# ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter

arXiv: https://arxiv.org/abs/2008.06465 · HTML: https://mlsanity.com/paper/2008.06465/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 17 of 17)

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- [Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language](https://mlsanity.com/paper/2503.01539/) (2025)
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- [DiffuDetox: A Mixed Diffusion Model for Text Detoxification](https://mlsanity.com/paper/2306.08505/) (2023)
- [Hateful Messages: A Conversational Data Set of Hate Speech produced by Adolescents on Discord](https://mlsanity.com/paper/2309.01413/) (2023)
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- [Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models](https://mlsanity.com/paper/2501.09039/) (2025)
- [Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi](https://mlsanity.com/paper/2101.07973/) (2021)
- [On the Challenges of Building Datasets for Hate Speech Detection](https://mlsanity.com/paper/2309.02912/) (2023)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)
- [WarCov -- Large multilabel and multimodal dataset from social platform](https://mlsanity.com/paper/2406.10255/) (2024)
- [Characterizing Online Toxicity During the 2022 Mpox Outbreak: A Computational Analysis of Topical and Network Dynamics](https://mlsanity.com/paper/2408.11962/) (2024)
- [Web(er) of Hate: A Survey on How Hate Speech Is Typed](https://mlsanity.com/paper/2506.16190/) (2025)
- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)

## Abstract (arXiv)

The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive explanation. Each instance of interaction includes tweets, images, emoji and related metadata. Our observations show that individual tweets do not provide sufficient evidence for toxic behavior, and meaningful use of context in interactions can enable highlighting or exonerating tweets with purported toxicity.