Skip to Main content Skip to Navigation
Journal articles

A Topic Modeling based Representation to Detect Tweet Locations: Example of the event "Je suis Charlie"

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.
Complete list of metadata
Contributor : Didier Josselin Connect in order to contact the contributor
Submitted on : Tuesday, January 5, 2016 - 6:57:20 AM
Last modification on : Thursday, August 4, 2022 - 5:00:03 PM

Links full text



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, Copernicus GmbH (Copernicus Publications), 2015, XL-3/W3, pp.629-634. ⟨10.5194/isprsarchives-XL-3-W3-629-2015⟩. ⟨hal-01250548⟩



Record views