Fonseca António, Pontes Catarina, Moro Sérgio, Batista Fernando, Ribeiro Ricardo, Guerra Rita, Carvalho Paula, Marques Catarina, Silva Cláudia
Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisbon, Portugal.
University of Jordan, Amman, Jordan.
Heliyon. 2024 May 31;10(11):e32246. doi: 10.1016/j.heliyon.2024.e32246. eCollection 2024 Jun 15.
This paper investigates the pervasive issue of hate speech within Twitter/X Portuguese network conversations, offering a multifaceted analysis of its characteristics. This study utilizes a mixed-method approach, combining several methodologies of network analysis (triad census and participation shifts) over the network of interaction between users. Qualitative manual content annotation was applied to the dataset to dissect different patterns of hate speech on the platform. Key findings reveal that the number of users followed by an individual and potentially reads is a relevant predictor for a user's propensity to post aggressive content. We concluded also that during a conversation thread, hate speech happens significantly more within the first 2 h of interaction. Transitivity of interactions and individual expression are considerably lower as more hate speech is prevalent in conversations. Our research confirms that hate speech is usually expressed by external individuals who intrude into conversations. Conversely, the expression of hate speech of indirect type by third parties interfering in conversations is uncommon. We also found that counter-speech discourse is strongly correlated with a type of discourse that typically avoids conflict and is not privately held.
本文研究了推特/X葡萄牙语网络对话中普遍存在的仇恨言论问题，对其特征进行了多方面分析。本研究采用混合方法，结合网络分析的几种方法（三元组普查和参与度变化）对用户之间的互动网络进行分析。对数据集应用了定性的人工内容标注，以剖析该平台上仇恨言论的不同模式。主要研究结果表明，一个人关注并可能阅读的用户数量是预测用户发布攻击性内容倾向的一个相关因素。我们还得出结论，在一个对话线程中，仇恨言论在互动的前两小时内显著更多。随着对话中仇恨言论的增多，互动的传递性和个人表达显著降低。我们的研究证实，仇恨言论通常由侵入对话的外部个体表达。相反，第三方干扰对话时间接类型的仇恨言论表达并不常见。我们还发现，反击性话语与一种通常避免冲突且非私下持有的话语类型密切相关。
