On Using Twitter to Monitor Political Sentiment and Predict Election Results

By Adam Bermingham, Alan F. Smeaton. 2011

433 citations recorded by the source corpus.

The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.

View Paper: https://www.semanticscholar.org/paper/32595517f623429cd323e6552a063b99ddb78766

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

On Unravelling Opinions of Issue Specific-Silent Users in Social Media (2016, background): “2010), US Senate special Election in Massachusetts 2010 (Chung and Mustafaraj 2011), Dutch Senate Election in 2011 (Sang and Bos 2012), Irish General Election in 2011 (Bermingham and Smeaton 2011) and French Presidential and Legislative Elections in 2012 (Ceron et al.”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, background): “…have explained and predicted the outcomes of major political elections through the analysis of tweets on the candidates and political issues (Bermingham & Smeaton, 2011; Chung & Mustafaraj, 2011; Mejova, Srinivasan, & Boynton, 2013; O’Connor et al., 2010; Ringsquandl & Petkovic, 2013;…”
Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach (2020, background): “This ubiquitous deployment creates an acute need to make AI systems understandable and explainable [5,7,8,12,25].”
Automatic Crime Prediction Using Events Extracted from Twitter Posts (2012, unknown): “For example, Twitter posts have been used to predict box office results [1], election results [2], and stock market trends [5].”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “Keywords: sentiment analysis, emotions, artificial intelligence, machine learning, natural language processing (NLP), social media, emotion lexicons, fairness in NLP”
Stance and Sentiment in Tweets (2016, background): “…identifying contentious issues and political opinions [Maynard and Funk 2011], detecting the amount of polarization in the electorate [Conover et al. 2011b], and even predicting the voting intentions or outcome of elections [Tumasjan et al. 2010; Bermingham and Smeaton 2011; Lampos et al. 2013].”
Predicting crime using Twitter and kernel density estimation (2014, background): “Recent research has shown that tweets can be used to predict various large-scale events like elections [3], infectious disease outbreaks [4], and national revolutions [5].”