  False information can spread quickly on social media, negatively influencing
the citizens' behaviors and responses to social events. To better detect all of
the fake news, especially long texts which are harder to find completely, a
Long-Text Chinese Rumor detection dataset named LTCR is proposed. The LTCR
dataset provides a valuable resource for accurately detecting misinformation,
especially in the context of complex fake news related to COVID-19. The dataset
consists of 1,729 and 500 pieces of real and fake news, respectively. The
average lengths of real and fake news are approximately 230 and 152 characters.
We also propose \method, Salience-aware Fake News Detection Model, which
achieves the highest accuracy (95.85%), fake news recall (90.91%) and F-score
(90.60%) on the dataset. (https://github.com/Enderfga/DoubleCheck)
