# Finding Trolls Under Bridges: Preliminary Work on a Motif Detector

arXiv: https://arxiv.org/abs/2204.06085 · HTML: https://mlsanity.com/paper/2204.06085/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Towards an Atlas of Cultural Commonsense for Machine Reasoning](https://mlsanity.com/paper/2009.05664/) (2020)

## Built on by (most cited first, 2 of 2)

- [Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry](https://mlsanity.com/paper/2411.03542/) (2024)
- [Automated Motif Indexing on the Arabian Nights](https://mlsanity.com/paper/2603.19283/) (2026)

## Abstract (arXiv)

Motifs are distinctive recurring elements found in folklore that have significance as communicative devices in news, literature, press releases, and propaganda. Motifs concisely imply a large constellation of culturally-relevant information, and their broad usage suggests their cognitive importance as touchstones of cultural knowledge, making their detection a worthy step toward culturally-aware natural language processing tasks. Until now, folklorists and others interested in motifs have only extracted motifs from narratives manually. We present a preliminary report on the development of a system for automatically detecting motifs. We briefly describe an annotation effort to produce data for training motif detection, which is on-going. We describe our in-progress architecture in detail, which aims to capture, in part, how people determine whether or not a motif candidate is being used in a motific way. This description includes a test of an off-the-shelf metaphor detector as a feature for motif detection, which achieves a F1 of 0.35 on motifs and a macro-average F1 of 0.21 across four categories which we assign to motif candidates.