Analysing Social Media Network Data with R: Semi-Automated Screening of Users, Comments and Communication Patterns
This research addresses the problem of identifying and understanding negative communication forms like hate speech and fake news on social media, which has significant societal implications.
This paper presents a method for analyzing social media network data to identify communication patterns, track individual users, and inspect their comments and reach. The approach can identify particularly active users with 100% accuracy when considering the social network's framing and topics.
Communication on social media platforms is not only culturally and politically relevant, it is also increasingly widespread across societies. Users not only communicate via social media platforms, but also search specifically for information, disseminate it or post information themselves. However, fake news, hate speech and even radicalizing elements are part of this modern form of communication: Sometimes with far-reaching effects on individuals and societies. A basic understanding of these mechanisms and communication patterns could help to counteract negative forms of communication, e.g. bullying among children or extreme political points of view. To this end, a method will be presented in order to break down the underlying communication patterns, to trace individual users and to inspect their comments and range on social media platforms; Or to contrast them later on via qualitative research. This approeach can identify particularly active users with an accuracy of 100 percent, if the framing social networks as well as the topics are taken into account. However, methodological as well as counteracting approaches must be even more dynamic and flexible to ensure sensitivity and specifity regarding users who spread hate speech, fake news and radicalizing elements.