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

Zeinab Ghaemi, Mahdi Farnaghi

Research output: Contribution to journalArticlepeer-review

18 Citations (SciVal)


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
Original languageEnglish
Article number82
JournalISPRS International Journal of Geo-Information
Issue number2
Publication statusPublished - 2019 Feb 13

Subject classification (UKÄ)

  • Geosciences, Multidisciplinary


  • Density-based clustering
  • Spatial clustering
  • Spatial heterogeneity
  • Text Similarity
  • Twitter


Dive into the research topics of 'A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data'. Together they form a unique fingerprint.

Cite this