Abstract
The predictive capabilities of metrics based on Twitter data have been stressed in different fields: business, health, market, politics, etc. In specific cases, a deeper analysis is required to create useful metrics and models with predicting capabilities. In this paper, a set of metrics based on Twitter data have been identified and presented in order to predict the audience of scheduled television programmes, where the audience is highly involved such as it occurs with reality shows (i.e., X Factor and Pechino Express, in Italy). Identified suitable metrics are based on the volume of tweets, the distribution of linguistic elements, the volume of distinct users involved in tweeting, and the sentiment analysis of tweets. On this ground a number of predictive models have been identified and compared. The resulting method has been selected in the context of a validation and assessment by using real data, with the aim of building a flexible framework able to exploit the predicting capabilities of social media data. Further details are reported about the method adopted to build models which focus on the identification of predictors by their statistical significance. Experiments have been based on the collected Twitter data by using Twitter Vigilance platform, which is presented in this paper, as well.
Highlights
Social media analysis is becoming a very important instrument to monitor communities, users’ preferences, and to make predictions
The paper proposed an approach for creating Twitter-based models and metrics in order to predict the expected audience on television programmes
The proposed solution has been tuned by using reality shows, which are specific kinds of TV shows not addressed in the literature, and which present high volume of Twitter data due to the high involvement of audience in the trend of the programme by voting and interacting
Summary
National Research Council, Florence, Italy 2 LAMMA Consortium, Tuscany Region-CNR, Sesto Fiorentino, Italy 3 DISIT Lab, Distributed [Systems and internet | Data Intelligence and] Technologies Lab, Department of Information Engineering (DINFO), University of Florence, Florence, Italy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have