Predicting Eurovision Song Contest Results Using Sentiment Analysis

Kumpulainen, Iiro; Praks, Eemil; Korhonen, Tenho; Ni, Anqi; Rissanen, Ville; Vankka, Jouko

doi:10.1007/978-3-030-59082-6_7

Communications in Computer and Information Science

2020

DOI: 10.1007/978-3-030-59082-6_7

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Predicting Eurovision Song Contest Results Using Sentiment Analysis

Iiro Kumpulainen

Eemil Praks

Tenho Korhonen

et al.

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2024

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Cited by 3 publications

References 19 publications

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Agenda Formation and Prediction of Voting Tendencies for European Parliament Election using Textual, Social and Network Features

Shahi,

Basyurt,

Stieglitz

et al. 2024

Inf Syst Front

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As per agenda-setting theory, political agenda is concerned with the government’s agenda, including politicians and political parties. Political actors utilize various channels to set their political agenda, including social media platforms such as Twitter (now X). Political agenda-setting can be influenced by anonymous user-generated content following the Bright Internet. This is why speech acts, experts, users with affiliations and parties through annotated Tweets were analyzed in this study. In doing so, the agenda formation during the 2019 European Parliament Election in Germany based on the agenda-setting theory as our theoretical framework, was analyzed. A prediction model was trained to predict users’ voting tendencies based on three feature categories: social, network, and text. By combining features from all categories logistical regression leads to the best predictions matching the election results. The contribution to theory is an approach to identify agenda formation based on our novel variables. For practice, a novel approach is presented to forecast the winner of events.

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Agenda Formation and Prediction of Voting Tendencies for European Parliament Election using Textual, Social and Network Features

Shahi,

Basyurt,

Stieglitz

et al. 2024

Inf Syst Front

View full text Add to dashboard Cite

show abstract

Can we predict the Billboard music chart winner? Machine learning prediction based on Twitter artist-fan interactions

Aum

Kim

Park

2022

Behaviour & Information Technology

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The Politics of Eurovision: A Case Study of the United Kingdom’s 2021 and 2022 Participations as Expressed on Social Media

García-Gámez,

Moreno-Ortiz

2024

Rev. lingüíst. leng. apl.

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In recent years, the opinion that the Eurovision Song Contest has become highly politicised is prevalent in the media and the popular voice, although not much research exists that can attest to this claim. In this work we conduct a case study that applies sentiment and discourse analysis methodologies to the assessment of political opinions in social media regarding this artistic and social event. The main objective is to explore to what extent and in what form this supposed politicisation has an expression on Twitter, as illustrated by the cases of artists Sam Ryder and James Newman, the United Kingdom’s representatives in the 2022 and 2021 editions of the contest, respectively. We examine references to two historical-political contexts that have had a severe impact on the European society over the last few years, and which have determined, among many other social aspects, the reception of Eurovision results ever since they took place: Brexit and the Russian invasion of Ukraine.

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Predicting Eurovision Song Contest Results Using Sentiment Analysis

Cited by 3 publications

References 19 publications

Agenda Formation and Prediction of Voting Tendencies for European Parliament Election using Textual, Social and Network Features

Agenda Formation and Prediction of Voting Tendencies for European Parliament Election using Textual, Social and Network Features

Can we predict the Billboard music chart winner? Machine learning prediction based on Twitter artist-fan interactions

The Politics of Eurovision: A Case Study of the United Kingdom’s 2021 and 2022 Participations as Expressed on Social Media

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