“My life is miserable, have to sign 500 autographs everyday”: Exposing Humblebragging, the Brags in Disguise (2025)

Abstract: 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.

## introduction humblebragging is a nuanced socio-linguistic phenomenon in which individuals subtly boast about their achievements, possessions, or qualities while disguising their self-promotion with expressions of complaint or modesty. the term was coined by american comedian harris wittels in 2010, who later published a book on the phenomenon titled humblebrag: the art of false modesty @xcite . a few examples of humblebrags are provided in humblebragging is closely related to other forms of figurative language, such as sarcasm @xcite and irony @xcite , which also rely on verbal incongruity, a contrast between what is said and what is meant. while irony typically contrasts expectation with reality and sarcasm adds a mocking tone, humblebragging uniquely conceals self-promotion within a modest or complaining remark. a detailed comparison of these differences is provided in appendix b. although extensive research exists on computational modeling of sarcasm @xcite @xcite and irony @xcite @xcite , to the best of our knowledge, this work is the first to explore humblebragging in computational linguistics. detecting humblebragging automatically is crucial for a nuanced understanding and processing of human language. this is necessary for enhancing accuracy of tasks such as sentiment analysis @xcite , intent recognition @xcite , emotion recognition @xcite and dialogue understanding @xcite . in applications like social media monitoring and customer feedback analysis, it helps differentiate genuine complaints @xcite from brags disguised as complaints. moreover, this capability is also valuable for researchers in the humanities and social sciences. in this paper, we formally introduce the task of humblebragging detection and present a curated dataset that combines existing resources with synthetic data generated using gpt-4o. by addressing this previously unexplored area, our work bridges the gap between computational linguistics and other disciplines in the study of humblebragging. our contributions are: in psychology and other disciplines humblebragging has been extensively studied in psychology. sezer et al. (2018) examined its effects on audiences, showing that humblebragging is ubiquitous in daily interactions, with 70% of humblebrags falling into the complaint-masked variety. trivedi and srinivas (2019) explained the widespread use of humblebragging through the dual perspectives of need theories and the hubris hypothesis, and also provided a contextual framework for understanding it. other notable studies include sezer et al. (2015); vranka et al. (2017); luo and hancock @xcite . beyond psychology, humblebragging has been explored in disciplines like tourism research @xcite , pragmatics @xcite @xcite , and advertising @xcite . ## formulation of the humblebragging definition in this section, we define and derive our proposed framework for humblebragging. ## dataset as there were no existing datasets for the task of humblebragging detection, we propose hb-24 foot_0 , a well-balanced collection of humblebrag and nonhumblebrag texts, comprising both human-written and synthetic samples. due to the limited availability of quality humblebrags, we leverage the capabilities of large language models to generate human-like examples, augmenting the existing data and enhancing the corpus for training classification models. the synthetic data is used for training, while the trained model's performance is evaluated on human-written samples. in other words, the training set is composed of synthetic humblebrags while the test set consists of human-written humblebrags. the non-humblebrags are all human-written. ## methodology we define two task setups: a standard binary classification task, and a sentence completion formulation where humblebrag detection is cast as a yes/no question answering problem. this enables effective use of decoder-only language models for classification through natural language prompts. ## experimental setup we conducted experiments with machine learning classifiers, encoder models, decoder models and compared the performance with those of human annotators. ## results and discussion we present the results of all our experiments in ## conclusion and future work we introduced the task of automatic humblebragging detection, formalized through our proposed 4tuple definition. we benchmarked various machine learning, deep learning, and large language models on this task, providing a comparative analysis against human performance. we also demonstrate that our 4-tuple definition significantly improves the zero-shot capabilities of all decoder models. additionally, we released a synthetic dataset, hb-24, generated using gpt-4o, to facilitate further research. our experiments and analysis reveal that detecting humblebragging is a challenging task, even for humans. this study lays the groundwork for exploring this intricate linguistic phenomenon and its integration into natural language understanding systems. future research could aim to enhance models for identifying humblebragging, fostering a deeper comprehension of this distinct communication style. this may include methods for utilizing contextual cues more effectively. another valuable direction could involve generating humblebrag captions for images. additionally, machines could be trained to transform direct brags into humblebrags.