# "My life is miserable, have to sign 500 autographs everyday": Exposing Humblebragging, the Brags in Disguise

arXiv: https://arxiv.org/abs/2412.20057 · HTML: https://mlsanity.com/paper/2412.20057/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 77 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Builds on (most cited first, 27 of 27)

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- [On Sarcasm Detection with OpenAI GPT-based Models](https://mlsanity.com/paper/2312.04642/) (2023)
- [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://mlsanity.com/paper/2209.04712/) (2022)
- [Template-based Approach to Zero-shot Intent Recognition](https://mlsanity.com/paper/2206.10914/) (2022)
- [ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler](https://mlsanity.com/paper/2403.17536/) (2024)
- [The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse](https://mlsanity.com/paper/2410.15182/) (2024)
- [Automatic Identification of Sarcasm Target: An Introductory Approach](https://mlsanity.com/paper/1610.07091/) (2016)
- [Who is bragging more online? A large scale analysis of bragging in social media](https://mlsanity.com/paper/2403.16668/) (2024)

## Abstract (arXiv)

Humblebragging is a phenomenon in which individuals present self-promotional statements under the guise of modesty or complaints. For example, a statement like, "Ugh, I can't believe I got promoted to lead the entire team. So stressful!", subtly highlights an achievement while pretending to be complaining. Detecting humblebragging is important for machines to better understand the nuances of human language, especially in tasks like sentiment analysis and intent recognition. However, this topic has not yet been studied in computational linguistics. For the first time, we introduce the task of automatically detecting humblebragging in text. We formalize the task by proposing a 4-tuple definition of humblebragging and evaluate machine learning, deep learning, and large language models (LLMs) on this task, comparing their performance with humans. We also create and release a dataset called HB-24, containing 3,340 humblebrags generated using GPT-4o. Our experiments show that detecting humblebragging is non-trivial, even for humans. Our best model achieves an F1-score of 0.88. This work lays the foundation for further exploration of this nuanced linguistic phenomenon and its integration into broader natural language understanding systems.