Informing observers: quality-driven filtering and composition of web 2.0 sources

Current Web technologies enable an active role of users, who can create and share their contents very easily. This mass of information includes opinions about a variety of key interest topics and represents a new and invaluable source of marketing information. Public and private organizations that aim at understanding and analyzing this unsolicited feedback need adequate platforms that can support the detection and monitoring of key topics. Hence, there is an emerging trend towards automated market intelligence and the crafting of tools that allow monitoring in a mechanized fashion. We therefore present an approach that is based on quality of Web 2.0 sources as the key factor for information filtering and also allows the users to flexibly and easily compose their analysis environments thanks to the adoption of a mashup platform.