Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patterns and possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance of hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns.
Proceedings of the 2012 Ieee/acm International Conference on Advances in Social Networks Analysis and Mining, 2012, p. 510-515