Dynamic Modeling of Twitter Users

Ahmed Galal, Abeer El-Korany


Social Networks are popular platforms for users to express themselves, facilitate interactions, and share knowledge. Today, users in social networks have personalized profiles that contain their dynamic attributes representing their interest and behavior over time such as published content, and location check-ins. Several proposed models emerged that analyze those profiles with their dynamic content in order to measure the degree of similarity between users. This similarity value can be further used in friend suggesting and link prediction. The main drawback of the majority of these models is that they rely on a static snapshot of attributes which do not reflect the change in user interest and behavior over time. In this paper a novel framework for modeling the dynamic of user’s behavior and measuring the similarity between users’ profiles in twitter is proposed. In this proposed framework, dynamic attributes such as topical interests and the associated locations in tweets are used to represent user’s interest and behavior respectively. Experiments on a real dataset from twitter showed that the proposed framework that utilizes those attributes outperformed multiple standard models that utilize a static snapshot of data.


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Paper Citation

in Harvard Style

Galal A. and El-Korany A. (2015). Dynamic Modeling of Twitter Users . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-097-0, pages 585-593. DOI: 10.5220/0005346105850593

in Bibtex Style

author={Ahmed Galal and Abeer El-Korany},
title={Dynamic Modeling of Twitter Users},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Dynamic Modeling of Twitter Users
SN - 978-989-758-097-0
AU - Galal A.
AU - El-Korany A.
PY - 2015
SP - 585
EP - 593
DO - 10.5220/0005346105850593