Misbehaviour Detection Algorithms and Application in Social Networks
- Publication Type:
- Thesis
- Issue Date:
- 2021
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The liberty to contribute content freely has encouraged malicious users to exploit the social platforms (i.e., social networks and e-commerce platforms) for their benefits. Spammers, rumours, and some other unexpected activities are almost an appendage to all social platforms that disrupt the network order. We summarize these unexpected activities as misbehaviours in social platforms. To detect such social platform misbehaviours, machine learning is an expected method where modelling and algorithms are two significant elements. Such an interesting topic that has application prospects and research value has attracted the attention of many researchers, and some results have also been put forward in the literature. In terms of spammer detection, due to the special characteristic, social networks call for relation-dependent but content-independent methods for spammer detection. To make up for the lack of existing research, this thesis proposes two methods for spammer detection on multi-relational social networks. First, “Send-Receive” Role Separable Graph-Embedding Model (RS-GEM) to extract and fuse the hidden features of heterogeneous relations in multi-relational social networks. Second, Multi-level Dependency Model (MDM), which exploits user’s behaviours in terms of long-term and short-term dependency from both individual-level and union-level. As for rumour analysis, before a rumour has an impact on social networks, we need to assess the possible impact it may have. Therefore, we devise a rumour influence prediction model RISM (Rumour Impact on Social Media) based on a popular rumour intensity formula to predict the impact of a newborn rumour. At last, since the global outbreak of COVID-19 in early 2019, COVID-19-related topics have become hot spots on social networking platforms. We analyze the COVID-19-related tweets on Twitter and get a preliminary understanding of the public's focus and sentiment trends during the pandemic.
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