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Open AccessArticle

Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping

1
Information Technology and Systems Management (ITS), Salzburg University of Applied Sciences, Urstein Sued 1, 5412 Puch/Hallein, Austria
2
Department of Geoinformatics—Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria
3
Center for Geographic Analysis, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138, USA
*
Authors to whom correspondence should be addressed.
Information 2020, 11(8), 376; https://doi.org/10.3390/info11080376
Received: 18 June 2020 / Revised: 21 July 2020 / Accepted: 22 July 2020 / Published: 25 July 2020
(This article belongs to the Special Issue Natural Language Processing for Social Media)
In the event of a natural disaster, geo-tagged Tweets are an immediate source of information for locating casualties and damages, and for supporting disaster management. Topic modeling can help in detecting disaster-related Tweets in the noisy Twitter stream in an unsupervised manner. However, the results of topic models are difficult to interpret and require manual identification of one or more “disaster topics”. Immediate disaster response would benefit from a fully automated process for interpreting the modeled topics and extracting disaster relevant information. Initializing the topic model with a set of seed words already allows to directly identify the corresponding disaster topic. In order to enable an automated end-to-end process, we automatically generate seed words using older Tweets from the same geographic area. The results of two past events (Napa Valley earthquake 2014 and hurricane Harvey 2017) show that the geospatial distribution of Tweets identified as disaster related conforms with the officially released disaster footprints. The suggested approach is applicable when there is a single topic of interest and comparative data available. View Full-Text
Keywords: topic modeling; social media; geospatial analysis; disaster management topic modeling; social media; geospatial analysis; disaster management
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MDPI and ACS Style

Ferner, C.; Havas, C.; Birnbacher, E.; Wegenkittl, S.; Resch, B. Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping. Information 2020, 11, 376. https://doi.org/10.3390/info11080376

AMA Style

Ferner C, Havas C, Birnbacher E, Wegenkittl S, Resch B. Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping. Information. 2020; 11(8):376. https://doi.org/10.3390/info11080376

Chicago/Turabian Style

Ferner, Cornelia; Havas, Clemens; Birnbacher, Elisabeth; Wegenkittl, Stefan; Resch, Bernd. 2020. "Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping" Information 11, no. 8: 376. https://doi.org/10.3390/info11080376

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