Artificial Intelligence on Social Media (AISoMe)

Task Description

Introduction & Motivation

Vaccination is important to minimize the risk and spread of various diseases. In recent years, vaccination has been a key step in countering the COVID-19 pandemic. Additionally, society-scale vaccination is needed to minimize the chances of various diseases including diseases in children, yearly outbreaks such as flu, and so on. However, many people are skeptical about the use of vaccines owing to various reasons, including the politics involved, potential side-effects of vaccines, etc. It is important to understand these various concerns towards vaccines, and social media can be used to gain a lot of data quickly about people talking about vaccines. 

Problem Statement

The goal is to build an effective multi-label classifier to label a social media post (particularly, a tweet) according to the specific concern(s) towards vaccines as expressed by the author of the post.
Note that a tweet can have more than one label (concern), e.g., a tweet expressing 3 different concerns towards vaccines will have 3 labels.

We consider the following concerns towards vaccines as the labels for the classification task:

1. Unnecessary: The tweet indicates vaccines are unnecessary, or that alternate cures are better.

2. Mandatory: Against mandatory vaccination — The tweet suggests that vaccines should not be made mandatory.

3. Pharma: Against Big Pharma — The tweet indicates that the Big Pharmaceutical companies are just trying to earn money, or the tweet is against such companies in general because of their history.

4. Conspiracy: Deeper Conspiracy — The tweet suggests some deeper conspiracy, and not just that the Big Pharma want to make money (e.g., vaccines are being used to track people, COVID is a hoax)

5. Political: Political side of vaccines — The tweet expresses concerns that the governments / politicians are pushing their own agenda though the vaccines.

6. Country: Country of origin — The tweet is against some vaccine because of the country where it was developed / manufactured

7. Rushed: Untested / Rushed Process — The tweet expresses concerns that the vaccines have not been tested properly or that the published data is not accurate.

8. Ingredients: Vaccine Ingredients / technology — The tweet expresses concerns about the ingredients present in the vaccines (eg. fetal cells, chemicals) or the technology used (e.g., mRNA vaccines can change your DNA)

9. Side-effect: Side Effects / Deaths — The tweet expresses concerns about the side effects of the vaccines, including deaths caused.

10. Ineffective: Vaccine is ineffective — The tweet expresses concerns that the vaccines are not effective enough and are useless.

11. Religious: Religious Reasons — The tweet is against vaccines because of religious reasons

12. None: No specific reason stated in the tweet, or some reason other than the given ones.

Announcement of  Winners

Winning Team 

• Name of team: AKCSIT ,  Participant Name: Aritra Mandal [ Affiliation: University of Calcutta]

Runner Up 

• Name of team: DataWiz,  Participant Name: Kaustav Das, Shruti Biswas [ Affiliation: Amity University, Kolkata]

• • 1. • Congratulations to the teams !!

       • We will distribute the certificate to the participants of the Winning team and the Runner Up team !!

        [ Participants must submit the working note and present the paper ]