PSB 2016 Social Media Mining Shared Task Workshop



Workshop Overview:

This workshop was designed to act as a platform for the application of state-of-the-art natural language processing systems on social media data. Using the DIEGO Lab Adverse Drug Reaction Twitter corpus, we prepared a shared task focusing on text classification and information extraction. Eleven teams participated in the shared task and several teams will be presenting their systems at the workshop. This workshop complements the Social Media Mining for Public Health Monitoring and Surveillance session.

Date: January 8, 2016. Friday.

Time: 12:30 3.30 PM



12.30 12.40

Introduction to the shared task and workshop.

12.40 12.55

Overview of the data, systems, results and ranks. Abeed Sarker (Arizona State University)

12.55 - 1.10

Detecting Signals in Noisy Data Can Ensemble Classifiers Help Identify Adverse Drug Reaction in Tweets. Majid Rastegar Mojarad (Mayo Clinic)

1.10 1.25

READ-BioMed-SS: Adverse drug reaction classification of microblogs using emotional and conceptual enrichment. Karin Verspoor (University of Melbourne)

1.25 1.40

Binary Classification of Twitter Posts for Adverse Drug Reactions. Dai Hong-Jie (National Taitung University)

1.40 1.55


1.55 - 2.15

Information extraction techniques for social media data. Azadeh Nikfarjam (Arizona State University)

2.15 2.30

NTTMUNSW System for Adverse Drug Reactions Extraction in Twitter Data. Dai Hong-Jie (National Taitung University)

2.30 3.30

Discussion session: Future tasks in social media based pharmacovigilance

Accepted Papers:

The following 7 system descriptions were accepted for publication in the online proceedings:

  1. M. Rastegar-Mojarad, R. K. Elayavilli, Y. Yu, and H Liu. Detecting signals in noisy data - can ensemble classifiers help identify adverse drug reaction in Tweets? [ pdf ]
  2. Z. Zhang, J.-Y. Nie, and X. Zhang, An ensemble method for binary classification of adverse drug reactions from social media [ pdf ]
  3. B. Ofoghi, S. Siddiqui, and K. Verspoor. READ-BioMed-SS: Adverse drug reaction classification of microblogs using emotional and conceptual enrichment. [ pdf ]
  4. J. Jonnagaddala, T. R. Jue, and H.-J. Dai, Binary classification of Twitter posts for adverse drug reactions [ pdf ]
  5. D. Egger, F. Uzdilli, M. Cieliebak, and L. Derczynski, Adverse Drug Reaction Detection using an adapted Sentiment Classifier. [ pdf ]
  6. W. Wang, Mining adverse drug reaction mentions in twitter with word embeddings. [ pdf ]
  7. C.-K. Wang, H.-J. Dai, J. Jonnagaddala, T. R. Jue, O. Singh, U. Iqbal and J. Y.-C. Li, NT- TUMUNSW system for adverse drug reactions extraction in Twitter data. [ pdf ]





DIEGO LAB 2015 Competition Organizers.