A meta-analysis of state-of-the-art electoral prediction from Twitter data

TL;DR

This paper critically reviews the use of Twitter data for electoral prediction, revealing that its predictive power is overstated and traditional polls remain more reliable, while proposing a comprehensive framework for future research.

Contribution

It provides the first meta-analysis of electoral prediction from Twitter data, introduces a scheme to evaluate prediction methods, and highlights the overestimation of social media's predictive capabilities.

Findings

Twitter data's predictive power is exaggerated

Current research lacks strong evidence to replace traditional polls

A new framework for evaluating electoral prediction methods is proposed

Abstract

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.