Detecting and locating trending places using multimodal social network data

Author:

Lucas LuisORCID,Tomás David,Garcia-Rodriguez Jose

Abstract

AbstractThis paper presents a machine learning-based classifier for detecting points of interest through the combined use of images and text from social networks. This model exploits the transfer learning capabilities of the neural network architecture CLIP (Contrastive Language-Image Pre-Training) in multimodal environments using image and text. Different methodologies based on multimodal information are explored for the geolocation of the places detected. To this end, pre-trained neural network models are used for the classification of images and their associated texts. The result is a system that allows creating new synergies between images and texts in order to detect and geolocate trending places that has not been previously tagged by any other means, providing potentially relevant information for tasks such as cataloging specific types of places in a city for the tourism industry. The experiments carried out reveal that, in general, textual information is more accurate and relevant than visual cues in this multimodal setting.

Funder

Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana

European Regional Development Fund

Universidad de Alicante

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

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1. Navigating the Multimodal Landscape: A Review on Integration of Text and Image Data in Machine Learning Architectures;Machine Learning and Knowledge Extraction;2024-07-09

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5. Scoping Review on Image-Text Multimodal Machine Learning Models;2023 International Conference on Computational Science and Computational Intelligence (CSCI);2023-12-13

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