Collective intelligence (CI) is a property of groups that emerges from the coordination and collaboration of members and predicts group performance on a wide range of tasks. Previous studies of CI have been conducted with lab-based groups in the USA. We introduce a new standardized online battery to measure CI and demonstrate consistent emergence of a CI factor across three different studies despite broad differences in (a) communication media (face-to-face vs online), (b) group contexts (short-term ad hoc groups vs long-term groups) and (c) cultural settings (US, Germany, and Japan). In two of the studies, we also show that CI is correlated with a group's performance on more complex tasks. Consequently, the CI metric provides a generalizable performance measure for groups that is robust to broad changes in media, context, and culture, making it useful for testing the effects of general-purpose collaboration technologies intended to improve group performance.

Today, online communities in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. Subjects of postings can be partly retrieved by state of the art text mining techniques. A much more challenging task is to detect factors influencing the evolvement of opinions within the social network. For such a kind of trend scouting you have to take into account the relationships among the community members. Social network analysis helps to explain social behavior of linked persons by providing quantitative measures of social interactions. A new approach based on social network analysis is presented, which allows detecting opinion leaders and opinion trends. This leads a better understanding of opinion formation. The overall concept based on text mining and social network analysis is introduced. An example is given which illustrates the analysis process.

An increasing number of people are socializing within online networks. By means of interaction, network members influence one another's opinion. For companies, it is important to know how opinions spread throughout networks in order to be able to take appropriate marketing actions. A new approach is presented which simulates the spread of opinions within online social networks. The principles of opinion formation are first revealed by ant mining algorithms coming from swarm intelligence and then applied to simulate the spread of opinions.

The Internet contains an increasing number of online forums where consumers exchange product opinions. It is important for companies to know how consumers judge their products and how these opinions are spread by interactions throughout online forums. With this knowledge it is possible to recognize risks and chances. However, classical opinion research is very time consuming and only possible to a certain extent. This paper introduces a system which automatically extracts opinions and communication relationships in forums by text mining and identifies influential users and trends by social network analysis.

An increasing number of users of opinion forums provide us with rich sources of data about customer opinions. Several fields of research deal with the analysis of qualitative or quantitative data. While affective text mining research explores qualitative data, social network analysis focuses on quantitative mining methods. A combined analysis of both information classes is still missing. However, this combined analysis is necessary for identifying the process of opinion formation in opinion forums. In this paper, we attempt to bridge this gap by applying new methods which are required for network analysis of opinion forum data. We are also presenting a plug-in for the popular biological research tool Cytoscape. This plug-in combined with visualization features of Cytoscape implements the key features described in this paper. Our approach will provide companies with information about chances and risks concerning their products.