research-article

personality2vec: Enabling the Analysis of Behavioral Disorders in Social Networks

Authors:

Amin Beheshti,

Vahid Moraveji-Hashemi,

Shahpar Yakhchi,

Hamid Reza Motahari-Nezhad,

Seyed Mohssen Ghafari,

Jian YangAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 825 - 828

https://doi.org/10.1145/3336191.3371865

Published: 22 January 2020 Publication History

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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.

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personality2vec: Enabling the Analysis of Behavioral Disorders in Social Networks

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Published In

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

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Association for Computing Machinery

New York, NY, United States

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Published: 22 January 2020

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AI-enabled Processes (AIP) Research Centre at Macquarie University

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

TX, Houston, USA

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https://doi.org/10.1109/ICAIT61638.2024.10690362
Bao EPérez AParapar J(2024)Explainable depression symptom detection in social mediaHealth Information Science and Systems10.1007/s13755-024-00303-912:1Online publication date: 6-Sep-2024
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Khan AAli R(2024)Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social mediaSocial Network Analysis and Mining10.1007/s13278-024-01205-014:1Online publication date: 4-Apr-2024
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Social media use among adults with autism spectrum disorders

Children's Aspirations and Emotional and Behavioural Problems

Social and delay discounting in a behavioral economic analysis of bystanders’ helping cyberbullying victims: The moderating role of gender

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