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In recent years, the problem of rumours on online social media (OSM) hasattracted lots of attention. Researchers have started investigating from twomain directions. First is the descriptive analysis of rumours and secondly,proposing techniques to detect (or classify) rumours. In the descriptive lineof works, where researchers have tried to analyse rumours using NLP approaches,there isnt much emphasis on psycho-linguistics analyses of social media text.These kinds of analyses on rumour case studies are vital for drawing meaningfulconclusions to mitigate misinformation. For our analysis, we explored thePHEME9 rumour dataset (consisting of 9 events), including source tweets (bothrumour and non-rumour categories) and response tweets. We compared the rumourand nonrumour source tweets and then their corresponding reply (response)tweets to understand how they differ linguistically for every incident.Furthermore, we also evaluated if these features can be used for classifyingrumour vs. non-rumour tweets through machine learning models. To this end, weemployed various classical and ensemble-based approaches. To filter out thehighly discriminative psycholinguistic features, we explored the SHAP AIExplainability tool. To summarise, this research contributes by performing anin-depth psycholinguistic analysis of rumours related to various kinds ofevents.

What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses