The emergence of online financial information channels, such as web portals and financial blogs, eases the challenge process for scammers of publishing fraudulent contents in order to manipulate share prices. To maintain market integrity, financial market surveillance authorities monitor these different information channels to detect suspicious behaviour. However, as the available amount of online information increases, analyses become more costly and time-consuming. In order to support related decisions, we have developed a model to identify fraudulent situations. Based on interviews with domain experts, we first identified the factors determining suspicious situations and then applied a qualitative multi-attribute modelling technique. Thereby, our resulting model builds upon valuable knowledge of domain experts and provides means to address the challenge of information based market manipulation.