Rumor Stance Detection (PHEME)

# Rumor Stance Detection (PHEME)
# Based on Zubiaga et al., EMNLP 2018
# Paper: https://aclanthology.org/D18-1401/
# Dataset: https://figshare.com/articles/dataset/PHEME_dataset
#
# This task classifies how users respond to rumors on social media.
# Understanding stance helps assess rumor veracity and spread patterns.
#
# Stance Labels (SDQC):
# - Support: The reply endorses or agrees with the rumor
# - Deny: The reply refutes or contradicts the rumor
# - Query: The reply questions the rumor's veracity
# - Comment: The reply discusses the rumor without taking a stance
#
# Annotation Guidelines:
# 1. Read the original rumor (source claim) first
# 2. Read the reply in context of the conversation thread
# 3. Focus on the reply's stance toward the SOURCE rumor
# 4. Support: Agrees, shares, adds confirming information
# 5. Deny: Disagrees, corrects, provides counter-evidence
# 6. Query: Asks for evidence, expresses doubt, questions truth
# 7. Comment: Neutral discussion, jokes, tangential remarks
#
# Note: A reply might support the rumor while denying someone else's
# denial. Focus on stance toward the ORIGINAL claim.

port: 8000
server_name: localhost
task_name: "Rumor Stance Detection"

data_files:
  - sample-data.json
id_key: id
text_key: reply

output_file: annotations.json

annotation_schemes:
  # Step 1: Stance classification
  - annotation_type: radio
    name: stance
    description: "What is the stance of this REPLY toward the RUMOR?"
    labels:
      - "Support"
      - "Deny"
      - "Query"
      - "Comment"
    tooltips:
      "Support": "The reply endorses, agrees with, or spreads the rumor as true"
      "Deny": "The reply refutes, contradicts, or provides evidence against the rumor"
      "Query": "The reply questions the rumor, asks for evidence, or expresses doubt"
      "Comment": "The reply discusses the topic without taking a clear stance on truth/falsity"

  # Step 2: Certainty
  - annotation_type: likert
    name: certainty
    description: "How certain does the reply author seem about their stance?"
    min_value: 1
    max_value: 5
    labels:
      1: "Very uncertain"
      2: "Somewhat uncertain"
      3: "Neutral"
      4: "Somewhat certain"
      5: "Very certain"
    tooltips:
      1: "Expresses strong doubt or hedging"
      2: "Shows some hesitation"
      3: "Neutral tone"
      4: "Fairly confident expression"
      5: "Expresses strong conviction"

  # Step 3: Confidence in annotation
  - annotation_type: likert
    name: confidence
    description: "How confident are you in your stance classification?"
    min_value: 1
    max_value: 5
    labels:
      1: "Very uncertain"
      2: "Somewhat uncertain"
      3: "Moderately confident"
      4: "Confident"
      5: "Very confident"

allow_all_users: true
instances_per_annotator: 100
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false