Implicit Communities in Social Networks and Indoctrination

Abstract

we studied the problem of assessing the quality of implicit community detection in a graph constructed from data imported from social networks and instant messengers. Additionally, we analyzed methods for identifying indoctrination within such networks. We considered two approaches to evaluating the accuracy of community detection. The first approach, based on information theory, involved calculating normalized mutual information (NMI) and asymmetrically normalized mutual information (ANRMI). The second approach examined three methods for assessing the quality of implicit community detection using text analysis. We determined and compared pairwise rank correlation coefficients for dictionaries derived from different text arrays. We also applied correspondence analysis to study corpora of community texts. The third method involved calculating the psycholinguistic characteristics of text arrays associated with implicit communities. Using real data, we demonstrated the applicability of these methods for evaluating the partitioning of a social network graph and analyzing information influence within the network.