task is to propose models for detecting writers’ stances (Favor, Against, or Non

task is to propose models for detecting writers’ stances (Favor, Against, or None) towards three selected topics (COVID-19 vaccine, digital transformation, and women empowerment). Participants can approach the stance detection task through single-task or multi-task learning (MTL). Single-task learning-based models depend only on the stance data for model development and training. MTL-based models can use other information, such as the sentiment and sarcasm of each tweet, to boost the performance of the stance detection system.
Classes
The possible stance labels are:
FAVOR means that we can infer from the post that the author supports the target (e.g., explicitly supporting the target or something aligned with the target, or if the post contains information such as news, a quote, a story, which reveals that the author is in favor of the target).
AGAINST means that we can infer from the tweet that the author is against the target (e.g., explicitly opposing the target or something aligned with the target, or if the post contains information such as news, a quote, a story, which reveals that the author is against the target).
NONE means that the tweet provides no hint as to the author’s stance toward the target (e.g., there is no evidence in the tweet to judge the author’s stance, such as inquiries, or news that does not express any positive or negative position).