Multimodal Deep Learning for Online Gambling Promotion Detection on Indonesian Social Media
Abstract View: 78,
DOI:
https://doi.org/10.32665/statkom.v5i1.6412Keywords:
Multimodal Classification, Online Gambling Detection, Text Embedding, Image Embedding, Indonesian Social MediaAbstract
Background: Online gambling promotion has become a major social problem in Indonesia, generating billions of rupiah in annual transactions despite strict legal prohibitions. Existing detection methods primarily focus on textual content while overlooking the visual information commonly used in social media promotions.
Objective: This study proposes a multimodal deep learning approach that combines image and text representations for detecting online gambling promotions on Indonesian social media and introduces the first publicly available multimodal dataset for this task.
Methods: A dataset of 5,028 labeled image-caption pairs was collected from Facebook, TikTok, and X, comprising 2,195 gambling promotion and 2,833 non-promotion samples. Three image embedding models, three text embedding models, and six multimodal fusion strategies were evaluated using a lightweight Multilayer Perceptron classifier.
Results: SigLIP 2 achieved the best image-only performance, while Multilingual E5-large achieved the best text-only performance. The contrastive similarity fusion strategy achieved the highest overall performance, with 97.51% accuracy and a 97.18% F1-score.
Conclusion: The proposed multimodal approach effectively detects online gambling promotions by leveraging complementary visual and textual information. The publicly available dataset also provides a benchmark to support future research.
References
Alfiansyah, M. I., & Muzakir, A. (2025). Enhanced detection of Indonesian online gambling advertisements using multimodal ensemble deep learning. International Journal of Artificial Intelligence and Science, 2(2), 159–175. https://doi.org/10.63158/ijais.v2i2.49
Arifat, M., Putri, W. A., & Mufida, A. S. (2023). Penerapan metode Naive Bayes Classifier untuk klasifikasi indeks Pembangunan manusia di Provinsi Jawa Timur. Jurnal Statistika Dan Komputasi, 2(1), 31–43. https://doi.org/10.32665/statkom.v2i1.1661
Artetxe, M., & Schwenk, H. (2019). Massively multilingual sentence embeddings for Zero-Shot Cross-Lingual transfer and beyond. Transactions of the Association for Computational Linguistics, 7, 597–610. https://doi.org/10.1162/tacl_a_00288
Bai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., Ge, W., Guo, Z., Huang, Q., Huang, J., Huang, F., Hui, B., Jiang, S., Li, Z., Li, M., . . . Zhu, K. (2025). QWEN3-VL Technical Report. ArXiv.org. https://doi.org/10.48550/arxiv.2511.21631
Cahyo, D. D. N., Handayani, R., Lestari, V. B., & Febriani, S. (2025). Sentiment Analysis of Public Opinion on Online Gambling Through Social Media Using Convolutional Neural Network. Journal of Information Technology and Engineering (JITE), 9(1), 99–115. https://doi.org/10.31289/jite.v9i1.15024
Chen, F., Zhang, D., Han, M., Chen, X., Shi, J., Xu, S., & Xu, B. (2023). VLP: A Survey on vision-Language Pre-training. Machine Intelligence Research, 20(1), 38–56. https://doi.org/10.1007/s11633-022-1369-5
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8440–8451. https://doi.org/10.18653/v1/2020.acl-main.747
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/N19-1423
Fan, H., Du, W., Dahou, A., Ewees, A. A., Yousri, D., Elaziz, M. A., Elsheikh, A. H., Abualigah, L., & Al-qaness, M. A. A. (2021). Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit. Electronics, 10(11), 1332. https://doi.org/10.3390/electronics10111332
Fisher, M. L., Piper, T., Fitzpatrick, M., Mavi, S., Retzer, A., Bradbury-Jones, C., Montgomery, P., Melendez-Torres, G. J., Kirby, J., Chandan, J. S., & Bedford, K. (2025). Legal and regulatory responses to online gambling harms: a scoping review of evidence. Harm Reduction Journal, 22(1), 163. https://doi.org/10.1186/s12954-025-01292-y
Ginasputri, H. N. A., Saputri, A. D., Wahid, S. N., Ningrum, A. F., & Haris, M. A. (2025). Analisis sentimen ulasan pengguna BRIMO terhadap pembaruan fitur aplikasi menggunakan Naive Bayes dengan seleksi fitur Chi-Square. Jurnal Statistika Dan Komputasi, 4(2), 56–67. https://doi.org/10.32665/statkom.v4i2.5194
Gupta, S., & Kishan, B. (2025). A performance-driven hybrid text-image classification model for multimodal data. Scientific Reports, 15(1), 11598. https://doi.org/10.1038/s41598-025-95674-8
Ilham, M. L. (2025). Indonesian Financial Watchdog Finds $58.9 Million in Online Gambling Transactions Linked to State Social Aid Recipients. https://www.jakartadaily.id/market-finance/16215502616/indonesian-financial-watchdog-finds-589-million-in-online-gambling-transactions-linked-to-state-social-aid-recipients
Imaduddin, H., A’la, F. Y., & Nugroho, Y. S. (2023). Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach. International Journal of Advanced Computer Science and Applications, 14(8). https://doi.org/10.14569/ijacsa.2023.0140813
Junaedi, J. (2024). England is the Largest Center for Online Gambling Activity in the World, Versus Indonesia is Exposed to Online Gambling Emergency Stage Five. International Journal of Law, Crime and Justice, 1(3), 100–114. https://doi.org/10.62951/ijlcj.v1i3.134
Kamdan, K., Anugrah, M. P., Almutaali, M. J., Ramdani, R., & Kharisma, I. L. (2025). Performance Analysis of IndoBERT for Detection of Online Gambling Promotion in YouTube Comments. Engineering Proceedings, 107(1), 66. https://doi.org/10.3390/engproc2025107066
Kongah, M. N. (2025). Promensisko 2025: Responding to the Threat of Online Gambling and Digital Crime Through Action. https://www.ppatk.go.id/siaran_pers/read/1474/promensisko-2025-menjawab-ancaman-judi-online-dan-kejahatan-digital-lewat-aksi-.html
Liem, J. A., Utomo, D. T. D., Ignasio, B., & Cenggoro, T. W. (2025). Deep Learning And Machine Learning For Indonesian Online Gambling Website Promotion Detection. Procedia Computer Science, 269, 979–992. https://doi.org/10.1016/j.procs.2025.09.040
Lin, S., Chen, Y., Chang, Y., Lo, S., & Chao, K. (2024). Text–image multimodal fusion model for enhanced fake news detection. Science Progress, 107(4), 368504241292685. https://doi.org/10.1177/00368504241292685
Lu, Y., & Yao, N. (2025). A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network. Scientific Reports, 15(1), 20544. https://doi.org/10.1038/s41598-025-05702-w
Maldini, A. S., Saputra, W. S. J., & Prasetya, D. A. (2025). Multimodal Detection of Covert Online Gambling Advertisements Using Faster R-CNN and Tr-OCR. Bit-Tech, 8(1), 953–963. https://doi.org/10.32877/bt.v8i1.2769
Manullang, M. C. T., Rakhman, A. Z., Tantriawan, H., & Setiawan, A. (2025). Comparative analysis of CNN, Transformers, and Traditional ML for classifying online gambling spam comments in Indonesian. Journal of Applied Informatics and Computing, 9(3), 592–602. https://doi.org/10.30871/jaic.v9i3.9468
Muggleton, N., Parpart, P., Newall, P., Leake, D., Gathergood, J., & Stewart, N. (2021). The association between gambling and financial, social and health outcomes in big financial data. Nature Human Behaviour, 5(3), 319–326. https://doi.org/10.1038/s41562-020-01045-w
Muzakir, A., Ependi, U., & Suyanto. (2025). A deep learning and AutoML-Based multimodal text extraction framework for detecting online gambling advertisements in Indonesian social media. International Journal of Safety and Security Engineering, 15(9). https://doi.org/10.18280/ijsse.150908
Perdana, R. B., Ardin, Budi, I., Santoso, A. B., Ramadiah, A., & Putra, P. K. (2024). Detecting online gambling promotions on Indonesian Twitter using text mining algorithm. International Journal of Advanced Computer Science and Applications, 15(8). https://doi.org/10.14569/ijacsa.2024.0150893
Prabhu, R., & Seethalakshmi, V. (2025). A comprehensive framework for multi-modal hate speech detection in social media using deep learning. Scientific Reports, 15(1), 13020. https://doi.org/10.1038/s41598-025-94069-z
Pratama, H. M., Wijayakusuma, I. L., & Widiastuti, R. S. (2025). Comparison of online gambling promotion detection performance using DistilBERT and DeBERTA models. Journal of Applied Informatics and Computing, 9(6), 3716–3725. https://doi.org/10.30871/jaic.v9i6.11293
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models from Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), Proceedings of Machine Learning Research, 139, 8748–8763. https://proceedings.mlr.press/v139/radford21a.html
Rahman, M. R. (2025). Indonesia pressed to intensify fight against online gambling networks. https://en.antaranews.com/news/390781/indonesia-pressed-to-intensify-fight-against-online-gambling-networks
Santika, E. F. (2025). Gambling-related Divorces in Indonesia Soar 83% in 2024. https://databoks.katadata.co.id/en/demographics/statistics/689ad79cdce8e/gambling-related-divorces-in-indonesia-soar-83-in-2024
Siméoni, O., Vo, H., V., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C., Khalidov, V., Szafraniec, M., Yi, S., Ramamonjisoa, M., Massa, F., Haziza, D., Wehrstedt, L., Wang, J., Darcet, T., Moutakanni, T., Sentana, L., Roberts, C., Vedaldi, A., . . . Bojanowski, P. (2025). DINOv3. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2508.10104
Situmorang, G. F., Manurung, J. D., Lubis, R., Sikana, N., & Lionggo, K. (2026). An intelligent system for detecting online gambling promotion on social media based on BERT and adaptive browser extension. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 14(3). https://doi.org/10.23887/janapati.v14i3.103511
Stankevičius, L., & Lukoševičius, M. (2024). Extracting Sentence Embeddings from Pretrained Transformer Models. Applied Sciences, 14(19), 8887. https://doi.org/10.3390/app14198887
Sutriawan, Rustad, S., Shidik, G. F., & Pujiono. (2025). Performance Evaluation of text embedding models for ambiguity classification in Indonesian news Corpus: A comparative study of TF-IDF, Word2VEC, FastText BERT, and GPT. Ingénierie Des Systèmes D Information, 30(6), 1469–1482. https://doi.org/10.18280/isi.300606
Tschannen, M., Gritsenko, A., Wang, X., Naeem, M. F., Alabdulmohsin, I., Parthasarathy, N., Evans, T., Beyer, L., Xia, Y., Mustafa, B., Hénaff, O., Harmsen, J., Steiner, A., & Zhai, X. (2025). SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features. ArXiv.org. https://doi.org/10.48550/arxiv.2502.14786
Uelwer, T., Robine, J., Wagner, S. S., Höftmann, M., Upschulte, E., Konietzny, S., Behrendt, M., & Harmeling, S. (2025). A survey on self-supervised methods for visual representation learning. Machine Learning, 114(4). https://doi.org/10.1007/s10994-024-06708-7
Ukhova, D., Marionneau, V., Nikkinen, J., & Wardle, H. (2023). Public health approaches to gambling: a global review of legislative trends. The Lancet Public Health, 9(1), e57–e67. https://doi.org/10.1016/s2468-2667(23)00221-9
Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., & Wei, F. (2024). Multilingual E5 Text Embeddings: a Technical report. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2402.05672
Widiyanto, A., Prameswari, M., & Latief, M. A. (2025). Gambling comments detection on YouTube: A comparative study of Tree-Based Boosting, LSTM and GRU models. JUTI Jurnal Ilmiah Teknologi Informasi, 144–160. https://doi.org/10.12962/j24068535.v23i2.a1305
Wirawan, C. (2022). Indonesian BERT base model (uncased) – 1.5G. https://huggingface.co/cahya/bert-base-indonesian-1.5G
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., & Chen, E. (2024). A survey on multimodal large language models. National Science Review, 11(12), nwae403. https://doi.org/10.1093/nsr/nwae403
Zhang, X., Li, G., Tu, S., Yi, J., & Zhang, Y. (2025). A gambling website identification framework based on fusion-assisted multimodal large language model. Journal of Computational Methods in Sciences and Engineering. https://doi.org/10.1177/14727978251364464
Zhang, Y., Li, M., Long, D., Zhang, X., Lin, H., Yang, B., Xie, P., Yang, A., Liu, D., Lin, J., Huang, F., &
Zhou, J. (2025). QWeN3 Embedding: advancing text embedding and reranking through foundation models. ArXiv.org. https://doi.org/10.48550/arxiv.2506.05176
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhamad Syukron, Danish Rafie Ekaputra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows:
Abstract View: 78,
PDF Download: 57
SIMILARITY INDEX Download: 0
