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Authors: Mario G. C. A. Cimino 1 ; Federico Galatolo 1 ; Alessandro Lazzeri 1 ; Witold Pedrycz 2 and Gigliola Vaglini 1

Affiliations: 1 University of Pisa, Italy ; 2 University of Alberta, Canada

ISBN: 978-989-758-222-6

Keyword(s): Microblog Analytics, Spikiness Assessment, Computational Stigmergy, Term Cloud.

Related Ontology Subjects/Areas/Topics: Applications ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Health Engineering and Technology Applications ; Pattern Recognition ; Signal Processing ; Software Engineering

Abstract: A significant phenomenon in microblogging is that certain occurrences of terms self-produce increasing mentions in the unfolding event. In contrast, other terms manifest a spike for each moment of interest, resulting in a wake-up-and-sleep dynamic. Since spike morphology and background vary widely between events, to detect spikes in microblogs is a challenge. Another way is to detect the spikiness feature rather than spikes. We present an approach which detects and aggregates spikiness contributions by combination of spike patterns, called archetypes. The soft similarity between each archetype and the time series of term occurrences is based on computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Archetypes are arranged into an architectural module called Stigmergic Receptive Field (SRF). The final spikiness indicator is computed through linear combination of SRFs, whose weights are determined with the Least Square Error minimization on a spikiness train ing set. The structural parameters of the SRFs are instead determined with the Differential Evolution algorithm, minimizing the error on a training set of archetypal series. Experimental studies have generated a spikiness indicator in a real-world scenario. The indicator has enhanced a cloud representation of social discussion topics, where the more spiky cloud terms are more blurred. (More)

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Paper citation in several formats:
Cimino, M.; Galatolo, F.; Lazzeri, A.; Pedrycz, W. and Vaglini, G. (2017). Spikiness Assessment of Term Occurrences in Microblogs: An Approach based on Computational Stigmergy.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 731-737. DOI: 10.5220/0006253807310737

@conference{icpram17,
author={Mario G. C. A. Cimino. and Federico Galatolo. and Alessandro Lazzeri. and Witold Pedrycz. and Gigliola Vaglini.},
title={Spikiness Assessment of Term Occurrences in Microblogs: An Approach based on Computational Stigmergy},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={731-737},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006253807310737},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Spikiness Assessment of Term Occurrences in Microblogs: An Approach based on Computational Stigmergy
SN - 978-989-758-222-6
AU - Cimino, M.
AU - Galatolo, F.
AU - Lazzeri, A.
AU - Pedrycz, W.
AU - Vaglini, G.
PY - 2017
SP - 731
EP - 737
DO - 10.5220/0006253807310737

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