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SOCIAL MEDIA MINING SHARED TASK WORKSHOP.

Abeed Sarker1, Azadeh Nikfarjam, Graciela Gonzalez

  • 1Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, United States of America diego.asu.edu, abeed.sarker@asu.edu.

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Summary
This summary is machine-generated.

This study addresses natural language processing challenges in public health using social media data. It details a shared task on adverse drug reaction detection and normalization from Twitter posts.

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Area of Science:

  • Public Health Informatics
  • Computational Linguistics
  • Social Media Analysis

Background:

  • Social media provides real-time data valuable for public health surveillance.
  • Challenges exist in processing domain-specific social media text for health insights.
  • Previous research has leveraged social media for monitoring diseases and medication effects.

Framework:

  • Focuses on three core natural language processing tasks: text classification, information extraction, and concept normalization.
  • Utilizes a shared task format to encourage global participation and diverse solutions.
  • Employs in-house annotated Twitter data specifically on adverse drug reactions.

Implementation:

  • Task 1: Automatic classification of user posts reporting adverse drug reactions.
  • Task 2: Extraction of specific adverse drug reaction mentions from social media text.
  • Task 3: Normalization of adverse drug reaction mentions to standardized concept identifiers.

Implications:

  • The study evaluates 11 participating teams and 24 system runs across the defined tasks.
  • Accepted 7 manuscripts detailing innovative approaches for publication.
  • Provides a benchmark and insights into solving NLP challenges for health-related social media data.