Paper draft - please export an up-to-date reference from http://www.iet.unipi.it/m.cimino/pub Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions 1, 1 2 1 Mario G. C. A. Cimino *, Alessandro Lazzeri , Witold Pedrycz and Gigliola Vaglini 1 Department of Information Engineering, University of Pisa, 56122 Pisa, Italy;
[email protected] (A.L.);
[email protected] (G.V.) 2 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada;
[email protected] * Correspondence:
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[email protected]; Tel.: +39-050-221-7455 Abstract: 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.