We draw insights from the social psychology literature to identify two facets of Twitter deliberations about migrants, i.e., perceptions about migrants and behaviors towards mi-grants. Our theoretical anchoring helped us in identifying two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants. We have employed unsuper-vised and supervised approaches to identify these perceptions and behaviors. In the domain of applied NLP, our study of-fers a nuanced understanding of migrant-related Twitter de-liberations. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outper-formed other models. Additionally, we argue that tweets con-veying antipathy or animosity can be broadly considered hate speech towards migrants, but they are not the same. Thus, our approach has fine-tuned the binary hate speech detection task by highlighting the granular differences between perceptual and behavioral aspects of hate speeches.

翻译：我们从社会心理学文献中汲取了深刻的见解,以辨别有关移民的Twitter讨论的两个方面,即对移民的看法和对准移民的行为。我们的理论定位帮助我们辨别了社交媒体使用者对移民的两种普遍看法(即同情和反感)和两种主导行为(即团结和敌意 ) 。我们采用了不受监督和监督的方法来辨别这些看法和行为。在应用的NLP领域,我们对与移民有关的Twitter解放的细微理解的研究。我们提议的基于变压器的模式,即BERT+CNN, 报告了一个F1点的0.76和优于其他模式。此外,我们争辩说,推特的反感或敌意可被广泛视为对移民的仇恨言论,但两者不同。因此,我们的方法通过强调仇恨言论的感知和行为方面之间的微分差异,对双调仇恨言论的检测任务进行了精确调整。