Abstract
With the growing impact of social networks in our daily lives, current trends towards reducing the gap between them and the physical world become more prominent. In this context, some geolocation solutions have recently been developed in order to determine the geographical appurtenance data on those networks. The majority of proposed geolocation strategies is composed of machine learning and neural network-based models. The latter are considered as black boxes, which means the complexity of understanding their structures and their behaviors by humans. This limitation is emphasized by the need to generate interpretable and explainable outputs. In this paper, we demonstrate explainability on the predictions made by an advanced neural-network-based model for tweets geolocation using LIME, a state of the art open source explainability technique. Experiences performed on a set of geotagged tweets showed the aggregation of LIME’s explanations with the geolocation model’s results by 78% when dealing with two English variants.
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Hasni, S., Faiz, S. (2022). An Explainable Predictive Model for the Geolocation of English Tweets. In: Bennour, A., Ensari, T., Kessentini, Y., Eom, S. (eds) Intelligent Systems and Pattern Recognition. ISPR 2022. Communications in Computer and Information Science, vol 1589. Springer, Cham. https://doi.org/10.1007/978-3-031-08277-1_18
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