Social Media Analytics using Apache Spark Application to Market Research

En este trabajo se intentará generar una herramienta de marketing de la que se pueda obtener información que puede no estar implícita en Instagram con la ayuda de Apache Spark y Apache Cassandra y con la que luego se podrán optimizar las campañas de publicidad que se hagan en esta red social

Automated curation of brand-related social media images with deep learning

This paper presents a work consisting in using deep convolutional neural networks (CNNs) to facilitate the curation of brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. When appropriate, we also apply object detection, usually to discover images containing logos. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic UGC curation.Peer ReviewedPostprint (author's final draft

Real-time logo detection in brand-related social media images

This paper presents a work consisting in using deep convolutional neural networks (CNNs) for real-time logo detection in brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with two CNNs designed for object detection, SSD InceptionV2 and Faster Atrous InceptionV4 (that provides better performance on small objects). We report experiments with 2 real brands, Estrella Damm and Futbol Club Barcelona. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic logo detection in UGC.This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P and by the SGR programme (2014- SGR-1051 and 2017-SGR-962) of the Catalan Government.Peer ReviewedPostprint (author's final draft

Social Media Analytics using Apache Spark Application to Market Research

En este trabajo se intentará generar una herramienta de marketing de la que se pueda obtener información que puede no estar implícita en Instagram con la ayuda de Apache Spark y Apache Cassandra y con la que luego se podrán optimizar las campañas de publicidad que se hagan en esta red social

Social Media Analytics using Apache Spark Application to Market Research

En este trabajo se intentará generar una herramienta de marketing de la que se pueda obtener información que puede no estar implícita en Instagram con la ayuda de Apache Spark y Apache Cassandra y con la que luego se podrán optimizar las campañas de publicidad que se hagan en esta red social

Automated curation of brand-related social media images with deep learning

This paper presents a work consisting in using deep convolutional neural networks (CNNs) to facilitate the curation of brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. When appropriate, we also apply object detection, usually to discover images containing logos. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic UGC curation.Peer Reviewe

More than 10,000 pre-Columbian earthworks are still hidden throughout Amazonia

Indigenous societies are known to have occupied the Amazon basin for more than 12,000 years, but the scale of their influence on Amazonian forests remains uncertain. We report the discovery, using LIDAR (light detection and ranging) information from across the basin, of 24 previously undetected pre-Columbian earthworks beneath the forest canopy. Modeled distribution and abundance of large-scale archaeological sites across Amazonia suggest that between 10,272 and 23,648 sites remain to be discovered and that most will be found in the southwest. We also identified 53 domesticated tree species significantly associated with earthwork occurrence probability, likely suggesting past management practices. Closed-canopy forests across Amazonia are likely to contain thousands of undiscovered archaeological sites around which pre-Columbian societies actively modified forests, a discovery that opens opportunities for better understanding the magnitude of ancient human influence on Amazonia and its current state