Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks
This addresses the social computing task of individual attribute inference for applications like link prediction and community detection, but it is incremental as it builds on existing neural network methods for representation learning.
The paper tackled the problem of inferring latent attributes of people online by integrating heterogeneous sources of information from social media, resulting in improved performance at predicting gender, occupation, location, and friendships on Twitter.
Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web. We propose learning individual representations of people using neural nets to integrate rich linguistic and network evidence gathered from social media. The algorithm is able to combine diverse cues, such as the text a person writes, their attributes (e.g. gender, employer, education, location) and social relations to other people. We show that by integrating both textual and network evidence, these representations offer improved performance at four important tasks in social media inference on Twitter: predicting (1) gender, (2) occupation, (3) location, and (4) friendships for users. Our approach scales to large datasets and the learned representations can be used as general features in and have the potential to benefit a large number of downstream tasks including link prediction, community detection, or probabilistic reasoning over social networks.