Dogwhistles are coded expressions that simultaneously convey one meaning to a broad audience and a second one, often hateful or provocative, to a narrow in-group; they are deployed to evade both political repercussions and algorithmic content moderation. For example, in the sentence 'we need to end the cosmopolitan experiment,' the word 'cosmopolitan' likely means 'worldly' to many, but secretly means 'Jewish' to a select few. We present the first large-scale computational investigation of dogwhistles. We develop a typology of dogwhistles, curate the largest-to-date glossary of over 300 dogwhistles with rich contextual information and examples, and analyze their usage in historical U.S. politicians' speeches. We then assess whether a large language model (GPT-3) can identify dogwhistles and their meanings, and find that GPT-3's performance varies widely across types of dogwhistles and targeted groups. Finally, we show that harmful content containing dogwhistles avoids toxicity detection, highlighting online risks of such coded language. This work sheds light on the theoretical and applied importance of dogwhistles in both NLP and computational social science, and provides resources for future research in modeling dogwhistles and mitigating their online harms.

翻译：狗哨是一种编码表达，能同时向广泛受众传递一种含义，而向狭窄的内群体传递第二种（常为仇恨性或煽动性）含义；它们被用来规避政治后果和算法内容审核。例如，在句子“我们需要结束世界主义实验”中，“世界主义”一词对多数人而言可能意为“博学的”，但对少数人却隐秘地指代“犹太人”。我们提出了首个针对狗哨的大规模计算研究。我们构建了狗哨的类型学，整理了迄今为止最大规模的包含超过300个狗哨的词汇表，并附有丰富的语境信息和实例，分析了它们在美国历史政治人物演讲中的使用情况。随后，我们评估了大型语言模型（GPT-3）识别狗哨及其含义的能力，发现GPT-3的表现因狗哨类型和针对群体而异。最后，我们表明含有狗哨的有害内容能够避开有毒内容检测，凸显了此类编码语言在线上环境的风险。本研究揭示了狗哨在自然语言处理与计算社会科学中的理论和应用重要性，并为未来建模狗哨及减轻其在线危害的研究提供了资源。