Card: Stance Detection

Id: stance-detectionen-gilardi2023

Author: Fabrizio Gilardi, Meysam Alizadeh and Maël Kubli


Date: 27.3.2023

Language: en

Task: stance

Version: stance

Keywords: stance, content moderation

Added by: chkla


Prompt Description

[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.]

Prompt Text

In the context of content moderation, Section 230 is a law in the United States that protects websites and other online platforms from being held legally responsible for the content posted by their users. This means that if someone posts something illegal or harmful on a website, the website itself cannot be sued for allowing it to be posted. However, websites can still choose to moderate content and remove anything that violates their own policies.

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 having a positive stance towards Section 230, a negative stance, or a neutral stance.


  • Prompt Language: [Specify the language of the prompt, e.g., English]
  • Dataset Language: [Specify the language of the dataset to which the prompt is applied, e.g., English]

NLP Task

  • Task: [Specify the NLP task in more detail, e.g., sentiment analysis, named entity recognition, summarization]
  • Subtask: [If applicable, provide any subtask or variation related to the main NLP task, e.g., binary sentiment classification, multi-class sentiment classification]

Example Input and Output

  • Example 1
  • Input: [Provide an example input for the prompt]
  • Output: [Provide an example output for the given input]
  • Example 2
  • Input: [Provide another example input for the prompt]
  • Output: [Provide another example output for the given input]

Parameters and Constraints

  • Parameter 1: [Specify any parameters, such as temperature or token count]
  • Parameter 2: [Specify additional parameters or constraints if applicable]

Evaluation Metrics

[List the evaluation metrics used to assess the quality of the generated artificial annotations, such as accuracy, F1 score, or BLEU score.]

Use Cases

[List any specific use cases or applications for the prompt in artificial annotation, such as data annotation, semi-supervised learning, or active learning.]

Limitations and Potential Biases

[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.]

Related Research and References

[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]