In this paper, we propose to model retweet event sequences using a marked Hawkes process, which is a self-exciting point process where the occurrence of previous events in time increases the probability of further events. The aim is to analyse Twitter data combining temporal point processes theory and textual analysis. Since each retweet event carries a set of properties, we mark the process by different characteristics drawn from the textual analysis, finding that the tone of the description of the Twitter user is a good predictor of the number of retweets in a single cascade.

Andrea Simonetti, Nicoletta D'Angelo, Giada Adelfio (2022). Marked Hawkes processes for Twitter data. In Proceedings of the 16th International Conference on Statistical Analysis of Textual Data.

Marked Hawkes processes for Twitter data

Andrea Simonetti
;
Nicoletta D'Angelo;Giada Adelfio
2022-01-01

Abstract

In this paper, we propose to model retweet event sequences using a marked Hawkes process, which is a self-exciting point process where the occurrence of previous events in time increases the probability of further events. The aim is to analyse Twitter data combining temporal point processes theory and textual analysis. Since each retweet event carries a set of properties, we mark the process by different characteristics drawn from the textual analysis, finding that the tone of the description of the Twitter user is a good predictor of the number of retweets in a single cascade.
2022
979-12-80153-31-9
Andrea Simonetti, Nicoletta D'Angelo, Giada Adelfio (2022). Marked Hawkes processes for Twitter data. In Proceedings of the 16th International Conference on Statistical Analysis of Textual Data.
File in questo prodotto:
File Dimensione Formato  
jadt2022.pdf

Solo gestori archvio

Descrizione: Contributo completo
Tipologia: Versione Editoriale
Dimensione 690.97 kB
Formato Adobe PDF
690.97 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/559002
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact