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. 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.
Highlights
Microblogging is a broadcast process that allows users to exchange short sentences
For the sake of significance, we focus on the record highlighting the most important properties of the approach: a dataset of 188,607 Twitter posts collected during the terrorist attacks in Paris on 13 November 2015, between the 9 p.m. on the and the 2 a.m. on the (BBC News, 9 December 2015, Paris attacks: What happened on the night, http://www.bbc.com/news/world-europe34818994)
We have presented a novel approach to identifying event-specific social discussion topics from a stream of posts in microblogging
Summary
Microblogging is a broadcast process that allows users to exchange short sentences. These short messages are important sources of information and opinion about socially relevant events.Microblogging systems such as Twitter, Tumblr, and Weibo are increasingly used in everyday life.a huge number of informal, unstructured messages are produced in real time. Microblogging is a broadcast process that allows users to exchange short sentences These short messages are important sources of information and opinion about socially relevant events. Microblogging systems such as Twitter, Tumblr, and Weibo are increasingly used in everyday life. A term cloud is a straightforward means to represent the content of the discussion topics of a given event [8]. This is achieved by arranging the most frequent terms at the center of the term cloud, and positioning less frequent ones on the border, by using a font size proportional to the frequency [9]. It can be noticed that the terms police, victim, shooting and blood, related to the story, are mostly used in the first part of Sensors 2018, 18, 2117; doi:10.3390/s18072117 www.mdpi.com/journal/sensors
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