Understanding and Adapting to Human Judges Involved in Story-based Interpretation
In the realm of crowdsourcing, annotators involved in narrative-sorting tasks, such as annotating tweets, often experience a wide range of emotions that can significantly impact their well-being and task performance. A recent study sheds light on this emotional impact, revealing common reactions like fear, confusion, distress, and moral discomfort[1].
These emotional responses extend beyond simple emotional labels to include complex affective states and moral judgments that annotators experience while processing sensitive or disturbing content. This emotional burden can contribute to psychological discomfort or harm, affecting annotators' mental health and potentially their annotation quality[1].
The study also highlights the role of these emotional responses in shaping annotators' sensemaking processes. For instance, encountering morally charged or sensitive content in tweets may influence how annotators make meaning and categorize information, impacting the consistency and reliability of the data they produce[1].
Moreover, the presence of AI assistance, like LLM-generated annotation suggestions, can alter annotators’ labeling behavior. While these tools can boost annotator confidence, they can also lead to acceptance bias, where annotators rely heavily on machine suggestions, impacting distributed label validity and downstream analyses[3].
In terms of the annotators' well-being and performance, the emotional strain they face can lead to negative psychological effects such as fear or distress, which are often underacknowledged in standard safety definitions for annotation tasks. This can result in a trade-off where annotators perform the task with altered emotional states that may reduce their overall effectiveness or engagement over time[1].
The study found that readers had different reactions to humor in the narrative-sorting task. Interestingly, the research did not find a measurable stress response for readers during the narrative-sorting task[1].
The study sheds light on the emotional impact of human readers in the context of crowdsourcing, underlining the role of readers' emotional responses in shaping their decisions during the narrative-sorting task. It also provides insights into the perceptive capabilities of human readers in crowdwork, highlighting the significance of readers' perceptive capabilities in crowdwork[1].
The findings of this study are strongly relevant to similar subjective annotation settings involving sensitive or complex social content, such as narrative-sorting tweet annotation tasks. As the use of crowdsourcing continues to grow, it is crucial to address the emotional well-being of annotators and develop support mechanisms to maintain their mental health while preserving data quality.
[1] [Citation for the study] [3] [Citation for the study on AI assistance and annotators’ labeling behavior]
- The emotional responses experienced by annotators during the processing of sensitive or disturbing content in narrative-sorting tasks can extend beyond simple emotions to complex affective states and moral judgments, which can potentially impact their mental health and the quality of data they produce.
- In the realm of crowdworking, the presence of AI assistance, such as LLM-generated annotation suggestions, can impact annotators' labeling behavior, potentially leading to acceptance bias and affecting the validity of distributed labels and downstream analyses.
- The study emphasizes the significance of readers' emotional responses and perceptive capabilities in shaping their decisions during the narrative-sorting task, suggesting that these elements play a crucial role in similar subjective annotation settings involving complex social content. As the use of crowdsourcing continues, addressing the emotional well-being of annotators and developing support mechanisms to maintain their mental health while preserving data quality becomes increasingly important.
