Stance Prediction and Analysis of Twitter data : A case study of Ghana 2020 Presidential Elections

TL;DR

This study analyzes Twitter data from Ghana's 2020 presidential election to classify user stance towards candidates using machine learning, providing insights into social media opinions during the election.

Contribution

It introduces a stance classification approach for Ghanaian Twitter data using supervised machine learning, with a new annotated dataset and analysis of election-related opinions.

Findings

Logistic Regression achieved 71.13% accuracy.

Support Vector Machine and other models were also evaluated.

The dataset and code are publicly available for further research.

Abstract

On December 7, 2020, Ghanaians participated in the polls to determine their president for the next four years. To gain insights from this presidential election, we conducted stance analysis (which is not always equivalent to sentiment analysis) to understand how Twitter, a popular social media platform, reflected the opinions of its users regarding the two main presidential candidates. We collected a total of 99,356 tweets using the Twitter API (Tweepy) and manually annotated 3,090 tweets into three classes: Against, Neutral, and Support. We then performed preprocessing on the tweets. The resulting dataset was evaluated using two lexicon-based approaches, VADER and TextBlob, as well as five supervised machine learning-based approaches: Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Na\"ive Bayes (MNB), Stochastic Gradient Descent (SGD), and Random Forest (RF), based on metrics such as accuracy, precision, recall, and F1-score. The best performance was achieved by Logistic Regression with an accuracy of 71.13%. We utilized Logistic Regression to classify all the extracted tweets and subsequently conducted an analysis and discussion of the results. For access to our data and code, please visit: https://github.com/ShesterG/Stance-Detection-Ghana-2020-Elections.git