Behavior prediction based on interest characteristic and user communication in opportunistic social networks

2021 ◽  
Vol 14 (2) ◽  
pp. 1006-1018
Author(s):  
Jia Wu ◽  
Jingge Qu ◽  
Genghua Yu
Keyword(s):  
Social Networks ◽  
Behavior Prediction

Predicting Personality Traits, Gender and Psychopath Behavior of Twitter Users

2014 ◽  
Vol 5 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Hasan Ali AL Akram ◽  
Amjad Mahmood
Keyword(s):  
Social Networks ◽  
Personality Traits ◽  
Social Networking Sites ◽  
Information Exchange ◽  
Big Five Personality ◽  
Behavior Prediction ◽  
New Ideas ◽  
Twitter Users ◽  
Available Information ◽  
Insight Into

Social networking sites, such as Facebook and Twitter, are quickly becoming one of the most popular tools for social interaction and information exchange. Users of social networks reveal a lot about themselves in their public profiles, photos and status updates. While, social networks request users to create a truthful representation of themselves, they actually do so with a varying degree of accuracy. Depending on their privacy attitudes, the users may choose not to share details they find sensitive or tend to provide fake information. Contrary to a number of previous studies to predict the personality traits of the users of social networks primarily based on the users' profiles and other publically available information, this study provides an insight into the personality traits and psychopath behavior of twitter users by analyzing the tweets. The authors predict personality traits along the dimensions of “Big Five” personality model, gender and psychopath behavior of Twitter users. The paper discusses our data collection, gender, personality traits and psychopathic behavior prediction tool. It presents the analysis results of 327672 tweets of 345 users. The results show that there are more male users than the female users (70% male and 30% female). The results also show that majority of Twitter users are open to new ideas, are more agreeable and conscientious in nature but are less extravert. Out of 345 users, nine were indicating psychopath behavior and show less neuroticism. The authors also present a comparison of our personality traits' results with the results of two other similar studies.

scholarly journals Social behavior prediction with graph U-Net+

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyue Yan ◽  
Wenming Cao ◽  
Jianhua Ji
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Feature Extraction ◽  
Binary Classification ◽  
Representation Learning ◽  
Graph Representation ◽  
Behavior Prediction ◽  
Graph Node ◽  
Future Behavior ◽  
Decoder Architecture

AbstractWe focus on the problem of predicting social media user’s future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. We propose an encoder–decoder architecture based on graph U-Net, known as the graph U-Net+. In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization approaches into both encoding and decoding blocks. We reuse four datasets from DeepInf and extensive experimental results demonstrate that our methods achieve better performance than previous models.

User behavior prediction in social networks using weighted extreme learning machine with distribution optimization

2019 ◽  
Vol 93 ◽  
pp. 1023-1035 ◽  
Author(s):  
Xiong Luo ◽  
Changwei Jiang ◽  
Weiping Wang ◽  
Yang Xu ◽  
Jenq-Haur Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Extreme Learning Machine ◽  
User Behavior ◽  
Behavior Prediction ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine

scholarly journals Ontology-based deep learning for human behavior prediction with explanations in health social networks

2017 ◽  
Vol 384 ◽  
pp. 298-313 ◽  
Author(s):  
Nhathai Phan ◽  
Dejing Dou ◽  
Hao Wang ◽  
David Kil ◽  
Brigitte Piniewski
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Human Behavior ◽  
Behavior Prediction
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