Social Media Mining for Covid-19

In recent years we have witnessed a combination of an enormous amount of fake or misleading information disseminated through social media. During the current COVID-19 pandemic the problem has been particularly noticeable. Wrong and misleading information can spread extremely rapidly, potentially causing serious harm, a problem which has been termed as an infodemic. In this project, we aim to investigate methods to detect potentially damaging misinformation before it spreads.

A very large collection of tweets related to COVID-19.

• Calculation of language, hashtag and domain distribution
• LDA topic models
• Calculation of number of posts per day/hashtag
• Sentiment analysis by hashtag
• Detection of mentions of paper preprints
• Detection of mentions of drugs
• Stance detection
• Discovering of emerging preprints and trending drugs
• Monitoring of the distribution of sources and topics of posts