The tremendous growth of event dissemination over social networks makes it very
challenging to accurately discover and track exciting events, as well as their evolution and …

Self-supervised representation learning (SSRL) concerns the problem of learning a useful
data representation without the requirement for labelled or annotated data. This …

Event detection from social media aims at extracting specific or generic unusual
happenings, such as, family reunions, earthquakes, and disease outbreaks, among others …

Geographical entity representation learning (GERL) aims to embed geographical entities
into a low-dimensional vector space, which provides a generalized approach for utilizing …

Event detection from social media aims at extracting specific or generic unusual
happenings, such as, family reunions, earthquakes, and disease outbreaks, among others …

Point Of Interest (POI) categorization is to group POIs into several categories and make them
easy-to-use in geospatial applications. Previous studies mainly used geospatial features …

Discovering and clustering similar trajectories is a cornerstone task for movement pattern
analysis and location prediction in applications like ride-sharing, supply-chain, maps and …

4.1. What types of representations were learnt? 13 In this section, for each individual data
type, we state the number of articles that 14 considered the problem of learning …

Resumo Pontos de Interesse (POIs) são locais específicos, como restaurantes, shoppings e
parques, considerados relevantes para os usuários. Representar seus tipos por meio de …

One of the most important ways of combating climate change is to better understand the way
that polluting gas levels are geographically distributed. We characterize cities by the amount …