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An interesting CNBC research article, “Amazon is filled with fake reviews and it’s getting harder to spot them”, exposes a long-standing problem: the sharp decline in the quality of Amazon reviews as a result of companies buying positive assessments for their products or negative ones for competing products on Facebook and other social networks.
Beyond the specific case of Amazon — which has recently suffered similar problems with Amazon’s Choice — the problem of the corruption of social systems on the web is, for me as a researcher, fascinating: as soon as a social-based metric, be it ratings, likes, favorites, followers or any other, acquires a certain popularity, schemes aimed at obtaining a profit by falsifying and distorting it automatically appear. As soon as a network or a scheme with a social base reaches a certain level of popularity, clandestine services destined to sell fake reviews proliferate on it, quickly finding interested parties willing to pay for them, and which end up, if no decisive action is taken, destroying the site’s or the metric’s value proposition.
This is a big issue for Amazon: in a crowded marketplace with many different options for every product, consumers tend to trust reviews. If those buyers start to believe that the reviews are simply bought and sold to the highest bidder, their trust in the site could crumble.
Will Amazon do anything to tackle the problem? Detecting user review patterns is easy, as is using browser fingerprinting techniques to flag when a user is managing several different accounts. A clear message needs to be sent out that these types of activities will not be tolerated, which means banning wrongdoers from the site, in addition to reporting and pursuing the services that offer them — which is more difficult, but at least signals the company’s commitment to increase the barriers to such fraudulent activities.
The problem is usually the result of growth hacking schemes by the companies involved, which tend to be extremely short-term: a more active review market is usually seen as a positive indicator, which in many cases leads to increased popularity, activity, time spent on the site or sales. In practice, few companies tend to consider the problem until it is too late and they find themselves in the headlines and with their credibility undermined.
Another obvious option is machine learning: the usage patterns of people taking part in these types of schemes are usually easy to recognize algorithmically, because their activity is restricted to these reviews. Again, Amazon is in a position to observe from on high the entirety of activity on its ecosystem, however vast and complex it might be, and could easily take action to prevent misuse.
The pattern, in any case, is evident, and can be stated as a law:
“Any system with a social base will experience, from a certain level of popularity, a corruption of its operations that will tend to destroy the value of the metrics used in it.“
From this point on, we can only judge companies that experience this type of corruption in terms of their commitment to respond rapidly and seriously to combat it. Many years on from scandals such as buying followers or likes on Twitter, Facebook, Instagram or, more recently, TikTok, these kinds of schemes are still generally available, despite attempts to stop using these metrics as a fundamental parameter in these social networks, and there is talk that these types of systems may have been responsible for the creation of the so called ‘internet of lies’. But for the social networks involved, the problem is relative: their users are mainly interested in connecting with other people or in accessing information, and the reliability of their metrics is usually a relatively secondary issue.
In the case of Amazon and other ecommerce sites, this is a bigger issue that affects the sales of products of companies that, ultimately, may even be forced to participate in such practices if they want to remain competitive, with the consequent direct economic damage. For consumers, the consequences of a fake review are sometimes greater, and can destroy confidence in the site as a whole. When a large percentage of your users or potential users know that your product evaluations are a big, fat lie, your value proposition and the trust that your users deposit in you and your operations as an online store can be significantly eroded.
In any event, the problem has been identified, and we also know whose turn it is to make a move. If it is still easy to make a fake review on Amazon in the future, the damage to the company will likely end up being greater than that experienced by Facebook, Twitter, Instagram or TikTok. The time has come for Amazon to take action.
