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
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 trai
ning 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.
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