Brand Impersonation and Customer Support Hijacking Analysing How Fraudulent Customer Service Accounts on Social Media Scam Corporate Clients
Year: 2026
Type: article
Abstract: Social media is a public-facing channel for customer-service, and brands are now able to respond to questions and complaints quickly. Consumers' visibility also enables scams to monitor consumers, replicate brand identities and approach consumers via replies or private messages. This article will discuss the methods used by fraudulent customer support accounts to leverage brand recognition and social trust, and will assess controls at the user, company, platform and payment levels. This focused narrative review collated 29 scholarly papers from 2020-2026, comprising of 21 journal papers and eight peer-reviewed conference papers. The evidence shows brand impersonation is not just a fake-profile problem of classification. It's a multi-step fraud route that includes squatting on an account, timely interception of support requests, transferring to private or external channels, requests for credentials, verification codes or payment. Machine-learning studies provide helpful indicators from profile content, profile behavior and network structure, and human-factors studies explain why social familiarity and perceived authenticity can make a difference in reducing scrutiny. Most detection studies rely on labels and data sets, however, that are not inherently linked to customer-support impersonation or financial loss. The article suggests a multi-layered approach to the response, incorporating the official channel clarity of guidance, fast reporting of evidence for preservation, behaviour-awareness in platform detection, safer support channels and metrics for success like time to takedown and repeat-account activity. The review offers a practical approach for minimising the risk of customer harm, as well as opportunities for research in cross-platform measurement and in the field.
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225792611
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