Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the Polya urn, introduced and studied in Aletti and Crimaldi (2019, 2020), which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard Polya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.

A model for the Twitter sentiment curve / G. Aletti, I. Crimaldi, F. Saracco. - In: PLOS ONE. - ISSN 1932-6203. - 16:4(2021 Apr 15). [10.1371/journal.pone.0249634]

A model for the Twitter sentiment curve

G. Aletti
Primo
Conceptualization
;
2021-04-15

Abstract

Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the Polya urn, introduced and studied in Aletti and Crimaldi (2019, 2020), which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard Polya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.
Settore MAT/06 - Probabilita' e Statistica Matematica
Settore SECS-S/01 - Statistica
Centro di Ricerca Interdisciplinare su Modellistica Matematica, Analisi Statistica e Simulazione Computazionale per la Innovazione Scientifica e Tecnologica ADAMSS
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/838430
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