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HRCE: Detecting Food Security Events in Social Media

Journal of Physics: Conference Series, ISSN: 1742-6596, Vol: 1437, Issue: 1
2020
  • 3
    Citations
  • 0
    Usage
  • 11
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    3
    • Citation Indexes
      3
  • Captures
    11

Conference Paper Description

Analyzing food security events shared on social networks not only helps people deepen their understanding of food security events, but also helps managers cope with these events. In this paper, we propose a model that utilizes task-specific features and a deep learning model to detect food security events from tweets, called HRCE. Specifically, the proposed model leverages a hierarchical Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) that takes word embeddings as inputs, and combines contextual embeddings to identify food security events from social media. We collected a novel food security related dataset from Twitter, and manually annotated 2,418 tweets. We conducted experiments on this dataset and concluded that HRCE outperforms baseline methods in terms of precision, recall and F1-score.

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