A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data

• Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged tweets provides the ability to discover events and their locations. DBSCAN (density-based spatial clustering of applications with noise), which has been widely used to retrieve events from geotagged tweets, cannot efficiently detect clusters when there is significant spatial heterogeneity in the dataset, as it is the case for Twitter data where the distribution of users, as well as the intensity of publishing tweets, varies over the study areas. This study proposes VDCT (Varied Density-based spatial Clustering for Twitter data) algorithm that extracts clusters from geotagged tweets by considering spatial heterogeneity. The algorithm employs exponential spline interpolation to determine different search radiuses for cluster detection. Moreover, in addition to spatial proximity, textual similarities among tweets are also taken into account by the algorithm. In order to examine the efficiency of the algorithm, geotagged tweets collected during a hurricane in the United States were used for event detection. The output clusters of VDCT have been compared to those of DBSCAN. Visual and quantitative comparison of the results proved the feasibility of the proposed method. View Full-Text

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Multidisciplinär geovetenskap (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Geosciences, Multidisciplinary (hsv//eng)

Keyword

Density-based clustering

Spatial clustering

Spatial heterogeneity

Text Similarity

Twitter

Publication and Content Type

art (subject category)

ref (subject category)