Motivation and goals:
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