Political Discourse Analysis (AgoraSpeech)

# Political Discourse Analysis (AgoraSpeech-style)
# Based on Sermpezis et al., arXiv 2025
# Paper: https://arxiv.org/abs/2501.06265
#
# Multi-task annotation covering 6 NLP tasks:
# 1. Sentiment Analysis - emotional tone
# 2. Polarization Detection - divisive language
# 3. Populism Detection - populist rhetoric
# 4. Topic Identification - subject matter
# 5. Named Entity Recognition - persons, organizations, locations
# 6. Text Classification - speech type/category
#
# Designed for political speeches, campaign statements, and public discourse

port: 8000
server_name: localhost
task_name: "Political Discourse Analysis"

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

output_file: annotations.json

annotation_schemes:
  # Task 1: Sentiment Analysis
  - annotation_type: radio
    name: sentiment
    description: "What is the overall emotional tone of this text?"
    labels:
      - Positive
      - Negative
      - Neutral
      - Mixed
    keyboard_shortcuts:
      Positive: "1"
      Negative: "2"
      Neutral: "3"
      Mixed: "4"
    tooltips:
      Positive: "Optimistic, hopeful, praising, or celebratory tone"
      Negative: "Critical, pessimistic, condemning, or fearful tone"
      Neutral: "Balanced, factual, or emotionally detached tone"
      Mixed: "Contains both positive and negative sentiment"

  # Task 2: Polarization Detection
  - annotation_type: radio
    name: polarization
    description: "Does this text contain polarizing or divisive language?"
    labels:
      - "Highly polarizing"
      - "Moderately polarizing"
      - "Slightly polarizing"
      - "Not polarizing"
    keyboard_shortcuts:
      "Highly polarizing": "h"
      "Moderately polarizing": "m"
      "Slightly polarizing": "s"
      "Not polarizing": "n"
    tooltips:
      "Highly polarizing": "Strong us-vs-them rhetoric, demonization of opponents, inflammatory language"
      "Moderately polarizing": "Clear partisan framing, criticism of opposing views"
      "Slightly polarizing": "Some partisan elements but mostly balanced"
      "Not polarizing": "Neutral, inclusive, or consensus-seeking language"

  # Task 3: Populism Detection
  - annotation_type: multiselect
    name: populism_markers
    description: "Select any populist rhetoric markers present in the text"
    labels:
      - "People vs. Elite"
      - "Anti-establishment"
      - "Crisis framing"
      - "Nationalism/Nativism"
      - "Direct democracy appeal"
      - "Charismatic leadership"
      - "None detected"
    tooltips:
      "People vs. Elite": "Framing ordinary people against corrupt/out-of-touch elites"
      "Anti-establishment": "Criticism of mainstream parties, media, or institutions"
      "Crisis framing": "Portraying situation as urgent crisis requiring immediate action"
      "Nationalism/Nativism": "Appeals to national identity, sovereignty, or anti-immigration sentiment"
      "Direct democracy appeal": "Bypassing institutions, appealing directly to 'the people's will'"
      "Charismatic leadership": "Positioning speaker as unique voice/savior of the people"
      "None detected": "No populist rhetoric markers identified"

  # Task 4: Topic Identification
  - annotation_type: multiselect
    name: topics
    description: "What topics are discussed in this text? (select all that apply)"
    labels:
      - Economy/Jobs
      - Healthcare
      - Immigration
      - Education
      - Environment/Climate
      - Foreign Policy/Security
      - Social Issues/Rights
      - Corruption/Ethics
      - Infrastructure
      - Taxes/Budget
      - Other
    tooltips:
      "Economy/Jobs": "Employment, wages, economic growth, business"
      "Healthcare": "Health policy, insurance, medical care"
      "Immigration": "Immigration policy, borders, refugees"
      "Education": "Schools, universities, education policy"
      "Environment/Climate": "Climate change, environmental protection, energy"
      "Foreign Policy/Security": "International relations, defense, national security"
      "Social Issues/Rights": "Civil rights, equality, social justice, family values"
      "Corruption/Ethics": "Political corruption, ethics, accountability"
      "Infrastructure": "Roads, bridges, public works, broadband"
      "Taxes/Budget": "Tax policy, government spending, budget"
      "Other": "Topics not covered above"

  # Task 5: Named Entity Recognition
  - annotation_type: span
    name: entities
    description: "Highlight named entities in the text"
    labels:
      - Person
      - Organization
      - Location
      - Political Party
      - Event
    label_colors:
      Person: "#3b82f6"
      Organization: "#22c55e"
      Location: "#f59e0b"
      "Political Party": "#8b5cf6"
      Event: "#ec4899"
    tooltips:
      Person: "Names of politicians, public figures, or individuals"
      Organization: "Government bodies, companies, NGOs, institutions"
      Location: "Countries, cities, regions, places"
      "Political Party": "Names of political parties or movements"
      Event: "Elections, summits, protests, historical events"
    allow_overlapping: false

  # Task 6: Speech Classification
  - annotation_type: radio
    name: speech_type
    description: "What type of political communication is this?"
    labels:
      - Campaign speech
      - Policy statement
      - Parliamentary address
      - Interview/Debate
      - Social media post
      - Press release
      - Other
    tooltips:
      "Campaign speech": "Electoral campaign rally or event speech"
      "Policy statement": "Official policy announcement or position paper"
      "Parliamentary address": "Speech in legislative body or official chamber"
      "Interview/Debate": "Response to questions or debate format"
      "Social media post": "Tweet, Facebook post, or similar short-form content"
      "Press release": "Official statement to media"
      "Other": "Other type of political communication"

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