Detecting Stance in Tweets

# Detecting Stance in Tweets
# Based on Mohammad et al., SemEval 2016
# Paper: https://aclanthology.org/S16-1003/
# Dataset: http://alt.qcri.org/semeval2016/task6/
#
# This task asks annotators to classify the stance expressed in a tweet
# toward a given target (topic or entity) as favor, against, or neither.
#
# Stance Labels:
# - Favor: The tweet expresses support for the target
# - Against: The tweet expresses opposition to the target
# - Neither: The tweet does not clearly express a stance toward the target

annotation_task_name: "Detecting Stance in Tweets"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
export_annotation_format: "jsonl"

port: 8000

annotation_schemes:
  - annotation_type: radio
    name: stance
    description: "What stance does this tweet express toward the target?"
    labels:
      - name: "Favor"
        tooltip: "The tweet expresses support for or positive stance toward the target"
        key_value: "1"
      - name: "Against"
        tooltip: "The tweet expresses opposition to or negative stance toward the target"
        key_value: "2"
      - name: "Neither"
        tooltip: "The tweet does not clearly express a stance, or is unrelated to the target"
        key_value: "3"

annotation_instructions: |
  You will be shown a tweet and a target topic or entity. Your task is to determine
  the stance of the tweet author toward the target. Note that stance is about the
  author's position, not sentiment -- a tweet can be negative in tone but still
  express favor toward the target.

user_config:
  allow_all_users: true
max_annotations_per_user: 50
num_annotators_per_item: 2