@inproceedings{2cb02eca176c491b999bc69ddec9e0e2,
title = "A general framework for event detection from social media",
abstract = "The availability of accurate and/or up-to-date mass data can stimulate the development of innovative approaches for the assessment of spatio-temporal processes. However, extracting meaningful information from these collections of user-generated data is a challenge. Event detection is an interesting concept in the era of Web 2.0 and ubiquitous Internet. Various existing event-detection algorithms share a very simple, yet powerful architecture model; pipes-&-filters. Using this model, the authors in this study developed a generic and extensible programming framework to find meaningful patterns out of heterogeneous and unstructured online data streams. The framework supports researchers with adapters to different social media platforms, optional preprocessing steps. Its graphical user interface supports users with an interactive graphical environment for setting up parameters and evaluating the results through maps, 3D visualization, and various charts. The framework has been successfully tested on Flicker and Instagram platforms for different time periods and locations to detect latent events.",
keywords = "Datastream mining, Event detection, Flicker, Instagram, Knowledge discovery, Social media",
author = "Khatereh Polous and Andr{\'e} Freitag and Jukka Krisp and Liqiu Meng and Smita Singh",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 16th International Symposium on Spatial Data Handling, SDH 2014 ; Conference date: 06-10-2014 Through 08-10-2014",
year = "2015",
doi = "10.1007/978-3-319-19950-4_6",
language = "English",
isbn = "9783319199498",
series = "Advances in Geographic Information Science",
publisher = "springer berlin",
pages = "85--105",
editor = "Yee Leung and Francis Harvey",
booktitle = "Advances in Spatial Data Handling and Analysis - Select Papers from the 16th IGU Spatial Data Handling Symposium",
}