Voting intentions on social media and political opinion polls |
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| Institution: | 1. Aston Business School, Aston University, Birmingham B4 7ET, UK;2. College of Arts and Sciences, University of Wisconsin, USA;3. Birmingham Business School, The University of Birmingham, Edgbaston, Birmingham B14 2TY, UK;4. Division of Technology Services, University of Wisconsin – River Falls, 410 S. 3rd Street, River Falls, WI 54022, USA;5. Department of Physics and Mathematics, University of Hull, Hull HU6 7RX, UK;1. Department of Organization, University of Zagreb, Faculty of Organization and Informatics Vara?din, Pavlinska 2, 42 000 Vara?din, Croatia;2. College of Arts and Sciences, Carlow University, 3333 Fifth Avenue, Pittsburgh, PA 15213, United States;1. FSA ULaval, Université Laval, Pavillon Palasis-Prince, 2325 rue de la Terrasse, Quebec, QC G1V 0A6, Canada;1. Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia;2. Al Balqa’ Applied University, Amman College for Financial & Managerial Science, Jordan;4. School of Management, University of Bradford, UK;5. Emerging Markets Research Centre (EMaRC), School of Management, Room #323, Swansea University, Bay Campus, Fabian Bay, Swansea, SA1 8EN Wales, UK;6. Department of Management, Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, Maharashtra, India;1. Department of Computer Science & Engineering, Indian Institute of Technology Kharagpur, India;2. Bournemouth University Business School, Bournemouth University, Bournemouth, BH8 8EB, UK;3. Faculty of Behavioural, Management and Social Sciences, University of Twente, P.O. Box 217, 7500AE Enschede, the Netherlands.;1. Mid Sweden University, Faculty of Science, Technology and Media, Department of Information systems and Technology, Forum for Digitalization, Holmgatan 10, Sundsvall 851 70, Sweden.;2. University of South Africa, Department of Information Science, Preller Street, Muckleneuk Ridge, Pretoria. |
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| Abstract: | Opinion polls play an important role in modern democratic processes: they are known to not only affect the outcomes of elections, but also have a significant influence on government policy after elections. Recent years have seen large discrepancies between polls and outcomes at several major elections and referendums, stemming from decreased participation in polls and an increasingly volatile electorate. This calls for new ways to measure public support for political parties. In this paper, we propose a method for measuring the popularity of election candidates on social media using Machine Learning-based Natural Language Processing techniques. The method is based on detecting voting intentions in the data. This is a considerable advance upon earlier work using automatic sentiment analysis. We evaluate the method both intrinsically on a set of hand-labelled social media posts, and extrinsically – by forecasting daily election polls. In the extrinsic evaluation, we analyze data from the 2016 US presidential election, and find that voting intentions measured from social media provide significant additional predictive value for forecasting daily polls. Thus, we demonstrate that the proposed method can be used to interpolate polls both spatially and temporally, thus providing reliable, continuous and fine-grained information about public opinion on current political issues. |
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