Abstract
Enabling the analysis of behavioral disorders over time in social networks, can help in suicide prevention, (school) bullying detection and extremist/criminal activity prediction. In this paper, we present a novel data analytics pipeline to enable the analysis of patterns of behavioral disorders on social networks. We present a Social Behavior Graph (sbGraph) model, to enable the analysis of factors that are driving behavior disorders over time. We use the golden standards in personality, behavior and attitude to build a domain specific Knowledge Base (KB). We use this domain knowledge to design cognitive services to automatically contextualize the raw social data and to prepare them for behavioral analytics. Then we introduce a pattern-based word embedding technique, namely personality2vec, on each feature extracted to build the sbGraph. The goal is to use mathematical embedding from a space with a dimension per feature to a continuous vector space which can be mapped to classes of behavioral disorders (such as cyber-bullying and radicalization) in the domain specific KB. We implement an interactive dashboard to enable social network analysts to analyze and understand the patterns of behavioral disorders over time. We focus on a motivating scenario in Australian government’s office of the e-Safety commissioner, where the goal is to empowering all citizens to have safer, more positive experiences online.
Original language | English |
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Title of host publication | WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 825-828 |
Number of pages | 4 |
ISBN (Electronic) | 9781450368223 |
DOIs | |
Publication status | Published - 2020 |
Event | 13th ACM International Conference on Web Search and Data Mining, WSDM 2020 - Houston, United States Duration: 3 Feb 2020 → 7 Feb 2020 |
Conference
Conference | 13th ACM International Conference on Web Search and Data Mining, WSDM 2020 |
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Country/Territory | United States |
City | Houston |
Period | 3/02/20 → 7/02/20 |
Keywords
- Behavioural analytics
- Cognitive analytics
- Deep learning