Inferring latent attributes of Twitter users with label regularization

@inproceedings{Ardehaly2015InferringLA,
  title={Inferring latent attributes of Twitter users with label regularization},
  author={Ehsan Mohammady Ardehaly and Aron Culotta},
  booktitle={HLT-NAACL},
  year={2015}
}
Inferring latent attributes of online users has many applications in public health, politics, and marketing. Most existing approaches rely on supervised learning algorithms, which require manual data annotation and therefore are costly to develop and adapt over time. In this paper, we propose a lightly supervised approach based on label regularization to infer the age, ethnicity, and political orientation of Twitter users. Our approach learns from a heterogeneous collection of soft constraints… CONTINUE READING

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