# Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets

## TL;DR

This paper presents a novel context-aware approach for stance detection in political tweets, outperforming previous methods by incorporating information about friends and enemies of politicians.

## Contribution

The study introduces a new feature set considering social context for stance detection, specifically applied to political debates in Twitter during the 2016 U.S. elections.

## Key findings

- Outperforms state-of-the-art models on SemEval-2016 dataset
- Contextual features about friends and enemies improve stance detection accuracy
- Effective in political tweet analysis during election campaigns

## Abstract

Stance detection, the task of identifying the speaker's opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for detecting stance in Twitter. We define a set of features in order to consider the context surrounding a target of interest with the final aim of training a model for predicting the stance towards the mentioned targets. In particular, we are interested in investigating political debates in social media. For this reason we evaluated our approach focusing on two targets of the SemEval-2016 Task6 on Detecting stance in tweets, which are related to the political campaign for the 2016 U.S. presidential elections: Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of the art, we evaluated our model against the dataset released in the SemEval-2016 Task 6 shared task competition. Our results outperform the best ones obtained by participating teams, and show that information about enemies and friends of politicians help in detecting stance towards them.