A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

• Published in: IEEE transactions on computational social systems Vol. 8; no. 4; pp. 819 - 843
• Main Authors: Brito, Kellyton Dos Santos, Filho, Rogerio Luiz Cardoso Silva, Adeodato, Paulo Jorge Leitao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blogs; Context; Data mining; Data models; Digital media; Elections; Literature reviews; Machine learning; Modelling; Performance evaluation; Predictive models; Sentiment analysis; social media (SM); Social networking (online); Social networks; social networks (SNs); systematic review; Systematics; Voting
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2021.3063660

The way politicians communicate with the electorate and run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SNs). Due to the inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using the SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial and many times challenged. In this context, this article aims to investigate and summarize how research on predicting elections based on the SM data has evolved since its beginning, to outline the state of both the art and the practice, and to identify research opportunities within this field. In terms of method, we performed a systematic literature review analyzing the quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation, and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances in process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-the-art machine learning approaches.