What goes on inside rumour and non-rumour tweets and their reactions: A psycholinguistic analyses. (October 2022)

• Record Type: Journal Article
• Authors: Butt, Sabur; Sharma, Shakshi; Sharma, Rajesh; Sidorov, Grigori; Gelbukh, Alexander
• Is Part Of: Computers in human behavior. Volume 135(2022)
• Publication Date: 2022-10
• Subjects: Rumour detection -- Psycho-linguistic analyses -- Explainable AI; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2022.107345
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: In recent years, the problem of rumours on online social media (OSM) has attracted lots of attention. Researchers have started investigating from two main directions. First is the descriptive analysis of rumours and secondly, proposing techniques to detect (or classify) rumours. In the descriptive line of works, where researchers have tried to analyse rumours using NLP approaches, there isn't much emphasis on psycho-linguistics analyses of social media text. These kinds of analyses on rumour case studies are vital for drawing meaningful conclusions to mitigate misinformation. For our analysis, we explored the PHEME-9 rumour dataset (consisting of 9 events), including source tweets (both rumour and non-rumour categories) and response tweets. We compared the rumour and non-rumour 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 classifying rumour vs. non-rumour tweets through machine learning models. To this end, we employed various classical and ensemble-based approaches. To filter out the highly discriminative psycholinguistic features, we explored the SHAP AI Explainability tool. To summarise, this research contributes by performing an in-depth psycholinguistic analysis of rumours related to various kinds of events. Highlights: Difference in psycho-linguistic characteristics between rumour and non-rumour source tweets. Psycho-linguistic features to differentiate between the reactions that rumour and non-rumour tweets attract. Contribution of psycho-linguistic features vary from event to event. Psycho-linguistic features for classifying rumour and non-rumour tweets using Machine Learning models. Psycho-linguistic features analyses using statistical metrics and SHAP plots.