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SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles

Summary

This shared task moves propaganda detection from whole articles down to the level of individual text fragments. Earlier work mostly labelled entire documents or news sources, sometimes through noisy distant supervision. The organizers instead released news articles in which professional annotators marked the exact spans where persuasion techniques appear. The task has two parts. Span Identification (SI) is binary sequence tagging: find the propagandistic fragments in a plain document. Technique Classification (TC) is multi-class labelling: assign one of 14 techniques to a given span, using the document as context. The 14 techniques come from an earlier, larger inventory of 18, with rare and similar categories merged. Participation was large: 250 teams registered and 44 submitted test runs. The strongest systems relied on pre-trained Transformers combined in ensembles. SI turned out to be the easier subtask, and every submission beat the baseline. TC was much harder, and some teams fell below the baseline. The task matters because it supplies a benchmark and a technique taxonomy for explainable, fragment-level analysis of manipulative news.

Key points

  • Two subtasks:
    • SI: binary span tagging.
    • TC: 14-way classification of a given span in its document context.
  • Participation:
    • 250 teams registered and 44 submitted on the test set.
    • There were 66 submissions in total: 35 for SI and 31 for TC.
    • 32 teams wrote system description papers.
  • Technique inventory: the 14 labels include Loaded language, Name calling, Repetition, Doubt, Flag-waving, Slogans, Appeal to authority and Thought-terminating cliché. Two labels are merged groups: Whataboutism/straw man/red herring and Bandwagon/reductio ad hitlerum.
  • Prior attempts: a pilot datathon and the NLP4IF 2019 shared task treated the problem as one combined task. Their best F1 scores were only 10.43 and 24.88, which motivated splitting the task into subtasks.
  • Winning approaches and next steps: the top systems used Transformer models such as BERT and RoBERTa, usually in ensembles. Future plans include more techniques, more data, and datasets in other languages. A demo system called Prta is available online.

Interesting ideas

  • I1. Decompose a task that is too hard end to end. When the joint "find and label" task gave very low scores, the organizers separated localization from classification. Each part can then be studied and improved on its own, which suits any span-plus-label annotation problem.
  • I2. Merge rare classes after annotation, not before. Annotators worked with the finer 18-label scheme, and sparse, similar labels were combined afterwards. The fine-grained annotations are kept, while the training data becomes less sparse.
  • I3. Only include labels that can be judged from the text itself. Techniques that require comparing outside sources, such as card stacking, were excluded. This keeps annotation consistent and the task solvable, and is a useful rule when designing taxonomies for manipulation or bias.
  • I4. Annotate minimal spans rather than whole documents or arguments. Span-level labels avoid the noise of source-level distant supervision. They also yield explanations a reader can see, which helps media-literacy tools.
  • I5. Treat fragment-level analysis as a complement to document-level methods. Span detectors can feed article- or source-level classifiers rather than replace them, making the components reusable in larger pipelines.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.semeval-1.186

@inproceedings{martino2020semeval,
  title = {{SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles}},
  author = {Martino, Giovanni Da San and Barrón-Cedeño, Alberto and Wachsmuth, Henning and Petrov, Rostislav and Nakov, Preslav},
  booktitle = {Proceedings of the Fourteenth Workshop on Semantic Evaluation},
  year = {2020},
  doi = {10.18653/v1/2020.semeval-1.186},
  url = {https://doi.org/10.18653/v1/2020.semeval-1.186}
}

References

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