Jiménez-Zafra Salud María, Sáez-Castillo Antonio José, Conde-Sánchez Antonio, Martín-Valdivia María Teresa
SINAI Department of Computer Science, CEATIC, Universidad de Jaén, Campus Las Lagunillas s/n, Jaén 23071, Spain.
Department of Statistics and Operational Research, Universidad de Jaén, Campus Las Lagunillas s/n, Jaén 23071, Spain.
R Soc Open Sci. 2021 Apr 14;8(4):201756. doi: 10.1098/rsos.201756.
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.
推特上的传播热度正引起研究人员的关注,他们试图找出增加或降低转发可能性的因素。我们通过一个针对转发数量的回归模型来研究表达情感的词汇如何影响转发频率,该模型在处理传播热度方面特别准确。我们关注的是2017年10月1日加泰罗尼亚举行的伪公投期间的西班牙政治局势。我们发现,推文中使用负面词汇会增加转发的可能性,并且iSOL词汇表能更好地确定极性与传播热度之间的关系。