Introduction: Microblogging sites like Twitter are increasingly being used for aiding relief operations during various mass emergencies. A lot of critical situational information is posted on microblogging sites during disaster events. However, messages posted on microblogging sites often contain rumors and overstated facts. In such situations, identification of claims or fact-checkable tweets, i.e., tweets that report some relevant and verifiable fact (other than sympathy or prayer) is extremely important for effective coordination of post-disaster relief operations.
Problem Satatement: Identifying claims or fact-checkable tweets
Here the participants need to develop automatic methodologies for identifying fact-checkable tweets. This task can be viewed as a binary classification problem, where tweets are classified into two classes – claims or fact-checkable tweets and non-fact-checkable tweets. However, apart from classification, the problem of identifying fact-checkable tweets can also be viewed as an IR problem where the goal of the participants would be to produce a ranked list of tweets based on their worthiness for fact checking. Additionally, It can also be posed as a pattern matching problem.
Following are some examples of claim/fact-checkable tweets and non-fact-checkable tweets from the dataset that consists of about 11,000 tweets posted during the 2015 Nepal earthquake .
Example of claims or fact-checkable tweets
ibnlive:Nepal earthquake: Tribhuvan International Airport bans landing of big aircraft [url]
#Nepal #Earthquake day four. Slowly in the capital valley Internet and electricity beeing restored . A relief for at least some ones
@mashable some pictures from Norvic Hospital *A Class Hospital of nepal* Patients have been put on parking lot.
@ Refugees: UNHCR rushes plastic sheeting and solar-powered lamps to Nepal earthquake survivors [url]
@ siromanid: Many temples in UNESCO world heritage site Bhaktapur Durbar Square have been reduced 2 debris after recent earthquake [url]
@SamitLive: Nepal has requested for Drinking water. @RailMinIndia has decided to send 1 Lak liter of Rail Neer over night.
Examples of non-fact-checkable tweets
Students of Himalayan Komang Hostel are praying for all beings who lost their life after earthquake!!! Please do...[url]
We humans need to come up with a strong solution to create earthquake proof zone's.
really sad to hear about d earthquake. praying for all the ppl who suffered & lost their loved ones. hope they get all the h…
@Gurmeetramrahim Msg helps earthquake victims
Nepal earthquake Students light candles offer prayers for victims: Students in Amritsar led a candle light vig...
I am so deeaking scared omg i dont even know what should i tweet.. This could possibly be my last tweet if the earthquake doesnt stop
Introduction: The COVID-19 pandemic is showing no signs of stopping and the only long term remedy to the disease seems to be through vaccination. However, quite a few people are sceptical about the use of vaccines owing to various reasons, including the politics involved and the fact that vaccines have been rushed into production. It becomes 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.
Problem Statement: Build an effective classifier for 3-class classification on tweets regarding their stance towards COVID-19 vaccines.
The 3 classes are described below:
AntiVax - the tweet is against 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
Note:
*Participants may take part in Task 1 or in Task 2 or in both the tasks.
* There can be at most 4 participants in a team.