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

arXiv: https://arxiv.org/abs/2112.03003 · HTML: https://mlsanity.com/paper/2112.03003/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media](https://mlsanity.com/paper/1610.07363/) (2016)
- [Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.05509/) (2021)
- [Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach](https://mlsanity.com/paper/2010.07647/) (2020)
- [Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media](https://mlsanity.com/paper/2004.12500/) (2020)
- [A Comprehensive Low and High-level Feature Analysis for Early Rumor Detection on Twitter](https://mlsanity.com/paper/1711.00726/) (2017)

## Built on by (most cited first, 8 of 8)

- [GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection](https://mlsanity.com/paper/2202.12478/) (2022)
- [Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking](https://mlsanity.com/paper/2310.07613/) (2023)
- [Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination](https://mlsanity.com/paper/2309.00639/) (2023)
- [Exploring the Emotional and Mental Well-Being of Individuals with Long COVID Through Twitter Analysis](https://mlsanity.com/paper/2307.07558/) (2023)
- [A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles](https://mlsanity.com/paper/2406.07693/) (2024)
- [AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System](https://mlsanity.com/paper/2310.19834/) (2023)
- [Language Markers of Emotion Flexibility Predict Depression and Anxiety Treatment Outcomes](https://mlsanity.com/paper/2601.07961/) (2026)
- [Unmasking Superspreaders: Data-Driven Approaches for Identifying and Comparing Key Influencers of Conspiracy Theories on X.com](https://mlsanity.com/paper/2602.04546/) (2026)

## Abstract (arXiv)

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 isnt 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 PHEME9 rumour dataset (consisting of 9 events), including source tweets (both rumour and non-rumour categories) and response tweets. We compared the rumour and 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 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.