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--- |
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license: apache-2.0 |
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language: en |
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pretty_name: Manipulative Language Detection Dataset |
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task_categories: |
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- text-classification |
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- text-scoring |
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tags: |
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- manipulative-language |
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- nlp |
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- binary-classification |
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- dialogue |
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- transformer |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Manipulative Language Detection Dataset |
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This dataset contains annotated text examples for detecting manipulative language at both sentence and dialogue levels. |
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## Dataset Description |
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The Manipulative Language Detection Dataset is designed to help train and evaluate transformer-based models in identifying manipulative language patterns. The dataset consists of two complementary components: |
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1. **Sentence-level data**: Individual sentences labeled as manipulative (1) or non-manipulative (0) |
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2. **Dialogue-level data**: Conversational exchanges with annotations for manipulation techniques, victim vulnerabilities, and context |
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The dataset is sourced from various dialogues, including movie scripts and other conversational contexts. Each entry is thoroughly annotated for manipulation attributes. |
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This work is aligned with recent research on mental manipulation detection, such as the MentalManip dataset (Wang et al., 2024). |
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## Data Format |
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### Sentence-Level Data |
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Each entry contains: |
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- Inner ID |
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- Unique ID |
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- Sentence text |
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- Binary manipulation label (1=manipulative, 0=non-manipulative) |
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- Original context (dialogue source) |
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- Movie name |
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- Annotator agreement metrics |
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- Manipulation technique categorization (persuasion, intimidation, seduction, etc.) |
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- Victim/vulnerability annotations |
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- Confidence scores |
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### Dialogue-Level Data |
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Each entry contains: |
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- Inner ID |
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- Unique ID |
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- Dialogue exchange with speaker identification |
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- Manipulation classification (binary) |
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- Movie Name |
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- Annotator agreement metrics |
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- Manipulation technique categorization (persuasion, intimidation, seduction, etc.) |
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- Victim/vulnerability annotations |
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- Confidence scores |
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## Manipulation Techniques |
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The dataset identifies several manipulation techniques, including: |
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- Persuasion or Seduction |
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- Accusation |
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- Denial |
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- Evasion |
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- Feigning Innocence |
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- Rationalization |
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- Playing the Victim Role |
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- Playing the Servant Role |
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- Shaming or Belittlement |
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- Intimidation |
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- Brandishing Anger |
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## Targeted Vulnerability |
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The dataset identifies several vulnerability targets, including: |
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- Over-responsibility |
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- Over-intellectualization |
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- Naivete |
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- Low self-esteem |
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- Dependency |
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## Usage |
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This dataset is designed for training transformer-based models to detect manipulative language. Researchers can use it to: |
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1. Train binary classifiers at the sentence level and/or dialogue level |
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2. Develop more sophisticated models that identify specific manipulation techniques |
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3. Study the contextual nature of manipulation in dialogues |
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4. Evaluate models' performance across different manipulation strategies |
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### Evaluation Metrics |
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Models can be evaluated using standard metrics including: |
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- Accuracy |
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- F1 score |
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- Precision |
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- Recall |
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- Balanced accuracy |
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Research has shown that current language models, even advanced ones like GPT-4, struggle with correctly identifying and classifying manipulative language patterns, highlighting the importance of specialized datasets and models for this task. |
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### Loading the Dataset |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("pauladroghoff/manipulative-language-detection") |
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# Access sentence-level data |
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sentence_data = dataset["sentence_level"] |
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# Access dialogue-level data |
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dialogue_data = dataset["dialogue_level"] |
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