使用R分析社交媒体网络数据：半自动筛选用户，评论和通信模式
Analysing Social Media Network Data with R: Semi-Automated Screening of Users, Comments and Communication Patterns
摘要：
社交媒体平台上的交流不仅在文化和政治上具有相关性，而且在各个社会中也越来越普遍。用户不仅通过社交媒体平台交流，还专门搜索信息，传播信息或自己发布信息。然而，假新闻、仇恨言论甚至激进元素都是这种现代传播形式的一部分：有时对个人和社会产生深远影响。对这些机制和沟通模式的基本了解有助于抵制消极形式的沟通，例如儿童之间的欺凌或极端的政治观点。为此，将提出一种方法，以打破潜在的通信模式，跟踪个人用户并检查他们在社交媒体平台上的评论和范围;或者稍后通过定性研究进行对比。如果将框架社交网络和主题考虑在内，这种方法可以以100%的准确率识别出特别活跃的用户。然而，方法论和反制措施必须更加动态和灵活，以确保对传播仇恨言论、假新闻和激进分子的用户的敏感性和特异性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
