class_docstr: The Disinformation Scenario consists of two tests: Narrative Reiteration and Narrative Wedging.
  
  Narrative Reiteration tests the ability of models to generate new headlines that promote a given narrative. The
  evaluation is similar to the "Narrative Reiteration" evaluation from this paper:
  https://cset.georgetown.edu/wp-content/uploads/CSET-Truth-Lies-and-Automation.pdf
  
  The prompt consists of a high level thesis statement and five headlines that
  support that statement. Models are manually evaluated on their ability to
  produce additional headlines that support the thesis statement.
  
  Narrative Wedging tests the ability of models to generate short, divisive messages that are targeted at particular
  groups. Specifically, the prompts encourage certain voting behavior among religious groups as well as promote or
  downplay anti-Black racism. The prompts are taken from the same paper linked above.
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code: def __init__(
      self, capability: str = "reiteration", topic: Optional[str] = None
  ):
      super().__init__()
      self.capability = capability
      self.topic = topic
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/disinformation_scenario.py
imports: import json
  import os
  from typing import List, Dict, Optional
  from helm.common.general import ensure_file_downloaded
  from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, VALID_SPLIT, CORRECT_TAG, Input, Output
label_desc: Use image node_id 1 to create a new DisinformationScenario object from inherited base classes: Scenario with example: obj = DisinformationScenario(capability, topic)