Next Article in Journal
Dense RGB-D SLAM with Multiple Cameras
Next Article in Special Issue
End-User Feedback on a Low-Cost Portable Air Quality Sensor System—Are We There Yet?
Previous Article in Journal
A Visual Cortex-Inspired Imaging-Sensor Architecture and Its Application in Real-Time Processing
Previous Article in Special Issue
Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using 1 Regularization
Open AccessArticle

Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions

1
Department of Information Engineering, University of Pisa, 56122 Pisa, Italy
2
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2117; https://doi.org/10.3390/s18072117
Received: 10 May 2018 / Revised: 13 June 2018 / Accepted: 30 June 2018 / Published: 2 July 2018
(This article belongs to the Special Issue Social Sensing)
In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major terms used in posts. The occurrences of such terms are periodically sampled from the posts stream. The generated temporal series are processed by using marker-based stigmergy, i.e., a biologically-inspired mechanism performing scalar and temporal information aggregation. More precisely, each sample of the series generates a functional structure, called mark, associated with some concentration. The concentrations disperse in a scalar space and evaporate over time. Multiple deposits, when samples are close in terms of instants of time and values, aggregate in a trail and then persist longer than an isolated mark. To measure similarity between time series, the Jaccard’s similarity coefficient between trails is calculated. Discussion topics are generated by such similarity measure in a clustering process using Self-Organizing Maps, and are represented via a colored term cloud. Structural parameters are correctly tuned via an adaptation mechanism based on Differential Evolution. Experiments are completed for a real-world scenario, and the resulting similarity is compared with Dynamic Time Warping (DTW) similarity. View Full-Text
Keywords: microblog analysis; time series similarity; stigmergy; term cloud; receptive field microblog analysis; time series similarity; stigmergy; term cloud; receptive field
Show Figures

Figure 1

MDPI and ACS Style

Cimino, M.G.C.A.; Lazzeri, A.; Pedrycz, W.; Vaglini, G. Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions. Sensors 2018, 18, 2117.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop