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This article presents a novel approach for learning low-dimensionaldistributed representations of users in online social networks. Existingmethods rely on the network structure formed by the social relationships amongusers to extract these representations. However, the network information can beobsolete, incomplete or dynamically changing. In addition, in some cases, itcan be prohibitively expensive to get the network information. Therefore, wepropose an alternative approach based on observations from topics being talkedon in social networks. We utilise the time information of users adopting topicsin order to embed them in a real-valued vector space. Through extensiveexperiments, we investigate the properties of the representations learned andtheir efficacy in preserving information about link structure among users. Wealso evaluate the representations in two different prediction tasks, namely,predicting most likely future adopters of a topic and predicting thegeo-location of users. Experiments to validate the proposed methods areperformed on a large-scale social network extracted from Twitter, consisting ofabout 7.7 million users and their activity on around 3.6 million topics over amonth-long period.

Learning User Representations in Online Social Networks using Temporal Dynamics of Information Diffusion