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Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets

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Advances in Computational Intelligence (MICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10061))

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Abstract

Stance detection, the task of identifying the speaker’s opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for detecting stance in Twitter. We define a set of features in order to consider the context surrounding a target of interest with the final aim of training a model for predicting the stance towards the mentioned targets. In particular, we are interested in investigating political debates in social media. For this reason we evaluated our approach focusing on two targets of the SemEval-2016 Task 6 on Detecting stance in tweets, which are related to the political campaign for the 2016 U.S. presidential elections: Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of the art, we evaluated our model against the dataset released in the SemEval-2016 Task 6 shared task competition. Our results outperform the best ones obtained by participating teams, and show that information about enemies and friends of politicians help in detecting stance towards them.

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Notes

  1. 1.

    They are the candidates who won the Party Presidential Primaries for the Democratic and Republican parties, respectively.

  2. 2.

    http://alt.qcri.org/semeval2016/task6/.

  3. 3.

    This tweet was extracted from the training set of SemEval-2016 Task 6.

  4. 4.

    Notice that not all the reports describing systems and approaches of teams participating at SemEval-2016 Task 6 are available in [8].

  5. 5.

    https://github.com/mirkolai/Friends-and-Enemies-of-Clinton-and-Trump.

  6. 6.

    For example, the term vote is strongly related to politics, but it is not present in commonly used SA lexica such as: AFINN, Hu & Liu, and LIWC.

  7. 7.

    Articles: Democratic Party presidential primaries, 2016 and Republican Party presidential candidates, 2016.

  8. 8.

    https://en.wikipedia.org/wiki/J._Christopher_Stevens.

  9. 9.

    Notice that this is the first publicly available Twitter dataset annotated with both stance and sentiment.

  10. 10.

    http://scikit-learn.org/.

  11. 11.

    The authors experimented with n-grams, char-grams and majority class to establish the baselines for the task.

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Acknowledgments

The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazú Hernández Farías (218109/313683). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030). The work of Viviana Patti was partially carried out at the Universitat Politècnica de València within the framework of a fellowship of the University of Turin co-funded by Fondazione CRT (World Wide Style Program 2).

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Torino, Turin, Italy

    Mirko Lai, Delia Irazú Hernández Farías & Viviana Patti

  2. Pattern Recognition and Human Language Technologies Research Center, Universitat Politécnica de Valéncia, Valencia, Spain

    Mirko Lai, Delia Irazú Hernández Farías & Paolo Rosso

Authors
  1. Mirko Lai
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  2. Delia Irazú Hernández Farías
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  3. Viviana Patti
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  4. Paolo Rosso
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Corresponding author

Correspondence to Mirko Lai .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Universidad Autónoma Metropolitana, Mexico City, Mexico

    Oscar Herrera-Alcántara

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Lai, M., Hernández Farías, D.I., Patti, V., Rosso, P. (2017). Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-62434-1_13

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