Social media data: Flickr and Twitter metadata for eight mountain parks in Spain and Portugal

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Authors: Domingo Alcaraz‐Segura, Ana del Águila, Akram Elghouat, Rohaifa Khaldi, Javier Martínez‐López, Daniel Molina Cabrera, Carlos J. Navarro, José Carlos Pérez-Girón, Nuria Pistón, Ricardo A. Moreno-Llorca, Francisco Javier Rodríguez Díaz, Andrea Ros‐Candeira
+2 more Siham Tabik, Regino Zamora Rodriguez
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Abstract

This dataset contains a collection of geolocated photos and their associated metadata, which were retrieved from two social media platforms: Flickr (241,582 photos) and Twitter/X (1,035,488 photos) downloaded for the period 2015 - 2022. The metadata includes information such as geographic coordinates, timestamps, anonymised user identifiers and platform-specific attributes (e.g. tags, text captions, image URLs and retweets/favourites for Twitter). The data were collected for eight mountain national parks located in Spain and Portugal: Aigüestortes, Sierra de Guadarrama, Ordesa, Peneda-Gerês, Picos de Europa, Sierra de las Nieves, Sierra Nevada and Teide. These databases were subsequently labeled using artificial intelligence models and expert human labelers. Table 1. Table of contents of the dataset Folder format Description Flickr Database metadata Flickr_database .csv This CSV file contains metadata associated with 241,582 geolocated photos retrieved from the Flickr platform Twitter Database metadata Twitter_database .csv This CSV file contains metadata associated with 1,035,488 geolocated photos retrieved from the Twitter platform Scripts to download photos Flickr Download Flickr .py Python script used to download geolocated photos from the Flickr platform (publicly accessible via API). The script implements a grid-based approach (1 km resolution) to spatially structure and optimize the download process, avoiding API rate limits and redundancies. Twitter Download Twitter .py Python script used to download geolocated photos from the Twitter/X platform (currently restricted). The script implements a grid-based approach (1 km resolution) to spatially structure and optimize the download process, avoiding API rate limits and redundancies.

Fields of Study & Subjects

Human Mobility and Location-Based Analysis Geographic Information Systems Studies Species Distribution and Climate Change

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
2 February 2026
Document type
Dataset