Modified valence aware dictionary for sentiment reasoning classifier for detection and classification of Covid-19 related rumors from social media data streams

The perpetual increase in social media data due to social distancing policy in practice and the low cost of communication has acquired an interest in rumor detection in social media. During the togetherness of the globalized world in the pandemic situation of Covid-19, social media applications drive excellent value to the analysts and journalists working in different domains. However, the unmoderated nature of social media users often leads to the spread of rumors due to a variety of opinions on government decisions, which becomes notoriously hard to detect. Also, the users may deny rumors as soon as they are debunked. This research extracts diffused information such as word cloud, hashtags, re-tweets and so forth and uses it as features. These features are applied in the sliding windows of data streams to detect tweets that may be rumors by validating using credible news sources. To address the challenge of veracity, tweets are classified into rumors and nonrumors. A modified unsupervised VADER sentiment classifier is proposed to further classify rumors tweets into amalgamated, unauthoritative, or exaggerated. It is revealed from the results that the proposed classifier is quite efficient in finding and classifying rumorous tweets that arise in critical situations.