Check-COVID: Fact-Checking COVID-19 News Claims

Fact-checking COVID-19 news claims. Annotators verify claims against evidence, identify supporting/refuting spans, and provide verdicts with explanations. Based on the Check-COVID dataset targeting misinformation during the pandemic.

配置文件config.yaml

# Check-COVID: Fact-Checking COVID-19 News Claims
# Based on Wang et al., Findings ACL 2023
# Paper: https://aclanthology.org/2023.findings-acl.888/
# Dataset: https://github.com/posuer/Check-COVID
#
# This task verifies COVID-19 news claims against scientific evidence.
# Annotators determine if a claim is supported, refuted, or lacks
# sufficient evidence, and highlight relevant evidence spans.
#
# Verdict Labels:
# - Supported: Evidence confirms the claim
# - Refuted: Evidence contradicts the claim
# - Not Enough Evidence: Available evidence is insufficient to verify
#
# Annotation Guidelines:
# 1. Read the COVID-19 claim carefully
# 2. Read the evidence passage thoroughly
# 3. Determine whether the evidence supports or refutes the claim
# 4. Highlight the specific sentences/phrases in the evidence that
#    are most relevant to your verdict
# 5. Write a brief explanation justifying your decision
# 6. Consider scientific nuance: preliminary findings vs established facts
# 7. Be careful with claims about causation vs correlation

annotation_task_name: "Check-COVID: Fact-Checking"
task_dir: "."

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

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

annotation_schemes:
  # Step 1: Verdict
  - annotation_type: radio
    name: verdict
    description: "Based on the evidence, is the COVID-19 claim supported, refuted, or is there not enough evidence?"
    labels:
      - "Supported"
      - "Refuted"
      - "Not Enough Evidence"
    keyboard_shortcuts:
      "Supported": "s"
      "Refuted": "r"
      "Not Enough Evidence": "n"
    tooltips:
      "Supported": "The evidence confirms or is consistent with the claim"
      "Refuted": "The evidence directly contradicts the claim"
      "Not Enough Evidence": "The evidence does not provide sufficient information to verify or refute the claim"

  # Step 2: Evidence highlighting
  - annotation_type: span
    name: evidence_spans
    description: "Highlight the sentence(s) in the evidence that are most relevant to your verdict"
    labels:
      - "Supporting Evidence"
      - "Refuting Evidence"
    label_colors:
      "Supporting Evidence": "#22c55e"
      "Refuting Evidence": "#ef4444"
    tooltips:
      "Supporting Evidence": "Text that supports or confirms the claim"
      "Refuting Evidence": "Text that contradicts or refutes the claim"
    allow_overlapping: false

  # Step 3: Explanation
  - annotation_type: text
    name: explanation
    description: "Briefly explain why the evidence supports, refutes, or is insufficient for the claim"

html_layout: |
  <div style="margin-bottom: 10px; padding: 10px; background: #fef2f2; border-left: 4px solid #ef4444; border-radius: 4px;">
    <strong>Claim:</strong> {{text}}
  </div>
  <div style="margin-bottom: 10px; padding: 10px; background: #f0fdf4; border-left: 4px solid #22c55e; border-radius: 4px;">
    <strong>Evidence:</strong> {{evidence}}
  </div>
  <div style="padding: 6px; background: #f5f5f5; border-radius: 4px; font-size: 13px;">
    <em>Source:</em> {{source}}
  </div>

allow_all_users: true
instances_per_annotator: 60
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false

示例数据sample-data.json

[
  {
    "id": "covid_001",
    "text": "Wearing masks significantly reduces the transmission of COVID-19.",
    "evidence": "A large randomized trial in Bangladesh involving 342,000 participants found that surgical mask distribution and promotion reduced symptomatic SARS-CoV-2 seroprevalence by 11.2%. Villages where surgical masks were distributed showed a 35% reduction in symptomatic seroprevalence among individuals over 60 years old. Cloth masks showed a smaller, non-statistically significant reduction of 5%.",
    "source": "Abaluck et al., Science 2022"
  },
  {
    "id": "covid_002",
    "text": "Hydroxychloroquine is an effective treatment for hospitalized COVID-19 patients.",
    "evidence": "The RECOVERY trial, a large-scale randomized controlled trial conducted in the UK, enrolled 4,716 hospitalized COVID-19 patients. Patients receiving hydroxychloroquine showed no significant difference in 28-day mortality compared to usual care (27.0% vs 25.0%). There was also no evidence of benefit in terms of hospital stay duration or need for mechanical ventilation. The trial was stopped early due to lack of efficacy.",
    "source": "Horby et al., NEJM 2020"
  }
]

// ... and 8 more items

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Clone or download from the repository

快速开始：

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/text/fact-verification/check-covid-fact-checking
potato start config.yaml

详情

标注类型

radiospantext

领域

NLPFact-CheckingPublic Health

应用场景

Fact VerificationMisinformation DetectionCOVID-19 Research

Check-COVID: Fact-Checking COVID-19 News Claims

配置文件config.yaml

示例数据sample-data.json

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详情

标注类型

领域

应用场景

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