In this paper, we present an approach for investigating information cascades in social and collaborative networks. The proposed approach seeks to improve methods limited to the detection of paths through which merely exact content-tokens are propagated. For this sake, we adopt to leverage web intelligence to the purpose of discovering paths that convey exact content-tokens cascades, as well as paths that convey concepts or topics related to these content-tokens. Indeed, we mine sequence of actors involved in cascades of keywords and topics extracted from their posts, using simple to use restful APIs available on the web. For the evaluation of the approach, we conduct experiments based on assimilating a scientific collaborative network to a social network. Our findings reveal the detection of missed information when using merely exact content propagation. Moreover, we noted that the vocabulary of actors is preserved mostly in short cascades, where topics become a better alternative in long cascades.