The number of communications and messages generated by users on social media platforms has progressively increased in the last years. Therefore, the issue of developing automated systems for a deep analysis of users’ generated contents and interactions is becoming increasingly relevant. In particular, when we focus on the domain of online political debates, interest for the automatic classification of users’ stance towards a given entity, like a controversial topic or a politician, within a polarized debate is significantly growing. In this paper we propose a new model for stance detection in Twitter, where authors’ messages are not considered in isolation, but in a diachronic perspective for shedding light on users’ opinion shift dynamics along the temporal axis. Moreover, different types of social network community, based on retweet, quote, and reply relations were analyzed, in order to extract network-based features to be included in our stance detection model. The model has been trained and evaluated on a corpus of Italian tweets where users were discussing on a highly polarized debate in Italy, i.e. the 2016 referendum on the reform of the Italian Constitution. The development of a new annotated corpus for stance is described. Analysis and classification experiments show that network-based features help in detecting stance and confirm the importance of modeling stance in a diachronic perspective.
- Political debates
The work of the last author was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).
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The majority of the voters rejected the reform causing the resignation of Matteo Renzi, the Prime Minister that assumed full responsibility for the referendum defeat.
#constitutionalreferendum, #Ivoteyes, #Ivoteno.
ConRef-STANCE-ita and code available at: https://github.com/mirkolai/Stance-Evolution-and-Twitter-Interactions.
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Lai, M., Patti, V., Ruffo, G., Rosso, P. (2018). Stance Evolution and Twitter Interactions in an Italian Political Debate. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_2
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Print ISBN: 978-3-319-91946-1
Online ISBN: 978-3-319-91947-8