WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
This work addresses the need for filtering relevant COVID-19 information on social media, but it is incremental as it builds on existing shared task frameworks and methods.
The paper tackles the problem of identifying informative COVID-19 English tweets by constructing a corpus of 10K tweets and evaluating 55 systems, with top systems achieving up to 0.91 F1 score and outperforming a baseline.
In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.