*** Apologies for multiple posting ***

Information Retrieval from Microblogs during Disasters (IRMiDis)
https://sites.google.com/view/irmidis-fire2022/irmidis

Track in conjunction with the Annual Conference of the Forum for Information Retrieval Evaluation (FIRE 2022 - http://fire.irsi.res.in/fire/2022/home), December 9-13, 2022, Kolkata (Hybrid Event)

The Information Retrieval from Microblogs during Disasters (IRMiDis) track aims to develop datasets and methods for solving various practical research problems associated with a disaster or pandemic situation. The IRMiDis track has been run successfully with FIRE in the years 2017, 2018 and 2021. This year IRMiDis will consist of two important classification tasks over microblogs/tweets associated with the COVID-19 pandemic.

*** Task 1: COVID-19 vaccine stance classification from tweets ***

It is important to understand the vaccine-stance of people in order to nudge people towards intake of COVID vaccines. With this motivation, this task aims to build an effective 3-class classifier on tweets with respect to the stance reflected towards COVID-19 vaccines. The 3 classes are:
(1) AntiVax - the tweet indicates hesitancy (of the user who posted the tweet) towards the use of vaccines.
(2) ProVax - the tweet supports / promotes the use of vaccines.
(3) Neutral - the tweet does not have any discernible sentiment expressed towards vaccines or is not related to vaccines

*** Task 2: Detection of COVID-19 symptom-reporting in tweets ***

Quickly identifying people who are experiencing COVID-19 symptoms is important for authorities to arrest the spread of the disease. In this task, we explore if tweets that report about someone experiencing COVID-19 symptoms (e.g., 'fever', 'cough') can be automatically identified. The task is to build an 4-class classifier on tweets that can detect tweets that report someone experiencing COVID-19 symptoms. The 4 classes are:
(1) Primary Reporting - The user (who posted the tweet) is reporting symptoms of himself/herself.
(2) Secondary Reporting - The user is reporting symptoms of some friend / relative / neighbour / someone they met.
(3) Third-party Reporting - The user is reporting symptoms of some celebrity / third-party person.
(4) Non-Reporting - The user is not reporting anyone experiencing COVID-19 symptoms, but talking about symptom-words in some other context or giving only general information about COVID-19 symptoms.

For both tasks, we will provide training data annotated by human workers, and test data for evaluating the submitted models. Details of how to participate are available at https://sites.google.com/view/irmidis-fire2022/irmidis.

*** Timeline *** 

June 6 -- open track website and training data release
July 15 -- test data release
August 1 -- run submission deadline
August 15 -- results declared
September 15 -- Working notes due
October 15 -- Camera ready copies of working notes and overview paper due

*** Organisers ***

Moumita Basu, Amity University Kolkata, India
Soham Poddar, Indian Institute of Technology Kharagpur, India
Saptarshi Ghosh, Indian Institute of Technology Kharagpur, India
Kripabandhu Ghosh, Indian Institute of Science Education and Research, Kolkata, India