Community Member Retrieval on Social Media Using Textual Information

This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive labeled examples defines the community. The solution introduces an unsupervised proxy task for learning user embeddings: user re-identification. Experiments with 16 different communities show that the resulting embeddings are more effective for community membership identification than common unsupervised representations.

• Publication year

  2018

• Venue

  North American Chapter of the Association for Computational Linguistics

• Publication date

  2018-04-16

• Fields of study

  Computer Science

• Identifiers
  DOI 10.18653/v1/N18-2094arXiv 1804.05499
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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