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Predicting the ideological orientation during the Spanish 24M elections in Twitter using machine learning

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Abstract

Through the application of machine learning techniques, this paper aims to estimate the importance of messages with ideological load during the elections held in Spain on May 24th, 2015 posted by Twitter’s users, as well as other variables associated with the publication of these types of messages. Our study collected and analysed 24,900 tweets associated to two of the main trending topics’ hashtags (#24M and #Elections2015) used in the election day and build a predictive model to infer the ideological orientation for the messages which made use of these hashtags during Election Day. This approach allows us to classify the ideological orientation of all collected tweets, instead of only tweets that explicitly express their ideological or partisan preferences in the messages. Using the ideological orientation for all tweets predicted by our model, it was possible to identify how messages with a defined ideological load were pushed forward by users with leftist tendencies. We also observed a relationship between these messages and the partisan orientation of those who published them.

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Notes

  1. https://www.twitonomy.com.

  2. http://www.trendinalia.com.

  3. http://nltk.org.

  4. In fivefold cross validation, the training set is split in five folds of similar length. Four folds are used for training the model, and the remaining fold for testing. The process is repeated five times, and a different fold is used for testing in each repetition.

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Authors and Affiliations

  1. Federal University of ABC, Brazil, Av. dos Estados, 5001, Bangú, Santo André, São Paulo, 09210-580, Brazil

    Ronaldo Cristiano Prati

  2. International University of the Rioja, Calle San Joaquin 8, 2, 28220, Majadahonda, Madrid, Spain

    Elias Said-Hung

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  1. Ronaldo Cristiano Prati
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  2. Elias Said-Hung
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Correspondence to Elias Said-Hung.

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Prati, R.C., Said-Hung, E. Predicting the ideological orientation during the Spanish 24M elections in Twitter using machine learning. AI & Soc 34, 589–598 (2019). https://doi.org/10.1007/s00146-017-0761-0

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