Author: Fabrizio Gilardi, Meysam Alizadeh and Maël Kubli
Keywords: frames, content moderation
Added by: chkla
[Briefly describe the purpose of the prompt and the context in which it is intended to be used, especially in the context of artificial annotation with generative models.]
Content moderation can be seen from two different perspectives: • Content moderation can be seen as a PROBLEM; for example, as a restriction of free speech • Content moderation can be seen as a SOLUTION; for example, as a protection from harmful speech
For each tweet in the sample, follow these instructions: 1. Carefully read the text of the tweet, paying close attention to details. 2. Classify the tweet as describing content moderation as a problem, as a solution, or neither.
Tweets should be classified as describing content moderation as a PROBLEM if they emphasize negative effects of content moderation, such as restrictions to free speech, or the biases that can emerge from decisions regarding what users are allowed to post.
Tweets should be classified as describing content moderation as a SOLUTION if they emphasize positive effects of content moderation, such as protecting users from various kinds of harmful content, including hate speech, misinformation, illegal adult content, or spam.
Tweets should be classified as describing content moderation as NEUTRAL if they do not emphasize possible negative or positive effects of content moderation, for example if they simply report on the content moderation activity of social media platforms without linking them to potential advantages or disadvantages for users or stakeholders.
[List the evaluation metrics used to assess the quality of the generated artificial annotations, such as accuracy, F1 score, or BLEU score.]
[List any specific use cases or applications for the prompt in artificial annotation, such as data annotation, semi-supervised learning, or active learning.]
[Briefly discuss any limitations or potential biases associated with the prompt, as well as any steps taken to mitigate them, in the context of artificial annotation with generative models.]
[List any relevant research papers, articles, or resources that informed the creation of the prompt or are closely related to it, especially in the area of artificial annotation with generative models. Include proper citations where applicable.]
Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli (2023) "ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks" [Paper]