Multilingual Characterization and Extraction of Narratives

# Multilingual Characterization and Extraction of Narratives
# Based on Piskorski et al. (2025), SemEval-2025 Task 10
# Paper: https://aclanthology.org/2025.semeval-1.331/
# Dataset: https://propaganda.math.unipd.it/semeval2025task10/ (access via registration)
#
# Honest single-page adaptation of SemEval-2025 Task 10, which is organized
# into three subtasks over online news articles (Ukraine-Russia War and
# Climate Change domains; Bulgarian, English, Hindi, Portuguese, Russian):
#   ST1 Entity Framing        - label the role each named entity plays
#                               (protagonist / antagonist / innocent).
#   ST2 Narrative Classification - assign topic-specific narrative labels.
#   ST3 Narrative Extraction  - write a short justification (<= 80 words)
#                               for the article's dominant narrative.

annotation_task_name: "Multilingual Characterization and Extraction of Narratives"
task_dir: "."

data_files:
  - sample-data.json

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

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  # Subtask 1: Entity Framing - highlight each named entity and assign the
  # role it plays. SemEval-2025 Task 10 uses a fine-grained taxonomy nested
  # under three main roles; this adaptation exposes the three main roles.
  - annotation_type: span
    name: entity_framing
    description: "Highlight each key named entity and label the role it plays in the narrative."
    labels:
      - "Protagonist"
      - "Antagonist"
      - "Innocent"
    tooltips:
      "Protagonist": "A positively framed actor: e.g. guardian, martyr, peacemaker, rebel, underdog, virtuous"
      "Antagonist": "A negatively framed actor: e.g. instigator, conspirator, tyrant, foreign adversary, traitor, terrorist, deceiver, corrupt, incompetent"
      "Innocent": "A party framed as harmed or blameless: e.g. victim, exploited, scapegoat, forgotten"

  # Subtask 2: Narrative Classification - assign topic-specific narrative
  # labels to the whole document.
  - annotation_type: multiselect
    name: narrative_classification
    description: "Select all narrative categories present in this article."
    labels:
      - "Blaming the West / NATO"
      - "Discrediting Ukraine"
      - "Praising / justifying Russia"
      - "Amplifying war consequences"
      - "Climate change is not real / not urgent"
      - "Criticism of climate policies"
      - "Criticism of climate movement"
      - "Green technology is unreliable"
      - "Other narrative"
    tooltips:
      "Blaming the West / NATO": "Ukraine-Russia domain: narratives attributing the conflict to Western actors"
      "Discrediting Ukraine": "Ukraine-Russia domain: narratives undermining Ukraine or its leadership"
      "Praising / justifying Russia": "Ukraine-Russia domain: narratives defending Russian actions"
      "Amplifying war consequences": "Ukraine-Russia domain: narratives emphasizing costs or threats of the war"
      "Climate change is not real / not urgent": "Climate domain: denial or downplaying narratives"
      "Criticism of climate policies": "Climate domain: narratives attacking climate regulations or measures"
      "Criticism of climate movement": "Climate domain: narratives attacking activists or institutions"
      "Green technology is unreliable": "Climate domain: narratives casting doubt on renewable/green technology"
      "Other narrative": "A dominant narrative not captured by the categories above"

  # Subtask 3: Narrative Extraction - free-text justification of the
  # dominant narrative (SemEval limits responses to ~80 words).
  - annotation_type: text
    name: narrative_extraction
    description: "In up to 80 words, describe and justify the dominant narrative of the article, grounded in its text."

annotation_instructions: |
  You will be shown a news article passage. Your task follows the three
  subtasks of SemEval-2025 Task 10:
  1. Entity Framing: highlight each key named entity and label it as
     Protagonist, Antagonist, or Innocent based on how the article frames it.
  2. Narrative Classification: select all narrative categories that the
     article promotes.
  3. Narrative Extraction: write a short justification (up to 80 words) for
     the article's dominant narrative, grounded in the text.

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="background: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #0369a1;">News Text:</strong>
      <p style="font-size: 16px; line-height: 1.7; margin: 8px 0 0 0;">{{text}}</p>
    </div>
    <div style="background: #f0fdf4; border: 1px solid #bbf7d0; border-radius: 8px; padding: 12px; margin-bottom: 8px;">
      <strong style="color: #166534;">Source:</strong> <span>{{source}}</span>
      &nbsp; | &nbsp;
      <strong style="color: #166534;">Language:</strong> <span>{{language}}</span>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 2
allow_skip: true
skip_reason_required: false