PREDICTION OF ELECTION RESULTS BASED ONSOCIAL MEDIA REVIEWS

Abstract

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 mostused 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 stateof-the-art machine learning approaches.