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Journal Articles International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Year : 2015

A Topic Modeling based Representation to Detect Tweet Locations

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.

Dates and versions

hal-01250548 , version 1 (05-01-2016)

Identifiers

Cite

Mohamed Morchid, Didier Josselin, Y. Portilla, R. Dufour, E. Altman, et al.. A Topic Modeling based Representation to Detect Tweet Locations: Example of the event "Je suis Charlie". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, XL-3/W3, pp.629-634. ⟨10.5194/isprsarchives-XL-3-W3-629-2015⟩. ⟨hal-01250548⟩
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