Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted.
The accurate forecasting of infectious epidemic diseases such as influenza is a crucial task undertaken by medical institutions.
Disease name recognition and normalization, which is generally called biomedical entity linking, is a fundamental process in biomedical text mining.
Since the concept of the KART framework is domain agnostic, it can contribute to the establishment of privacy guidelines of language models beyond the biomedical domain.
To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i. e., transfer learning).
Second, we extracted the location of the WSSCI via the smartphone application.
Based on these results, we can infer that social sensors can reliably detect unseasonal and local disease events under certain conditions, just as they can for seasonal or global events.
Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events.