Written and spoken corpus of real and fake social media postings about
  COVID-19
Ng Bee Chin Ng Zhi Ee Nicole Kyla Kwan Lee Yong Han Dylann Liu Fang Xu Hong
This study investigates the linguistic traits of fake news and real news.
There are two parts to this study: text data and speech data. The text data for
this study consisted of 6420 COVID-19 related tweets re-filtered from Patwa et
al. (2021). After cleaning, the dataset contained 3049 tweets, with 2161
labeled as 'real' and 888 as 'fake'. The speech data for this study was
collected from TikTok, focusing on COVID-19 related videos. Research assistants
fact-checked each video's content using credible sources and labeled them as
'Real', 'Fake', or 'Questionable', resulting in a dataset of 91 real entries
and 109 fake entries from 200 TikTok videos with a total word count of 53,710
words. The data was analysed using the Linguistic Inquiry and Word Count (LIWC)
software to detect patterns in linguistic data. The results indicate a set of
linguistic features that distinguish fake news from real news in both written
and speech data. This offers valuable insights into the role of language in
shaping trust, social media interactions, and the propagation of fake news.