How annotation styles influence content and preferences

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Many factors affect the quality of human-annotations, including: (i) unreliable annotators, (ii) poorly specified annotation tasks and guidelines, (iii) poor category design (categories that are too broad, too narrow, or too vague), or (iv) insufficient information to make a reliable assessment (Che”
Rehumanized Crowdsourcing: A Labeling Framework Addressing Bias and Ethics in Machine Learning (2019, background): “In regards to biases originating from crowd work, several prior works have looked at biases introduced by the process of labeling via crowds (e.g., [3, 5, 6, 8, 9, 23, 24, 26, 34]).”
Between Subjectivity and Imposition (2020, background): “However, these systems require, in many cases, the intervention of human discretion in their deployment [2, 64], and even more frequently, in their creation [18, 22, 35, 36, 39, 48, 51, 61, 66, 67].”