Trust amongst users engaged in consumer-to-consumer (C2C) e-commerce on Twitter as well as other social media platforms has been on the decline. The cost effective manner and timely delivery of C2C content makes it possible to reach a wider consumer base across the globe. However, this is under threat partly because of the risk of being scammed by other consumers on these platforms and the uncertainty related to this kind of e-commerce. Social media platforms such as Twitter are experiencing a decline in active user partly because of misuse of their platform. Twitter features can be used to build a consumer-to-consumer trust inference algorithm that can be relied upon by consumers in determining who to engage with in C2C e-commerce for specific contexts having not interacted directly with the seller/buyer in the physical world. There is a need by consumers to know whom they can trust on important C2C e-commerce transactions to limit their exposure to scams and fraudulent users on Twitter. This research sought to develop an algorithm to infer the trust score of a user engaged in consumer-to-consumer e-commerce using features present on his/her user profile. The algorithm utilized machine learning techniques. The algorithm provides consumers with a sense of trust in C2C engagements on Twitter. The research employed an experimental approach that involved the development of an algorithm and its validation. Wrapper approach was adopted for feature selection using data mined using Twitter Search API using C2C keyword-hashtag (#). Multi-class classification was successfully applied to infer a consumers trust score. Potential users can then use the proposed algorithm to check and choose trusted consumers on Twitter for different transactions.