scholarly journals Online social network data as sociometric markers.

2017 ◽  
Vol 72 (7) ◽  
pp. 668-678 ◽  
Author(s):  
Jens F. Binder ◽  
Sarah L. Buglass ◽  
Lucy R. Betts ◽  
Jean D. M. Underwood
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Network Data ◽  
Social Network Data

Privacy Preserving Approaches for Online Social Network Data Publishing

2021 ◽  
pp. 119-132
Author(s):  
Kamalkumar Macwan ◽  
Sankita Patel
Keyword(s):  
Social Network ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Online Social Network ◽  
Data Publishing ◽  
Network Data ◽  
User Privacy ◽  
Social Network Data ◽  
Huge Data ◽  
Popularity Prediction

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.

scholarly journals QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4882 ◽  
Author(s):  
Fernando Terroso-Saenz ◽  
Andres Muñoz ◽  
José Cecilia
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Online Social Networks ◽  
Road Traffic ◽  
Online Social Network ◽  
Network Data ◽  
Regression Problem ◽  
Factors Affecting ◽  
Demand Prediction ◽  
Social Network Data

Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.

scholarly journals A Comprehensive Survey of Time Series Anomaly Detection in Online Social Network Data

2017 ◽  
Vol 180 (3) ◽  
pp. 13-22 ◽  
Author(s):  
Md Rafiqul ◽  
Naznin Sultana ◽  
Mohammad Ali ◽  
Prohollad Chandra ◽  
Bushra Rahman
Keyword(s):  
Time Series ◽  
Social Network ◽  
Anomaly Detection ◽  
Online Social Network ◽  
Network Data ◽  
Social Network Data ◽  
Comprehensive Survey

Preserving Relation Privacy in Online Social Network Data

2011 ◽  
Vol 15 (3) ◽  
pp. 35-42 ◽  
Author(s):  
Na Li ◽  
Nan Zhang ◽  
Sajal Das
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Network Data ◽  
Social Network Data
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