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.File | Dimensione | Formato | |
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