# Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation

arXiv: https://arxiv.org/abs/2103.05345 · HTML: https://mlsanity.com/paper/2103.05345/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [Reducing Gender Bias in Abusive Language Detection](https://mlsanity.com/paper/1808.07231/) (2018)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Demoting Racial Bias in Hate Speech Detection](https://mlsanity.com/paper/2005.12246/) (2020)
- [Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting](https://mlsanity.com/paper/2004.14088/) (2020)
- [Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features](https://mlsanity.com/paper/1904.07372/) (2019)

## Built on by (most cited first, 6 of 6)

- [Text Detoxification using Large Pre-trained Neural Models](https://mlsanity.com/paper/2109.08914/) (2021)
- [A Family of Pretrained Transformer Language Models for Russian](https://mlsanity.com/paper/2309.10931/) (2023)
- [The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design](https://mlsanity.com/paper/2408.12503/) (2024)
- [RuBia: A Russian Language Bias Detection Dataset](https://mlsanity.com/paper/2403.17553/) (2024)
- [Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language](https://mlsanity.com/paper/2203.02392/) (2022)
- [Which Values Do LLMs Confuse? A Schwartz-Based Recognition Study](https://mlsanity.com/paper/2607.20270/) (2026)

## Abstract (arXiv)

Not all topics are equally "flammable" in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labeling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labeled dataset and an appropriateness-labeled dataset. We also release pre-trained classification models trained on this data.