  We present the results and the main findings of the NLP4IF-2021 shared tasks.
Task 1 focused on fighting the COVID-19 infodemic in social media, and it was
offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict
whether that tweet contains a verifiable claim, and if so, whether it is likely
to be false, is of general interest, is likely to be harmful, and is worthy of
manual fact-checking; also, whether it is harmful to society, and whether it
requires the attention of policy makers. Task~2 focused on censorship
detection, and was offered in Chinese. A total of ten teams submitted systems
for task 1, and one team participated in task 2; nine teams also submitted a
system description paper. Here, we present the tasks, analyze the results, and
discuss the system submissions and the methods they used. Most submissions
achieved sizable improvements over several baselines, and the best systems used
pre-trained Transformers and ensembles. The data, the scorers and the
leaderboards for the tasks are available at
http://gitlab.com/NLP4IF/nlp4if-2021.
