How do sentiments affect virality on Twitter?
Virality on Twitter is catching the attention of researchers, trying to identify factors which increase or decrease the probability of retweeting. We study how terms expressing sentiments affect retweeting frequencies by means of a regression model on the number of retweets, which is specially accur...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/7493 |
| Acceso en línea: | https://doi.org/10.1098/rsos.201756 https://royalsocietypublishing.org/rsos/article/8/4/201756/95943/How-do-sentiments-affect-virality-on-Twitter https://hdl.handle.net/10953/7493 |
| Access Level: | acceso abierto |
| Palabra clave: | virality sentiment analysis generalized Waring regression 004 004.6 004.8 004.91 32 519.2 81 |
| Sumario: | Virality on Twitter is catching the attention of researchers, trying to identify factors which increase or decrease the probability of retweeting. We study how terms expressing sentiments affect retweeting frequencies by means of a regression model on the number of retweets, which is specially accurate to deal with virality. We focus on the Spanish political situation during the pseudo-referendum held in Catalonia on 1 October 2017. We have found that the use of negativity in a tweet increases the probability of retweeting and that iSOL lexicon is the one that better determines the relationship between polarity and virality. |
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