Written and spoken corpus of real and fake social media postings about COVID-19
This provides insights into language's role in trust and fake news propagation on social media, but it is incremental as it applies an existing method to new data.
The study tackled the problem of distinguishing fake from real news by analyzing linguistic traits in COVID-19 related social media posts, resulting in the identification of a set of linguistic features that differentiate fake news in both written tweets and spoken TikTok videos, with datasets of 3049 tweets and 200 videos analyzed.
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.