Predicting the outcome of elections is a topic that has been extensively studied in political polls, which have generally provided reliable predictions by means of statistical models. In recent years, online social media platforms have become a potential alternative to traditional polls, since they provide large amounts of post and user data, also referring to socio-political aspects.
In this context, we designed a research that aimed at defining a user modeling pipeline to analyze dis cussions and opinions shared on social media regarding polarized political events (such as a public poll or referendum).
The pipeline follows a four-step methodology.
- First, social media posts and users metadata are crawled.
- Second, a filtering mechanism is applied to filter out spammers and bot users.
- Third, demographics information is extracted out of the valid users, namely gender, age, ethnicity and location information.
- Fourth, the political polarity of the users with respect to the analyzed event is predicted.
In the scope of this work, our proposed pipeline is applied to two referendum scenarios:
- independence of Catalonia in Spain
- autonomy of Lombardy in Italy
We used these real-world examples to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users.
Experiments show that the method was effective in predicting the political trends for the Catalonia case, but not for the Lombardy case. Among the various motivations for this, we noticed that in general Twitter was more representative of the users opposing the referendum than the ones in favor.
A preprint of the paper can be downloaded from ArXiv and cited as reported here:
Roberto Napoli, Ali Mert Ertugrul, Alessandro Bozzon, Marco Brambilla. A User Modeling Pipeline for Studying Polarized Political Events in Social Media. KDWeb Workshop 2018, co-located with ICWE 2018, Caceres, Spain, June 2018. arXiv:1807.09459