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.

Stance Evolution and Twitter Interactions in an Italian Political Debate

Mirko Lai;Viviana Patti;Giancarlo Ruffo;
2018

Abstract

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.
23rd International Conference on Natural Language & Information Systems
Paris, France
13rd - 15th June 2018
Natural Language Processing and Information Systems 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, Paris, France, June 13-15, 2018, Proceedings
Springer International Publishing AG, part of Springer Nature 2018
10859
15
27
978-3-319-91946-1
978-3-319-91947-8
https://link.springer.com/chapter/10.1007/978-3-319-91947-8_2
Stance, Political debates, Homophily, Twitter
Mirko Lai, Viviana Patti, Giancarlo Ruffo, Paolo Rosso
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1669103
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