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WANLP22T3Propaganda.py
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WANLP22T3Propaganda.py
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import json
from pathlib import Path
from llmebench.datasets.dataset_base import DatasetBase
from llmebench.tasks import TaskType
class WANLP22T3PropagandaDataset(DatasetBase):
def __init__(self, techniques_path=None, **kwargs):
# Get the path to the file listing the target techniques
self.techniques_path = Path(techniques_path) if techniques_path else None
super(WANLP22T3PropagandaDataset, self).__init__(**kwargs)
@staticmethod
def metadata():
return {
"language": "ar",
"citation": """@inproceedings{alam2022overview,
title={Overview of the $\\{$WANLP$\\}$ 2022 Shared Task on Propaganda Detection in $\\{$A$\\}$rabic},
author={Alam, Firoj and Mubarak, Hamdy and Zaghouani, Wajdi and Da San Martino, Giovanni and Nakov, Preslav and others},
booktitle={Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)},
pages={108--118},
year={2022},
organization={Association for Computational Linguistics}
}""",
"link": "https://gitlab.com/araieval/propaganda-detection",
"license": "Research Purpose Only",
"splits": {
"test": "task1_test_gold_label_final.json",
"train": "task1_train.json",
},
"task_type": TaskType.MultiLabelClassification,
"class_labels": [
"no technique",
"Smears",
"Exaggeration/Minimisation",
"Loaded Language",
"Appeal to fear/prejudice",
"Name calling/Labeling",
"Slogans",
"Repetition",
"Doubt",
"Obfuscation, Intentional vagueness, Confusion",
"Flag-waving",
"Glittering generalities (Virtue)",
"Misrepresentation of Someone's Position (Straw Man)",
"Presenting Irrelevant Data (Red Herring)",
"Appeal to authority",
"Whataboutism",
"Black-and-white Fallacy/Dictatorship",
"Thought-terminating cliché",
"Causal Oversimplification",
],
}
@staticmethod
def get_data_sample():
return {"input": "Tweet", "label": ["no technique"]}
def get_predefined_techniques(self):
# Load a pre-defined list of propaganda techniques, if available
if self.techniques_path and self.techniques_path.exists():
self.techniques_path = self.resolve_path(self.techniques_path)
with open(self.techniques_path, "r", encoding="utf-8") as f:
techniques = [label.strip() for label in f.readlines()]
else:
techniques = [
"no technique",
"Smears",
"Exaggeration/Minimisation",
"Loaded Language",
"Appeal to fear/prejudice",
"Name calling/Labeling",
"Slogans",
"Repetition",
"Doubt",
"Obfuscation, Intentional vagueness, Confusion",
"Flag-waving",
"Glittering generalities (Virtue)",
"Misrepresentation of Someone's Position (Straw Man)",
"Presenting Irrelevant Data (Red Herring)",
"Appeal to authority",
"Whataboutism",
"Black-and-white Fallacy/Dictatorship",
"Thought-terminating cliché",
"Causal Oversimplification",
]
return techniques
def load_data(self, data_path):
data_path = self.resolve_path(data_path)
data = []
with open(data_path, mode="r", encoding="utf-8") as infile:
json_data = json.load(infile)
for index, tweet in enumerate(json_data):
text = tweet["text"]
label = tweet["labels"]
data.append({"input": text, "label": label, "line_number": index})
return data