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Semeval2021task6 St1

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Detects persuasion techniques in unimodal text by classifying which techniques are present in a given text snippet. It probes the model's ability to perform multilabel classification on propaganda and persuasive content. Use when the user wants to benchmark on SemEval-2021 Task 6 Subtask 1, or asks about evaluating this task. Reports F1-Micro.

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SKILL.md
---
name: semeval2021task6-st1
description: Detects persuasion techniques in unimodal text by classifying which techniques are present in a given text snippet. It probes the model's ability to perform multilabel classification on propaganda and persuasive content. Use when the user wants to benchmark on SemEval-2021 Task 6 Subtask 1, or asks about evaluating this task. Reports F1-Micro.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2105.09284
  bibtex_key: [REDACTED]
  confidence: high
---

# semeval2021task6-st1

> SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images — Dimitrov et al. (2021) (arXiv:2105.09284, 2021)

## What this evaluates

Detects persuasion techniques in unimodal text by classifying which techniques are present in a given text snippet. It probes the model's ability to perform multilabel classification on propaganda and persuasive content.

## Datasets

- **SemEval-2021 Task 6 Subtask 1** — total ?; splits: test (-1)

## Metrics

- `F1-Micro` **(primary)** — range: [0, 1]
  - Micro-averaged F1 score computed over all persuasion technique labels across the dataset.
- `F1-Macro` — range: [0, 1]
  - Macro-averaged F1 score computed over all persuasion technique labels.

## Input / output format

**Input**: Raw text snippet.

**Output**: Multilabel classification: a set of persuasion technique labels (e.g., Loaded Language, Repetition) applicable to the text.

## Scoring recipe

```python
tp = fp = fn = 0
for p, g in zip(predictions, golds):
    for label in all_labels:
        if label in p and label in g: tp += 1
        elif label in p and label not in g: fp += 1
        elif label not in p and label in g: fn += 1
prec = tp / (tp + fp) if (tp + fp) > 0 else 0
rec = tp / (tp + fn) if (tp + fn) > 0 else 0
return 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
```

## Common pitfalls

- Using image features for Subtask 1 is strictly prohibited and leads to disqualification.
- Class imbalance is significant; a majority-class baseline always predicts 'Loaded Language'.

## Evidence (verbatim from paper)

> The evaluation results are shown in Table 3, which also includes two baselines: (i) random, and (ii) majority class. The latter always predicts Loaded Language, as it is the most frequent technique for Subtask 1... Table 3: Results for Subtask 1. The systems are ordered by the official score: F1-micro.

## Citation

```bibtex
@misc{dimitrov2021semevaltask6,
  title={SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images},
  author={Dimitrov et al. (2021)},
  year={2021},
  note={arXiv:2105.09284}
}
```

- arXiv: 2105.09284

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