@inproceedings{sanders-van-den-bosch-2022-correlating,
title = "Correlating Political Party Names in Tweets, Newspapers and Election Results",
author = "Sanders, Eric and
van den Bosch, Antal",
editor = "Afli, Haithem and
Alam, Mehwish and
Bouamor, Houda and
Casagran, Cristina Blasi and
Boland, Colleen and
Ghannay, Sahar",
booktitle = "Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.politicalnlp-1.2",
pages = "8--15",
abstract = "Twitter has been used as a textual resource to attempt to predict the outcome of elections for over a decade. A body of literature suggests that this is not consistently possible. In this paper we test the hypothesis that mentions of political parties in tweets are better correlated with the appearance of party names in newspapers than to the intention of the tweeter to vote for that party. Five Dutch national elections are used in this study. We find only a small positive, negligible difference in Pearson{'}s correlation coefficient as well as in the absolute error of the relation between tweets and news, and between tweets and elections. However, we find a larger correlation and a smaller absolute error between party mentions in newspapers and the outcome of the elections in four of the five elections. This suggests that newspapers are a better starting point for predicting the election outcome than tweets.",
}
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<abstract>Twitter has been used as a textual resource to attempt to predict the outcome of elections for over a decade. A body of literature suggests that this is not consistently possible. In this paper we test the hypothesis that mentions of political parties in tweets are better correlated with the appearance of party names in newspapers than to the intention of the tweeter to vote for that party. Five Dutch national elections are used in this study. We find only a small positive, negligible difference in Pearson’s correlation coefficient as well as in the absolute error of the relation between tweets and news, and between tweets and elections. However, we find a larger correlation and a smaller absolute error between party mentions in newspapers and the outcome of the elections in four of the five elections. This suggests that newspapers are a better starting point for predicting the election outcome than tweets.</abstract>
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%0 Conference Proceedings
%T Correlating Political Party Names in Tweets, Newspapers and Election Results
%A Sanders, Eric
%A van den Bosch, Antal
%Y Afli, Haithem
%Y Alam, Mehwish
%Y Bouamor, Houda
%Y Casagran, Cristina Blasi
%Y Boland, Colleen
%Y Ghannay, Sahar
%S Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F sanders-van-den-bosch-2022-correlating
%X Twitter has been used as a textual resource to attempt to predict the outcome of elections for over a decade. A body of literature suggests that this is not consistently possible. In this paper we test the hypothesis that mentions of political parties in tweets are better correlated with the appearance of party names in newspapers than to the intention of the tweeter to vote for that party. Five Dutch national elections are used in this study. We find only a small positive, negligible difference in Pearson’s correlation coefficient as well as in the absolute error of the relation between tweets and news, and between tweets and elections. However, we find a larger correlation and a smaller absolute error between party mentions in newspapers and the outcome of the elections in four of the five elections. This suggests that newspapers are a better starting point for predicting the election outcome than tweets.
%U https://aclanthology.org/2022.politicalnlp-1.2
%P 8-15
Markdown (Informal)
[Correlating Political Party Names in Tweets, Newspapers and Election Results](https://aclanthology.org/2022.politicalnlp-1.2) (Sanders & van den Bosch, PoliticalNLP 2022)
ACL