The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Citation
Articles | Volume XL-3/W3
https://doi.org/10.5194/isprsarchives-XL-3-W3-629-2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-629-2015
03 Sep 2015
 | 03 Sep 2015

A TOPIC MODELING BASED REPRESENTATION TO DETECT TWEET LOCATIONS. EXAMPLE OF THE EVENT ”JE SUIS CHARLIE”

M. Morchid, D. Josselin, Y. Portilla, R. Dufour, E. Altman, and G. Linarès

Keywords: Tweets location, Topic modeling, Author topic model, Twitter

Abstract. Social Networks became a major actor in information propagation. Using the Twitter popular platform, mobile users post or relay messages from different locations. The tweet content, meaning and location, show how an event-such as the bursty one ”JeSuisCharlie”, happened in France in January 2015, is comprehended in different countries. This research aims at clustering the tweets according to the co-occurrence of their terms, including the country, and forecasting the probable country of a non-located tweet, knowing its content. First, we present the process of collecting a large quantity of data from the Twitter website. We finally have a set of 2,189 located tweets about “Charlie”, from the 7th to the 14th of January. We describe an original method adapted from the Author-Topic (AT) model based on the Latent Dirichlet Allocation (LDA) method. We define an homogeneous space containing both lexical content (words) and spatial information (country). During a training process on a part of the sample, we provide a set of clusters (topics) based on statistical relations between lexical and spatial terms. During a clustering task, we evaluate the method effectiveness on the rest of the sample that reaches up to 95% of good assignment. It shows that our model is pertinent to foresee tweet location after a learning process.