Stance detection determines whether the author of a piece of text is in favor
of, against, or neutral towards a specified target, and can be used to gain
valuable insights into social media. The ubiquitous indirect referral of
targets makes this task challenging, as it requires computational solutions to
model semantic features and infer the corresponding implications from a literal
statement. Moreover, the limited amount of available training data leads to
subpar performance in out-of-domain and cross-target scenarios, as data-driven
approaches are prone to rely on superficial and domain-specific features. In
this work, we decompose the stance detection task from a linguistic
perspective, and investigate key components and inference paths in this task.
The stance triangle is a generic linguistic framework previously proposed to
describe the fundamental ways people express their stance. We further expand it
by characterizing the relationship between explicit and implicit objects. We
then use the framework to extend one single training corpus with additional
annotation. Experimental results show that strategically-enriched data can
significantly improve the performance on out-of-domain and cross-target
evaluation.

本文从语言学角度分解了立场检测任务，并详细研究了该任务中的关键组件和推理路径。作者扩展了先前提出的立场三角形语言框架，并使用该框架扩展了单一的训练语料库以增加注释。 实验结果表明，战略丰富的数据可以显着提高跨领域和跨目标评估的性能。

使用扩展立场三角框架指导计算立场检测

Guiding Computational Stance Detection with Expanded Stance Triangle  Framework

Dehumanization is a pernicious psychological process that often leads to
extreme intergroup bias, hate speech, and violence aimed at targeted social
groups. Despite these serious consequences and the wealth of available data,
dehumanization has not yet been computationally studied on a large scale.
Drawing upon social psychology research, we create a computational linguistic
framework for analyzing dehumanizing language by identifying linguistic
correlates of salient components of dehumanization. We then apply this
framework to analyze discussions of LGBTQ people in the New York Times from
1986 to 2015. Overall, we find increasingly humanizing descriptions of LGBTQ
people over time. However, we find that the label homosexual has emerged to be
much more strongly associated with dehumanizing attitudes than other labels,
such as gay. Our proposed techniques highlight processes of linguistic
variation and change in discourses surrounding marginalized groups.
Furthermore, the ability to analyze dehumanizing language at a large scale has
implications for automatically detecting and understanding media bias as well
as abusive language online.

本研究根据社会心理学的研究，创建了一个计算语言分析框架来分析去人性化言论，然后将这个框架应用于分析 New York Times 自 1986 年至 2015 年关于 LGBTQ 人士的文章。总体而言，我们发现随着时间的推移，LGBTQ 人士的被人性化描述越来越多。但是，我们发现与其他标签（例如同性恋）相比，“同性恋” 这个标签更容易被视为去人性化的态度。本文所提出的技术揭示了围绕边缘化群体的语言变化和变异的过程，并且大规模分析非人性化言论的能力对于自动检测和理解媒体偏见以及虐待性言论的意义重大。