---
title: "Multimodal Hate and Sentiment Understanding in Low-Resource"
description: "This overview paper reports on a CHiPSAL 2026 shared task, held at LREC 2026, on moderating… 34 references, cited by 12 papers here."
authors: ["Surendrabikram Thapa", "Shuvam Shiwakoti", "Siddhant Bikram Shah", "Kritesh Rauniyar", "Laxmi Thapa", "Surabhi Adhikari", "Kristina T Johnson", "Kengatharaiyer Sarveswaran", "Bal Krishna Bal", "Usman Naseem"]
year: 2026
venue: "Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026)"
source: "https://aclanthology.org/2026.chipsal-1.21/"
doi: "10.63317/3rhxrpvoi74j"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/thapa-2026-multimodal-hate-sentiment-understanding-low/"
---
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# Multimodal Hate and Sentiment Understanding in Low-Resource Text-Embedded Images for Online Safety and Digital Well-being

**Authors:** [Surendrabikram Thapa](https://md.research.elamur.ai/free/en/authors/surendrabikram-thapa/), [Shuvam Shiwakoti](https://md.research.elamur.ai/free/en/authors/shuvam-shiwakoti/), [Siddhant Bikram Shah](https://md.research.elamur.ai/free/en/authors/siddhant-bikram-shah/), [Kritesh Rauniyar](https://md.research.elamur.ai/free/en/authors/kritesh-rauniyar/), [Laxmi Thapa](https://md.research.elamur.ai/free/en/authors/laxmi-thapa/), [Surabhi Adhikari](https://md.research.elamur.ai/free/en/authors/surabhi-adhikari/), [Kristina T Johnson](https://md.research.elamur.ai/free/en/authors/kristina-t-johnson/), [Kengatharaiyer Sarveswaran](https://md.research.elamur.ai/free/en/authors/kengatharaiyer-sarveswaran/), [Bal Krishna Bal](https://md.research.elamur.ai/free/en/authors/bal-krishna-bal/) and [Usman Naseem](https://md.research.elamur.ai/free/en/authors/usman-naseem/) · **Year:** 2026 · **Venue:** [Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026)](https://md.research.elamur.ai/free/en/venues/chipsal/) ([workshop on Challenges in Processing South Asian Languages 2026](https://md.research.elamur.ai/free/en/venues/chipsal/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.chipsal-1.21/) · [PDF](https://aclanthology.org/2026.chipsal-1.21.pdf) · [DOI 10.63317/3rhxrpvoi74j](https://doi.org/10.63317/3rhxrpvoi74j) · [OpenAlex](https://openalex.org/W7166506653)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [12](https://md.research.elamur.ai/free/en/papers/thapa-2026-multimodal-hate-sentiment-understanding-low/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This overview paper reports on a CHiPSAL 2026 shared task, held at LREC 2026, on moderating memes in Nepali, a widely spoken language with few resources. The organizers used the monolingual Devanagari portion of the NeMeme dataset and excluded code-mixed Nepali–English memes. They set two classification problems: deciding whether a meme is hateful, and labelling its sentiment as negative, neutral or positive. Both were scored with macro-F1 because the labels are unevenly distributed.

Participation reached 23 teams for the hate track and 13 for sentiment. Submissions ranged from single-modality baselines to:
- fusion of multilingual text encoders with image models,
- Gemini-generated captions that turn the problem into text classification,
- ensembles.

The winning entry was a late-fusion hybrid trained with discriminative learning rates. It scored 80.52% on hate and 68.81% on sentiment. The organizers conclude that combining modalities usually helps and that three-way sentiment is clearly harder. They also identify the shortage of Devanagari-aware pretrained models as a key obstacle. The task provides a reference benchmark for multimodal safety research on South Asian languages.

## Key points
- The data is the monolingual Nepali subset of NeMeme, collected from Twitter, Instagram, Reddit, Facebook and Threads.
- Three annotators labelled each meme, using a pilot–revision–consolidation scheme. Agreement (Fleiss' κ) was 0.74 for hate and 0.69 for sentiment.
- Hate splits:
  - train: 1,068 memes (348 hate / 720 non-hate)
  - validation: 133 memes (35 hate)
  - test: 134 memes (44 hate)
- The best systems paired text encoders such as NepBERTa, XLM-RoBERTa or BGE-M3 with CLIP ViT through late fusion. This beat early fusion and joint models like ViLT.
- The top sentiment score trails the top hate score by about 12 points. Vision-only models were unexpectedly strong, while text-only ones lagged behind.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Surendrabikram Thapa, Shuvam Shiwakoti, Siddhant Bikram Shah, Kritesh Rauniyar, Laxmi Thapa, Surabhi Adhikari, Kristina T Johnson, Kengatharaiyer Sarveswaran, Bal Krishna Bal and Usman Naseem. 2026. Multimodal Hate and Sentiment Understanding in Low-Resource Text-Embedded Images for Online Safety and Digital Well-being. Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{thapa2026multimodal,
  title = {{Multimodal Hate and Sentiment Understanding in Low-Resource Text-Embedded Images for Online Safety and Digital Well-being}},
  author = {Surendrabikram Thapa, Shuvam Shiwakoti, Siddhant Bikram Shah, Kritesh Rauniyar, Laxmi Thapa, Surabhi Adhikari, Kristina T Johnson, Kengatharaiyer Sarveswaran, Bal Krishna Bal and Usman Naseem},
  booktitle = {Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026)},
  year = {2026},
  doi = {10.63317/3rhxrpvoi74j},
  url = {https://doi.org/10.63317/3rhxrpvoi74j}
}
```

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## References

34 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ashish Acharya, Anish Khatiwada, Rohit Khadka, and Pragya Aryal. 2026. Teamherald@chipsal 2026: Hate speech detection and sentiment analysis of nepali memes using transformer-based architectures and ensemble learning. In Proceedings of the Second Workshop on Challenges in Processing South Asian Lang… [find](https://scholar.google.com/scholar?q=Ashish+Acharya%2C+Anish+Khatiwada%2C+Rohit+Khadka%2C+and+Pragya+Aryal%2E+2026%2E+Teamherald%40chipsal+2026%3A+Hate+speech+detection+and+sentiment+analysis+of+nepali+memes+using+transformer%2Dbased+architectures+and+e)
2. Vinayak Bansal, Deepawali Sharma, Aakash Singh, and Vivek Kumar Singh. 2026. Ethosai@chipsal2026: Hate and sentiment understanding in low-resource memes using a multimodal approach. In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL). [find](https://scholar.google.com/scholar?q=Vinayak+Bansal%2C+Deepawali+Sharma%2C+Aakash+Singh%2C+and+Vivek+Kumar+Singh%2E+2026%2E+Ethosai%40chipsal2026%3A+Hate+and+sentiment+understanding+in+low%2Dresource+memes+using+a+multimodal+approach%2E+In+Proceedings+of+)
3. Aashish Bhandari, Siddhant B Shah, Surendrabikram Thapa, Usman Naseem, and Mehwish Nasim. 2023. Crisishatemm: Multimodal analysis of directed and undirected hate speech in text-embedded images from russia-ukraine conflict. In Proceedings of the IEEE/CVF conference on computer vision and pattern reco… [find](https://scholar.google.com/scholar?q=Aashish+Bhandari%2C+Siddhant+B+Shah%2C+Surendrabikram+Thapa%2C+Usman+Naseem%2C+and+Mehwish+Nasim%2E+2023%2E+Crisishatemm%3A+Multimodal+analysis+of+directed+and+undirected+hate+speech+in+text%2Dembedded+images+from+ru)
4. Anusha Chhabra and Dinesh Kumar Vishwakarma. 2023. A literature survey on multimodal and multilingual automatic hate speech identification. Multimedia Systems, 29(3):1203–1230. [find](https://scholar.google.com/scholar?q=Anusha+Chhabra+and+Dinesh+Kumar+Vishwakarma%2E+2023%2E+A+literature+survey+on+multimodal+and+multilingual+automatic+hate+speech+identification%2E+Multimedia+Systems%2C+29%283%29%3A1203%E2%80%931230%2E)
5. Alvee Hasan Chowdhury, MD. Abul Hasnat, and Adnan Faisal. 2026. Hasnat@chipsal 2026: Multimodal hate speech detection in low-resource nepali memes using aligned vision–language models. In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL). [find](https://scholar.google.com/scholar?q=Alvee+Hasan+Chowdhury%2C+MD%2E+Abul+Hasnat%2C+and+Adnan+Faisal%2E+2026%2E+Hasnat%40chipsal+2026%3A+Multimodal+hate+speech+detection+in+low%2Dresource+nepali+memes+using+aligned+vision%E2%80%93language+models%2E+In+Proceedings+)
6. Rotna Dipika Debnath, Shahrin Afroz Hoque Ruhi, Ayesha Labiba, Arpita Mallik, and Hasan Murad. 2026. Cuet yet another baseline@chipsal lrec 2026: Shared task on multimodal sentiment understanding in low-resource memes. In Proceedings of the Second Workshop on Challenges in Processing South Asian Lan… [find](https://scholar.google.com/scholar?q=Rotna+Dipika+Debnath%2C+Shahrin+Afroz+Hoque+Ruhi%2C+Ayesha+Labiba%2C+Arpita+Mallik%2C+and+Hasan+Murad%2E+2026%2E+Cuet+yet+another+baseline%40chipsal+lrec+2026%3A+Shared+task+on+multimodal+sentiment+understanding+in+l)
7. Kriti Dubey, Vaishnavi Srivastava, Garima Sharma, Nonita Sharma, Deepak Sharma, Uttam Ghosh, Osama Alfarraj, and Amr Tolba. 2025. Multimodal detection of offensive content in hindi memes. ACM Transactions on Asian and Low-Resource Language Information Processing. [find](https://scholar.google.com/scholar?q=Kriti+Dubey%2C+Vaishnavi+Srivastava%2C+Garima+Sharma%2C+Nonita+Sharma%2C+Deepak+Sharma%2C+Uttam+Ghosh%2C+Osama+Alfarraj%2C+and+Amr+Tolba%2E+2025%2E+Multimodal+detection+of+offensive+content+in+hindi+memes%2E+ACM+Transact)
8. Ashweta Fondekar, Milind Shivolkar, and Jyoti Pawar. 2026. Unigoa@chipsal 2026: Early vs late fusion for multimodal hate and sentiment detection in nepali memes. In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL). [find](https://scholar.google.com/scholar?q=Ashweta+Fondekar%2C+Milind+Shivolkar%2C+and+Jyoti+Pawar%2E+2026%2E+Unigoa%40chipsal+2026%3A+Early+vs+late+fusion+for+multimodal+hate+and+sentiment+detection+in+nepali+memes%2E+In+Proceedings+of+the+Second+Workshop+)
9. Eftekhar Hossain, Omar Sharif, and Mohammed Moshiul Hoque. 2022. Mute: A multimodal dataset for detecting hateful memes. In Proceedings of the 2nd conference of the asia-pacific chapter of the association for computational linguistics and the 12th international joint conference on natural language p… [find](https://scholar.google.com/scholar?q=Eftekhar+Hossain%2C+Omar+Sharif%2C+and+Mohammed+Moshiul+Hoque%2E+2022%2E+Mute%3A+A+multimodal+dataset+for+detecting+hateful+memes%2E+In+Proceedings+of+the+2nd+conference+of+the+asia%2Dpacific+chapter+of+the+associa)
10. Md Saroar Jahan and Mourad Oussalah. 2023. A systematic review of hate speech automatic detection using natural language processing. Neurocomputing, page 126232. [find](https://scholar.google.com/scholar?q=Md+Saroar+Jahan+and+Mourad+Oussalah%2E+2023%2E+A+systematic+review+of+hate+speech+automatic+detection+using+natural+language+processing%2E+Neurocomputing%2C+page+126232%2E)
11. Junhui Ji, Wei Ren, and Usman Naseem. 2023. Identifying creative harmful memes via prompt based approach. In Proceedings of the ACM web conference 2023, pages 3868–3872. [find](https://scholar.google.com/scholar?q=Junhui+Ji%2C+Wei+Ren%2C+and+Usman+Naseem%2E+2023%2E+Identifying+creative+harmful+memes+via+prompt+based+approach%2E+In+Proceedings+of+the+ACM+web+conference+2023%2C+pages+3868%E2%80%933872%2E)
12. Nitiz Khanal. 2026. Zeror@chipsal 2026: Two-stage vision-language adaptation with contrastive learning for nepali meme classification. In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL). [find](https://scholar.google.com/scholar?q=Nitiz+Khanal%2E+2026%2E+Zeror%40chipsal+2026%3A+Two%2Dstage+vision%2Dlanguage+adaptation+with+contrastive+learning+for+nepali+meme+classification%2E+In+Proceedings+of+the+Second+Workshop+on+Challenges+in+Processing)
13. Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. 2020. [The hateful memes challenge: Detecting hate speech in multimodal memes](https://md.research.elamur.ai/free/en/papers/kiela-2020-hateful-memes-challenge-detecting-hate/). Advances in neural information processing systems, 33:2611–2624.
14. Gitanjali Kumari, Chandranath Adak, and Asif Ekbal. 2024. Mu2sts: a mu ltitask mu ltimodal s arcasm-humor-differential t eacher-s tudent model for sarcastic meme detection. In European Conference on Information Retrieval, pages 19–37. Springer. [find](https://scholar.google.com/scholar?q=Gitanjali+Kumari%2C+Chandranath+Adak%2C+and+Asif+Ekbal%2E+2024%2E+Mu2sts%3A+a+mu+ltitask+mu+ltimodal+s+arcasm%2Dhumor%2Ddifferential+t+eacher%2Ds+tudent+model+for+sarcastic+meme+detection%2E+In+European+Conference+on+I)
15. Sidratul Muntaha, Sabila Anzum, Arpita Mallik, and Hasan Murad. 2026. [Neuralnoodles@chipsal 2026: Late-fusion multimodal stacking for nepali meme sentiment classification](https://md.research.elamur.ai/free/en/papers/muntaha-2026-neuralnoodles-chipsal-2026-late-fusion/). In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL).
16. Noore Tamanna Orny, Joyeta Barua Moni, and Md. Abtahee Kabir. 2026. [Team oryu@chipsal 2026: Integrating text and vision transformers for multimodal hate speech detection in memes](https://md.research.elamur.ai/free/en/papers/orny-2026-team-oryu-chipsal-2026-integrating/). In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL).
17. Anil Singh Parihar, Surendrabikram Thapa, and Sushruti Mishra. 2021. Hate speech detection using natural language processing: Applications and challenges. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pages 1302–1308. IEEE. [find](https://scholar.google.com/scholar?q=Anil+Singh+Parihar%2C+Surendrabikram+Thapa%2C+and+Sushruti+Mishra%2E+2021%2E+Hate+speech+detection+using+natural+language+processing%3A+Applications+and+challenges%2E+In+2021+5th+International+Conference+on+Trend)
18. Sweta Poudel, Kritesh Rauniyar, Ashish Acharya, Junaid Rashid, Surabhi Adhikari, Usman Naseem, and Surendrabikram Thapa. 2025. Nepaes: exploring the promise of automated essay scoring for nepali essays. PeerJ Computer Science, 11:e3253. [find](https://scholar.google.com/scholar?q=Sweta+Poudel%2C+Kritesh+Rauniyar%2C+Ashish+Acharya%2C+Junaid+Rashid%2C+Surabhi+Adhikari%2C+Usman+Naseem%2C+and+Surendrabikram+Thapa%2E+2025%2E+Nepaes%3A+exploring+the+promise+of+automated+essay+scoring+for+nepali+essay)
19. Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2021. [Momenta: A multimodal framework for detecting harmful memes and their targets](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/). In Findings of the association for computational linguistics: EMNLP 2021, pages 4439– 4455.
20. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. [Learning transferable visual models from natural language supervision](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/). In International conference on machine learning, pages 8748–8763. Pm…
21. Pooja Rai, Sanjay Chatterji, and Byung-Gyu Kim. 2023. Deep learning-based sequence labeling tools for nepali. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(8):1–23. [find](https://scholar.google.com/scholar?q=Pooja+Rai%2C+Sanjay+Chatterji%2C+and+Byung%2DGyu+Kim%2E+2023%2E+Deep+learning%2Dbased+sequence+labeling+tools+for+nepali%2E+ACM+Transactions+on+Asian+and+Low%2DResource+Language+Information+Processing%2C+22%288%29%3A1%E2%80%9323%2E)
22. Sunil Regmi, Bipesh Subedi, Saugat Singh, and Suman Shrestha. 2026. [linus@chipsal 2026: Multimodal hate speech and sentiment detection in low-resource memes using late-fusion hybrid architecture](https://md.research.elamur.ai/free/en/papers/regmi-2026-linus-chipsal-2026-multimodal-hate/). In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL).
23. Kengatharaiyer Sarveswaran, Surendrabikram Thapa, Ashwini Vaidya, Tafseer Ahmed Khan, and Bal Krishna Bal. 2026. [Findings of the second workshop on challenges in processing south asian languages (chipsal 2026)](https://md.research.elamur.ai/free/en/papers/sarveswaran-2026-findings-second-workshop-challenges-processing/). In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (…
24. Siddhant Bikram Shah, Shuvam Shiwakoti, Maheep Chaudhary, and Haohan Wang. 2024. Memeclip: Leveraging clip representations for multimodal meme classification. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17320– 17332. [find](https://scholar.google.com/scholar?q=Siddhant+Bikram+Shah%2C+Shuvam+Shiwakoti%2C+Maheep+Chaudhary%2C+and+Haohan+Wang%2E+2024%2E+Memeclip%3A+Leveraging+clip+representations+for+multimodal+meme+classification%2E+In+Proceedings+of+the+2024+Conference+on+)
25. Lanyu Shang, Yang Zhang, Yuheng Zha, Yingxi Chen, Christina Youn, and Dong Wang. 2021. Aomd: An analogy-aware approach to offensive meme detection on social media. Information Processing & Management, 58(5):102664. [find](https://scholar.google.com/scholar?q=Lanyu+Shang%2C+Yang+Zhang%2C+Yuheng+Zha%2C+Yingxi+Chen%2C+Christina+Youn%2C+and+Dong+Wang%2E+2021%2E+Aomd%3A+An+analogy%2Daware+approach+to+offensive+meme+detection+on+social+media%2E+Information+Processing+%26+Management%2C)
26. Limor Shifman. 2013. Memes in a digital world: Reconciling with a conceptual troublemaker. Journal of computer-mediated communication, 18(3):362–377. [find](https://scholar.google.com/scholar?q=Limor+Shifman%2E+2013%2E+Memes+in+a+digital+world%3A+Reconciling+with+a+conceptual+troublemaker%2E+Journal+of+computer%2Dmediated+communication%2C+18%283%29%3A362%E2%80%93377%2E)
27. Shuvam Shiwakoti, Surendrabikram Thapa, Kritesh Rauniyar, Akshyat Shah, Aashish Bhandari, and Usman Naseem. 2024. Analyzing the dynamics of climate change discourse on twitter: A new annotated corpus and multi-aspect classification. In Proceedings of the 2024 joint international conference on comput… [find](https://scholar.google.com/scholar?q=Shuvam+Shiwakoti%2C+Surendrabikram+Thapa%2C+Kritesh+Rauniyar%2C+Akshyat+Shah%2C+Aashish+Bhandari%2C+and+Usman+Naseem%2E+2024%2E+Analyzing+the+dynamics+of+climate+change+discourse+on+twitter%3A+A+new+annotated+corpus+)
28. Sandesh Shrestha, Bikram K.C., Akshyat Shah, Ashish Acharya, and Rabin Thapa. 2026. Multimodal-minds@chipsal 2026: A comparative study of textual, visual and multimodal architecture for nepali meme moderation. In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (C… [find](https://scholar.google.com/scholar?q=Sandesh+Shrestha%2C+Bikram+K%2EC%2E%2C+Akshyat+Shah%2C+Ashish+Acharya%2C+and+Rabin+Thapa%2E+2026%2E+Multimodal%2Dminds%40chipsal+2026%3A+A+comparative+study+of+textual%2C+visual+and+multimodal+architecture+for+nepali+meme+mo)
29. Surendrabikram Thapa, Kritesh Rauniyar, Shuvam Shiwakoti, Sweta Poudel, Usman Naseem, and Mehwish Nasim. 2023. Nehate: Large-scale annotated data shedding light on hate speech in nepali local election discourse. Frontiers in Artificial Intelligence and Applications, 372:2346– 2353. [find](https://scholar.google.com/scholar?q=Surendrabikram+Thapa%2C+Kritesh+Rauniyar%2C+Shuvam+Shiwakoti%2C+Sweta+Poudel%2C+Usman+Naseem%2C+and+Mehwish+Nasim%2E+2023%2E+Nehate%3A+Large%2Dscale+annotated+data+shedding+light+on+hate+speech+in+nepali+local+election)
30. Surendrabikram Thapa, Hariram Veeramani, Liang Hu, Qi Zhang, Wei Wang, and Usman Naseem. 2025a. A multimodal prompt-based framework for analyzing code-mixed and low-resource memes. In Proceedings of the International AAAI Conference on Web and Social Media, volume 19, pages 1913–1923. [find](https://scholar.google.com/scholar?q=Surendrabikram+Thapa%2C+Hariram+Veeramani%2C+Liang+Hu%2C+Qi+Zhang%2C+Wei+Wang%2C+and+Usman+Naseem%2E+2025a%2E+A+multimodal+prompt%2Dbased+framework+for+analyzing+code%2Dmixed+and+low%2Dresource+memes%2E+In+Proceedings+of+t)
31. Surendrabikram Thapa, Hariram Veeramani, Imran Razzak, Roy Ka-Wei Lee, and Usman Naseem. 2025b. Cross platform multimodal retrieval augmented distillation for code-switched content understanding. In Companion Proceedings of the ACM on Web Conference 2025, pages 2042– 2051. [find](https://scholar.google.com/scholar?q=Surendrabikram+Thapa%2C+Hariram+Veeramani%2C+Imran+Razzak%2C+Roy+Ka%2DWei+Lee%2C+and+Usman+Naseem%2E+2025b%2E+Cross+platform+multimodal+retrieval+augmented+distillation+for+code%2Dswitched+content+understanding%2E+In+C)
32. Anish Thapaliya. 2026. [egrantha.ai@chipsal 2026: Stochastic image captioning for robust hate speech detection in low-resource nepali memes](https://md.research.elamur.ai/free/en/papers/thapaliya-2026-egrantha-ai-chipsal-2026-stochastic/). In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL).
33. Samir Wagle, Reewaj Khanal, and Abiral Adhikari. 2026. [Meme-fusion@chipsal 2026: Multimodal ablation study of hate detection and sentiment analysis on nepali memes](https://md.research.elamur.ai/free/en/papers/wagle-2026-meme-fusion-chipsal-2026-multimodal/). In Proceedings of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL).
34. Bo Xu, Tingting Li, Junzhe Zheng, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, and Feng Xia. 2022. Met-meme: A multimodal meme dataset rich in metaphors. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pages 2887–2899. [find](https://scholar.google.com/scholar?q=Bo+Xu%2C+Tingting+Li%2C+Junzhe+Zheng%2C+Mehdi+Naseriparsa%2C+Zhehuan+Zhao%2C+Hongfei+Lin%2C+and+Feng+Xia%2E+2022%2E+Met%2Dmeme%3A+A+multimodal+meme+dataset+rich+in+metaphors%2E+In+Proceedings+of+the+45th+international+ACM+)

*Updated 2 November 2026*