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

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.Comment: 19 pages, 3 table

Simplifying the Auto Regressive and Moving Average (ARMA) Model Representing the Dynamic Thermal Behaviour of iHouse Based on Theoretical Knowledge

Modelling and simulation is an alternative way of testing the dynamic behaviour of a real system – in some situation, testing the real system are expensive, time consuming, not comfortable, and dangerous. Mathematical model describing the dynamic behaviour of a system can be represented by using white, black, or grey box model. This study focuses on developing a simplified Auto Regressive Moving Average (ARMA) model (a type of linear black model) to represent the dynamic thermal behaviour of iHouse – simplification is done based on the theoretical knowledge of the building. The performance of the simplified ARMA model developed in this study is compared with the performance of the models developed in previous studies, which are: (1) House Thermal Simulator; (2) and ARMA model. Result shows that the simplified ARMA model developed in this study consists of simpler set of mathematical equations, but can still simulate the dynamic thermal behaviour of iHouse with the accuracy that is almost on par with the models developed in previous studies.Modeling, Design and Simulation of Systems. AsiaSim 2017

The Digital Divide Among Twitter Users and Its Implications for Social Research

Hundreds of papers have been published using Twitter data, but few previous papers report the digital divide among Twitter users. British Twitter users are younger, wealthier, and better educated than other Internet users, who in turn are younger, wealthier, and better educated than the off-line British population. American Twitter users are also younger and wealthier than the rest of the population, but they are not better educated. Twitter users are disproportionately members of elites in both countries. Twitter users also differ from other groups in their online activities and their attitudes. These biases and differences have important implications for research based on Twitter data. The unrepresentative characteristics of Twitter users suggest that Twitter data are not suitable for research where representativeness is important, such as forecasting elections or gaining insight into attitudes, sentiments, or activities of large populations. In general, Twitter data seem to be more suitable for corporate use than for social science research

140 characters to victory?: Using Twitter to predict the UK 2015 General Election

This paper uses Twitter data to forecast the outcome of the 2015 UK General Election. While a number of empirical studies to date have demonstrated striking levels of accuracy in estimating election results using this new data source, there have been no genuine i.e. pre-election forecasts issued to date. Furthermore there have been widely varying methods and models employed with seemingly little agreement on the core criteria required for an accurate estimate. We attempt to address this deficit with our ‘baseline’ model of prediction that incorporates sentiment analysis and prior party support to generate a true forecast of parliament seat allocation. Our results indicate a hung parliament with Labour holding the majority of seats