10.004 [Google Scholar] [Crossref]

33. Integrity, Notification, and Fairness in Online Retail Marketplaces for Consumers Act, Pub. L. No. 117-328, div. BB, tit. III, 15 U.S.C. § 45f (2022). [Google Scholar] [Crossref]

34. https://www.ftc.gov/legal-library/browse/statutes/inform-consumers-act [Google Scholar] [Crossref]

35. Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1–22. https://doi.org/10.1177/002224299305700101 [Google Scholar] [Crossref]

36. Kintis, P., Miramirkhani, N., Lever, C., Chen, Y., Romero-Gómez, R., Pitropakis, N., Nikiforakis, N., & Antonakakis, M. (2017). Hiding in plain sight: A longitudinal study of combosquatting abuse. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 569–586). ACM. https://doi.org/10.1145/3133956.3134002 [Google Scholar] [Crossref]

37. Kirmani, A., & Rao, A. R. (2000). No pain, no gain: A critical review of the literature on signaling unobservable product quality. Journal of Marketing, 64(2), 66–79. [Google Scholar] [Crossref]

38. https://doi.org/10.1509/jmkg.64.2.66.18000 [Google Scholar] [Crossref]

39. Le Pochat, V., Van Goethem, T., Tajalizadehkhoob, S., Korczyński, M., & Joosen, W. (2019). Tranco: A research-oriented top sites ranking hardened against manipulation. In Proceedings 2019 Network and Distributed System Security Symposium. Internet Society. [Google Scholar] [Crossref]

40. https://doi.org/10.14722/ndss.2019.23386 [Google Scholar] [Crossref]

41. Liang, T.-P., & Turban, E. (2011). Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5–14. [Google Scholar] [Crossref]

42. https://doi.org/10.2753/JEC1086-4415160201 [Google Scholar] [Crossref]

43. Lin, X., Li, Y., & Wang, X. (2017). Social commerce research: Definition, research themes and the trends. International Journal of Information Management, 37(3), 190–201. [Google Scholar] [Crossref]

44. https://doi.org/10.1016/j.ijinfomgt.2016.06.006 [Google Scholar] [Crossref]

45. Mariconti, E., Onaolapo, J., Ahmad, S. S., Nikiforou, N., Egele, M., Nikiforakis, N., & Stringhini, G. (2017). What's in a name? Understanding profile name reuse on Twitter. In Proceedings of the 26th International Conference on World Wide Web (pp. 1161–1170). ACM. [Google Scholar] [Crossref]

46. https://doi.org/10.1145/3038912.3052589 [Google Scholar] [Crossref]

47. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. [Google Scholar] [Crossref]

48. https://doi.org/10.1287/isre.13.3.334.81 [Google Scholar] [Crossref]

49. Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210–220. [Google Scholar] [Crossref]

50. https://doi.org/10.1016/j.pragma.2013.07.012 [Google Scholar] [Crossref]

51. Moore, T., & Edelman, B. (2010). Measuring the perpetrators and funders of typosquatting. In R. Sion (Ed.), Financial cryptography and data security (Lecture Notes in Computer Science, Vol. 6052, pp. 175–191). Springer. https://doi.org/10.1007/978-3-642-14577-3_15 [Google Scholar] [Crossref]

52. Morris, M. R., Counts, S., Roseway, A., Hoff, A., & Schwarz, J. (2012). Tweeting is believing? Understanding microblog credibility perceptions. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (pp. 441–450). ACM. [Google Scholar] [Crossref]

53. https://doi.org/10.1145/2145204.2145274 [Google Scholar] [Crossref]

54. Oladoyinbo, T. O., Olabanji, S. O., Olaniyi, O. O., Adebiyi, O. O., Okunleye, O. J., & Alao, A. I. (2024). Exploring the challenges of artificial intelligence in data integrity and its influence on social dynamics. Asian Journal of Advanced Research and Reports, 18(2), 1–23. [Google Scholar] [Crossref]

55. https://doi.org/10.9734/ajarr/2024/v18i2601 [Google Scholar] [Crossref]

56. Olaniyi, O. O., Olabanji, S. O., & Abalaka, A. I. (2023). Navigating risk in the modern business landscape: Strategies and insights for enterprise risk management implementation. Journal of Scientific Research and Reports, 29(9), 103–109. https://doi.org/10.9734/jsrr/2023/v29i91789 [Google Scholar] [Crossref]

57. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2 [Google Scholar] [Crossref]

58. Raval, D. (2020). Whose voice do we hear in the marketplace? Evidence from consumer complaining behavior. Marketing Science, 39(1), 168–187. https://doi.org/10.1287/mksc.2018.1140 [Google Scholar] [Crossref]

59. Roberts, R., Goldschlag, Y., Walter, R., Chung, T., Mislove, A., & Levin, D. (2019). You are who you appear to be: A longitudinal study of domain impersonation in TLS certificates. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 2489–2504). ACM. https://doi.org/10.1145/3319535.3363188 [Google Scholar] [Crossref]

60. Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. [Google Scholar] [Crossref]

61. https://doi.org/10.2307/1882010 [Google Scholar] [Crossref]

62. Tian, K., Jan, S. T. K., Hu, H., Yao, D., & Wang, G. (2018). Needle in a haystack: Tracking down elite phishing domains in the wild. In Proceedings of the Internet Measurement Conference 2018 (pp. 429–442). ACM. https://doi.org/10.1145/3278532.3278569 [Google Scholar] [Crossref]

63. Trade Regulation Rule on Impersonation of Government and Businesses, 16 C.F.R. Part 461, 89 Fed. Reg. 15017 (March 1, 2024) (effective April 1, 2024). https://www.govinfo.gov/content/pkg/FR-2024-03-01/html/2024-04335.htm [Google Scholar] [Crossref]

64. Vaidya, T., Votipka, D., Mazurek, M. L., & Sherr, M. (2019). Does being verified make you more credible? Account verification's effect on tweet credibility. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. https://doi.org/10.1145/3290605.3300755 [Google Scholar] [Crossref]

65. Vu, D. L., Pashchenko, I., Massacci, F., Plate, H., & Sabetta, A. (2020). Typosquatting and combosquatting attacks on the Python ecosystem. In 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) (pp. 509–514). IEEE. [Google Scholar] [Crossref]

66. https://doi.org/10.1109/EuroSPW51379.2020.00074 [Google Scholar] [Crossref]

67. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934 [Google Scholar] [Crossref]

68. Wilcox, K., Kim, H. M., & Sen, S. (2009). Why do consumers buy counterfeit luxury brands? Journal of Marketing Research, 46(2), 247–259. https://doi.org/10.1509/jmkr.46.2.247 [Google Scholar] [Crossref]

69. Wilson, P. (2015). The misuse of the Vuong test for non-nested models to test for zero-inflation. Economics Letters, 127, 51–53. https://doi.org/10.1016/j.econlet.2014.12.029 [Google Scholar] [Crossref]

70. Zeng, E., Kohno, T., & Roesner, F. (2021). What makes a "bad" ad? User perceptions of problematic online advertising. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–24). ACM. https://doi.org/10.1145/3411764.3445459 [Google Scholar] [Crossref]