---
title: "Duluth at SemEval-2020 Task 12 — paper by Pedersen (2020)"
description: "This paper describes the University of Minnesota Duluth entry to the three English subtasks of… 17 references, cited by 2 papers here."
authors: ["Ted Pedersen"]
year: 2020
venue: "Proceedings of the Fourteenth Workshop on Semantic Evaluation"
source: "https://aclanthology.org/2020.semeval-1.255/"
doi: "10.18653/v1/2020.semeval-1.255"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pedersen-2020-duluth-semeval-2020-task-12/"
---
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# Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression

**Authors:** [Ted Pedersen](https://md.research.elamur.ai/free/en/authors/ted-pedersen/) · **Year:** 2020 · **Venue:** [Proceedings of the Fourteenth Workshop on Semantic Evaluation](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2020](https://md.research.elamur.ai/free/en/venues/semeval/2020/)) · **Publisher:** [International Committee on Computational Linguistics (ICCL)](https://md.research.elamur.ai/free/en/publishers/international-committee-on-computational-linguistics-iccl/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.semeval-1.255/) · [PDF](https://aclanthology.org/2020.semeval-1.255.pdf) · [DOI 10.18653/v1/2020.semeval-1.255](https://doi.org/10.18653/v1/2020.semeval-1.255) · [OpenAlex](https://openalex.org/W3044241435)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read

## Summary
This paper describes the University of Minnesota Duluth entry to the three English subtasks of OffensEval–2020. Task A decides whether a tweet is offensive. Task B decides whether an offensive tweet is a targeted insult. Task C decides whether the target is an individual, a group or some other entity. The author deliberately built a plain baseline. It is a Scikit logistic regression classifier over unigram and bigram features, adapted from an earlier Duluth SemEval-2018 tweet system. It was trained only on the distantly supervised SOLID corpus. SOLID gives each tweet a real-valued score rather than a label, so the author converted scores to classes with a strict cutoff of .8, aiming for high precision. The system ranked near the bottom on Task A and in the middle or lower on Tasks B and C. The main contribution is a manual error analysis. It suggests that the gold labels are noisy and that profanity or in-group slurs are often treated as offensive automatically. This raises doubts about whether the 90%+ scores of the top systems reflect genuine progress in detecting offensive language.

## Key points
- **Training data:**
  - SOLID provided 9,089,139 tweets for Task A, 188,974 for Task B and 188,973 for Task C.
  - The manually labeled OLID corpus (13,240 train and 860 test tweets) was deliberately left out, because SOLID was seeded from it and it showed inconsistencies in 2019.
- **Label thresholds:**
  - Task A scores had a median of .25 and a standard deviation of .185.
  - With a .8 cutoff, only about 4% of tweets (356,811) counted as offensive.
  - In Task B, a score below .2 meant "targeted".
- **Official results (macro F1, Duluth vs. top system):**

  | Task | Duluth | Top system | Rank |
  |---|---|---|---|
  | A | .7713 | .92226 | 79th of 85 |
  | B | .53816 | .74618 | 34th of 43 |
  | C | .57437 | .71450 | 24th of 39 |

- **Task A behaviour:**
  - Precision on the OFF class was .896.
  - Accuracy was .845, against .722 for a majority-class baseline.
  - The system missed half (540) of the gold-offensive tweets.
- **Error audit:**
  - The author drew 10 random samples of 10 tweets from the gold-OFF / predicted-NOT cell.
  - Each sample held 2–5 tweets whose offensiveness was reasonably doubtful.
  - In the shared task as a whole, 59 teams exceeded a macro F1 of .90.

## Interesting ideas
- Audit one confusion-matrix cell by hand through repeated random samples, and count how many items have debatable gold labels; this gives a rough estimate of label noise rather than a single anecdote.
- When supervision comes as a continuous confidence score, set a deliberately strict cutoff to obtain a smaller but more precise positive class, accepting lower recall.
- Treat a weak, transparent baseline as a probe, and read near-perfect leaderboard scores as a possible sign that systems are fitting dataset artifacts rather than solving the task.
- Offensiveness depends on who speaks, who is addressed and their social relation, so the same slur can be bonding inside a group and hostile outside it; word-level features cannot capture this.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Run this protocol on cases where Mova disagrees with a treebank or silver annotation, to tell real parser errors apart from annotation mistakes before training on them.
- **I2** (learn): Admit silver trees or frames into training only when witness agreement is well above a bare majority, trading coverage for cleaner data.
- **I3** (learn): Include such simple baselines among the negative controls, so that suspiciously high scores on a test set prompt a check for shortcut patterns.
- **I4** (frames): Model the speaker, the addressee and the target as roles in a speech-act frame, so that judgments about insults or irony depend on those participants rather than on surface vocabulary.

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

## Institutions

- [University of Minnesota](https://md.research.elamur.ai/free/en/universities/university-of-minnesota/) — University, Minneapolis, United States · 28 works · 2,021 citations
- [University of Minnesota, Duluth](https://md.research.elamur.ai/free/en/universities/university-of-minnesota-duluth/) — University, Duluth, United States · 5 works · 1,160 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🤝 Communication](https://md.research.elamur.ai/free/en/shelves/social-sciences/communication/) · 104 works

🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works

## Cite

Cite as: Ted Pedersen. 2020. Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression. Proceedings of the Fourteenth Workshop on Semantic Evaluation.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pedersen2020duluth,
  title = {{Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression}},
  author = {Ted Pedersen},
  booktitle = {Proceedings of the Fourteenth Workshop on Semantic Evaluation},
  year = {2020},
  doi = {10.18653/v1/2020.semeval-1.255},
  url = {https://doi.org/10.18653/v1/2020.semeval-1.255}
}
```

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## References

17 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*