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Fuzzy Modelling for Human Dynamics Based on Online Social Networks
Jesus Cuenca-Jara1, Fernando Terroso-Saenz2, Mercedes Valdes-Vela3
1Department of Communications and Information Engineering, University of Murcia, Murcia 30100, Spain. jesus.cuenca1@um.es.
Sensors (Basel, Switzerland)
|August 25, 2017
Summary
This study introduces a fuzzy logic framework for human mobility mining using online social network data. It enhances location prediction accuracy by addressing data noise and inaccuracy in urban environments.
Area of Science:
- Data Mining
- Urban Computing
- Computational Social Science
Background:
- Online Social Networks (OSN) offer valuable location data for human mobility mining.
- Human-generated data from OSNs is often noisy and inaccurate, posing challenges for mobility modeling.
- Innovative urban services require accurate human mobility models.
Purpose of the Study:
- To develop a novel framework for human mobility mining using fuzzy logic.
- To address data noise and inaccuracy in OSN-derived location data.
- To create a location prediction service based on the proposed framework.
Main Methods:
- Fuzzy clustering algorithm to identify active OSN areas across different time periods.
- Composition of mobility patterns using extracted fuzzy clusters.
- Development of a location prediction service utilizing a fuzzy rule classifier.
Main Results:
- Successfully extracted active OSN areas and composed mobility patterns.
- Developed and tested a fuzzy logic-based location prediction service.
- Demonstrated framework effectiveness using Twitter and Flickr data in two major cities.
Conclusions:
- The fuzzy logic framework effectively mines human mobility patterns from noisy OSN data.
- The proposed approach improves the accuracy of location prediction services in urban settings.
- This framework provides a robust method for leveraging OSN data for urban mobility insights.
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