Analysing Social Media Network Data with R: Semi-Automated Screening of Users, Comments and Communication Patterns

• URL: http://arxiv.org/abs/2011.13327v1
• Date: Thu, 26 Nov 2020 14:52:01 GMT
• Title: Analysing Social Media Network Data with R: Semi-Automated Screening of Users, Comments and Communication Patterns
• Authors: Dennis Klinkhammer
• Abstract summary: Communication on social media platforms is increasingly widespread across societies. Fake news, hate speech and radicalizing elements are part of this modern form of communication. A basic understanding of these mechanisms and communication patterns could help to counteract negative forms of communication.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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