With the recognition of the significance of OSNs (Online Social Networks) in the recommendation of services in e-commerce, there are more and more e-commerce platform being combined with OSNs, forming social e-commerce, where a participant could recommend a product to his/her friends based on the participant’s corresponding purchasing experience. For example, at Epinions, a buyer could share product reviews with his/her friends. In such platforms, a buyer providing lots of high quality reviews is very likely to influence many potential buyers’ purchase behaviours. Such a buyer is believed to have strong social influence. However, dishonest participants in OSNs can deceive the existing social influence evaluation models, by mounting attacks, such as Constant (Dishonest advisors constantly provide unfairly positive/negative ratings to sellers.) and Camouflage (Dishonest advisors camouflage themselves as honest advisors by providing fair ratings to build up their trustworthiness first and then gives unfair ratings.), to obtain fake strong social influence. Therefore, it is crucial to devise a robust social influence evaluation model that can defend against attacks and deliver more accurate social influence evaluation results. In this paper, we propose a novel robust Trust-Aware Social Influencer Finding, TrustINF, method that considers the evolutionary trust relationship and the variations of historical social influences of participants, which can help deliver more accurate social influence evaluation results in social e-commerce. Our experiments conducted on four real social network datasets validate the effectiveness and robustness of our proposed method, which is greatly superior to the state-of-the-art method.