# Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure

arXiv: https://arxiv.org/abs/1901.01911 · HTML: https://mlsanity.com/paper/1901.01911/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [BERT based classification system for detecting rumours on Twitter](https://mlsanity.com/paper/2109.02975/) (2021)
- [Evaluating BERT-based Pre-training Language Models for Detecting Misinformation](https://mlsanity.com/paper/2203.07731/) (2022)
- [5q032e@SMM4H'22: Transformer-based classification of premise in tweets related to COVID-19](https://mlsanity.com/paper/2209.03851/) (2022)

## Abstract (arXiv)

Analysing how people react to rumours associated with news in social media is an important task to prevent the spreading of misinformation, which is nowadays widely recognized as a dangerous tendency. In social media conversations, users show different stances and attitudes towards rumourous stories. Some users take a definite stance, supporting or denying the rumour at issue, while others just comment it, or ask for additional evidence related to the veracity of the rumour. On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets. The goal is predicting user stance towards emerging rumours in Twitter, in terms of supporting, denying, querying, or commenting the original rumour, looking at the conversation threads originated by the rumour. This paper describes a new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, has been explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 Task 8, showing the effectiveness of the feature set proposed.