# A semi-supervised approach to message stance classification

## TL;DR

This paper introduces semi-supervised graph-based methods for message stance classification on social media, demonstrating improved accuracy, speed, and scalability over supervised approaches for rumour verification.

## Contribution

It presents the use of Label Propagation and Label Spreading algorithms for semi-supervised stance classification, outperforming supervised models in accuracy and efficiency.

## Key findings

- Semi-supervised methods outperform supervised models in accuracy.
- Graph-based algorithms are faster and more scalable.
- Effective for real-time rumour verification.

## Abstract

Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely on determining the constituent messages' stance towards the rumour, a feature known as the "wisdom of the crowd". Although several supervised machine-learning approaches have been proposed to tackle the message stance classification problem, these have numerous shortcomings. In this paper we argue that semi-supervised learning is more effective than supervised models and use two graph-based methods to demonstrate it. This is not only in terms of classification accuracy, but equally important, in terms of speed and scalability. We use the Label Propagation and Label Spreading algorithms and run experiments on a dataset of 72 rumours and hundreds of thousands messages collected from Twitter. We compare our results on two available datasets to the state-of-the-art to demonstrate our algorithms' performance regarding accuracy, speed and scalability for real-time applications.