TY - GEN
T1 - Stance evolution and twitter interactions in an italian political debate
AU - Lai, Mirko
AU - Patti, Viviana
AU - Ruffo, Giancarlo
AU - Rosso, Paolo
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Homophily
KW - Political debates
KW - Stance
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85048040464&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91947-8_2
DO - 10.1007/978-3-319-91947-8_2
M3 - Conference contribution
AN - SCOPUS:85048040464
SN - 9783319919461
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 15
EP - 27
BT - Natural Language Processing and Information Systems - 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, Proceedings
A2 - Meziane, Farid
A2 - Silberztein, Max
A2 - Atigui, Faten
A2 - Kornyshova, Elena
A2 - Metais, Elisabeth
PB - Springer Verlag
T2 - 23rd International Conference on Natural Language and Information Systems, NLDB 2018
Y2 - 13 June 2018 through 15 June 2018
ER -