Document Type

Article

Publication Date

5-25-2026

Publication Title

SpectrumNet: Detecting LGBTQ+ Cyberbullying with Dynamic Context-Aware Attention

Volume

20

Issue

1

Pages

276–290

Publisher Name

AAAI Press

Publisher Location

Washington, D.C., USA

Abstract

Cyberbullying remains a critical societal issue, with LGBTQ+ individuals disproportionately affected. Although previous work proposed general cyberbullying detection models, LGBTQ+-targeted cyberbullying detection remains relatively unexplored. SpectrumNet, a novel transformer-based model introduced in this paper, goes beyond conventional cyberbullying detection by adding conversational context and identity-aware modeling. SpectrumNet freezes the RoBERTa backbone and adds three key components: a hierarchical attention network to capture linguistic nuance, a GRU-based encoder to better capture comment history, and a dynamic fusion module to effectively weigh contextual signals. To address dataset imbalance, we apply focal loss and weighted sampling. Trained on a large, annotated Instagram dataset, SpectrumNet effectively differentiates between non-bullying, general bullying, and LGBTQ+-targeted bullying. In particular, it achieves strong recall on targeted content and excels at detecting subtle forms of discrimination often missed in isolation but evident within threaded interactions.

Identifier

10.1609/icwsm.v20i1.42638

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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