Task 1: COVID-19 vaccine stance classification from tweets
The only long term remedy for the COVID-19 pandemic seems to be through society-scale vaccination. However, quite a few people are skeptical about the use of vaccines owing to various reasons, including the politics involved and the fact that vaccines have been rushed into production. It is important to understand public sentiments towards vaccines, and social media can be used to gain a lot of data quickly about people talking about vaccines. Building an effective classifier to predict the user stance (towards vaccines) from social media posts (e.g., microblogs) becomes a crucial first step in any kind of analysis towards vaccine stance.
Build an effective classifier for 3-class classification on tweets with respect to the stance reflected towards COVID-19 vaccines. The 3 classes are described below:
AntiVax - the tweet indicates hesitancy (of the user who posted the tweet) towards the use of vaccines.
ProVax - the tweet supports / promotes the use of vaccines.
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 specifically explore if tweets that report about someone experiencing COVID-19 symptoms (e.g., ‘fever’, ‘cough’) can be automatically identified. We call such tweets symptom-reporting tweets. Note that, simply identifying tweets that contain mentions of COVID-19 symptoms is not helpful, since these tweets can contain lots of irrelevant information. For instance, a tweet mentioning “weekend football fever” contains the symptom-word “fever” but is clearly not a symptom-reporting tweet. Again, a tweet giving just general information about potential symptoms of COVID-19 is not a symptom-reporting tweet. In fact, our analyses show that a very large majority of tweets that include COVID-symptom words are not symptom-reporting tweets, i.e., these tweets do not inform about some person experiencing COVID-19 symptoms. Thus it is important to build an effective classifier to understand which tweets actually inform about someone experiencing COVID-19 symptoms.
Build an effective classifier for 4-class classification on tweets that can detect tweets that report someone experiencing COVID-19 symptoms. The 4 classes are described below:
Primary Reporting - The user (who posted the tweet) is reporting symptoms of himself/herself.
Secondary Reporting - The user is reporting symptoms of some friend / relative / neighbour / someone they met.
Third-party Reporting - The user is reporting symptoms of some celebrity / third-party person.
Non-Reporting - The user is not reporting anyone experiencing COVID-19 symptoms, but talking about symptom-words in some other context. This class includes tweets that only give general information about COVID-19 symptoms, without specifically reporting about a person experiencing such symptoms.
Announcement of Winners
Task 1
Winning Team
Name of team: Data@IITD, Participant Name: Shivangi Bithel [ Affiliation: IIT Delhi ]
Runner Up
Name of team: IREL, Participant Name: Sumanth Balaji , Sagar Joshi, Aditya Hari [ Affiliation: IIIT Hyderabad]
Task 2
Winning Team
Name of team: Data@IITD, Participant Name: Shivangi Bithel [ Affiliation: IIT Delhi ]
Runner Up
Name of team: Thapar Programmers, Participant Name: Akshit Bansal , Dr. Jatin Bedi, Rohit Jain [ Affiliation: Thapar Institute of Engineering and Technology, Patiala ]
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 ]