# Detecting Stance in Tweets : A Signed Network based Approach

arXiv: https://arxiv.org/abs/2201.07472 · HTML: https://mlsanity.com/paper/2201.07472/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.07178/) (2026)
- [LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection](https://mlsanity.com/paper/2603.19293/) (2026)
- [EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization](https://mlsanity.com/paper/2605.23412/) (2026)

## Abstract (arXiv)

Identifying user stance related to a political event has several applications, like determination of individual stance, shaping of public opinion, identifying popularity of government measures and many others. The huge volume of political discussions on social media platforms, like, Twitter, provide opportunities in developing automated mechanisms to identify individual stance and subsequently, scale to a large volume of users. However, issues like short text and huge variance in the vocabulary of the tweets make such exercise enormously difficult. Existing stance detection algorithms require either event specific training data or annotated twitter handles and therefore, are difficult to adapt to new events. In this paper, we propose a sign network based framework that use external information sources, like news articles to create a signed network of relevant entities with respect to a news event and subsequently use the same to detect stance of any tweet towards the event. Validation on 5,000 tweets related to 10 events indicates that the proposed approach can ensure over 6.5% increase in average F1 score compared to the existing stance detection approaches.