# Collective Intelligence in Polish-Ukrainian Internet Projects. Debate Models and Research Methods
## 3 Results
### The Literature Review and Theoretical Framework for the Qualitative Research
#### 3.1.1 Debate Models Present in the Public Sphere and Their Features
In this paragraph, we will briefly present those methods that we considered to be compatible with the public sphere debate models described above and the most adequate to examine the aspects of communication on public issues conducted online. The first method shows the purely behavioral aspects of CI by calculating metrics of online communication, measuring several aspects of users' behavior, identifying and mapping meaningful patterns and finally applying algorithms and statistical analysis to detect meaningful anomalies from those patterns. Analytical software, dedicated to gathering and processing the data arising from a particular debate, seems to be the most adequate tool for this work. Using real-time online analytics (often powered by AI algorithms) would allow the extraction of elements from the debate such as: Issues, ideas, arguments for and against an idea, users' clusters, users' relations, communication patterns, and decisions. The advanced analytics allowed us to generate customised metrics, alerts, and reports to give the participants and the moderators a sense of how the debate is going, and how they can contribute best [40]. Theoretical background of this approach is based on a scheme developed recently by [PERSON] [40; 41; 42; 43; 44], and earlier by [PERSON] [45; 46; 47]. Using such a system would allow us to identify hot spots in the debate, measure the level of involvement in the discussion, useful contributions, and other key issues. Among the metrics already available in the existing software [43], and the proposals of their possible extension, we found the following as the most promising:  
* Maturity--indication how mature the discussion for an issue is, estimated by gathering statistics on the topology of the branch for the debated problem: The greater complexity of the threads and the coverage of them by arguments, the better;
* Controversy--a topic that generated a large number of conflicting opinions;
* Inequality--measuring to what extent the community support is unequal for the ideas related to an issue;
* An individual participant's level of expertise and integrity, evaluated by other participants and/or the AI algorithms during the debate;
* Clusters--identifying clusters of posts that tend to be liked, rated, and viewed together;
* Support consistency--measuring to what extent an idea's average rating is consistent with the ratings for the underlying arguments;
* Social graph--returning a graph showing which users have interacted (rated, commented on, responded to, or edited posts created by the other user);
* Groupthink--estimating the level of groupthink in the deliberation for a given issue. Groupthink occurs when a particular group converges prematurely on one solution, without giving adequate attention to competing ideas. This can help to detect such phenomena as polarisation and balkanisation [43, 44];
* Social interaction strength, level of confidence, the density of social relations, scope of distrust, and the value of interdependence [48, 49, 50]. The advantage of this approach is a high level of objectivity: The assessment is independent of the individual interpretations of the researcher and is based on the processing of directly obtained numerical data. To identify the features of the deliberative model, metrics can be used that reveal: High level of equality (no dominant opinion leaders), low polarisation and/or balkanisation, low conflict level, low level of controversy in the top rated contributions, low level of distrust and criticality towards users who gained a high metric of expertise, and active content assimilation by making consistent arguments.