CrisisKAN: Knowledge-Infused and Explainable Multimodal Attention Network for Crisis Event Classification | Advances in Information Retrieval (2024)

CrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (2)

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  • Shubham Gupta https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    CrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (3)http://orcid.org/0000-0003-4908-843X

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  • Nandini Saini https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    CrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (4)http://orcid.org/0000-0003-1736-943X

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  • Suman Kundu https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    CrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (5)http://orcid.org/0000-0002-7856-4768

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  • Debasis Das https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    https://ror.org/03yacj906Department of Computer and Science Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    CrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (6)http://orcid.org/0000-0001-6205-4096

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Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part IIMar 2024Pages 18–33https://doi.org/10.1007/978-3-031-56060-6_2

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Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part II

CrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification

Pages 18–33

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CrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (7)

Abstract

Pervasive use of social media has become the emerging source for real-time information (like images, text, or both) to identify various events. Despite the rapid growth of image and text-based event classification, the state-of-the-art (SOTA) models find it challenging to bridge the semantic gap between features of image and text modalities due to inconsistent encoding. Also, the black-box nature of models fails to explain the model’s outcomes for building trust in high-stakes situations such as disasters, pandemic. Additionally, the word limit imposed on social media posts can potentially introduce bias towards specific events. To address these issues, we proposed CrisisKAN, a novel Knowledge-infused and Explainable Multimodal Attention Network that entails images and texts in conjunction with external knowledge from Wikipedia to classify crisis events. To enrich the context-specific understanding of textual information, we integrated Wikipedia knowledge using proposed wiki extraction algorithm. Along with this, a guided cross-attention module is implemented to fill the semantic gap in integrating visual and textual data. In order to ensure reliability, we employ a model-specific approach called Gradient-weighted Class Activation Mapping (Grad-CAM) that provides a robust explanation of the predictions of the proposed model. The comprehensive experiments conducted on the CrisisMMD dataset yield in-depth analysis across various crisis-specific tasks and settings. As a result, CrisisKAN outperforms existing SOTA methodologies and provides a novel view in the domain of explainable multimodal event classification. (Code repository: https://github.com/shubhamgpt007/CrisisKAN)

References

  1. 1.Abavisani, M., Wu, L., Hu, S., Tetreault, J., Jaimes, A.: Multimodal categorization of crisis events in social media. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14679–14689 (2020)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (8)
  2. 2.Adadi ABerrada MPeeking inside the black-box: a survey on explainable artificial intelligence (XAI)IEEE Access20186521385216010.1109/ACCESS.2018.2870052Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (9)Cross Ref
  3. 3.Agarwal MLeekha MSawhney RRatn Shah RKumar Yadav RKumar Vishwakarma Det al.Jose JMet al.MEMIS: multimodal emergency management information systemAdvances in Information Retrieval2020ChamSpringer47949410.1007/978-3-030-45439-5_32Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (11)Digital Library
  4. 4.Alam, F., Ofli, F., Imran, M.: CrisisMMD: multimodal twitter datasets from natural disasters. In: Proceedings of the International AAAI Conference on Web and Social Media. vol. 12 (2018)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (13)
  5. 5.Anonymous: EA2n: Evidence-based AMR attention network for fake news detection. In: Submitted to The Twelfth International Conference on Learning Representations (2023). https://openreview.net/forum?id=5rrYpa2vts, under reviewGoogle ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (14)
  6. 6.Arevalo, J., Solorio, T., Montes-y Gómez, M., González, F.A.: Gated multimodal units for information fusion (2017). arXiv preprint arXiv:1702.01992Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (15)
  7. 7.Bandyopadhyay, D., Kumari, G., Ekbal, A., Pal, S., Chatterjee, A., BN, V.: A knowledge infusion based multitasking system for sarcasm detection in meme. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. LNCS, vol. 13980. Springer, Cham (2023). DOI: https://doi.org/10.1007/978-3-031-28244-7_7Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (16)Digital Library
  8. 8.Chu, S.Y., Lee, M.S.: MT-DETR: robust end-to-end multimodal detection with confidence fusion. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5241–5250 (2023). DOI: https://doi.org/10.1109/WACV56688.2023.00522Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (18)Cross Ref
  9. 9.Clark, K., Luong, M., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. In: ICLR. OpenReview.net (2020)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (20)
  10. 10.Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-XL: Attentive language models beyond a fixed-length context (2019). arXiv preprint arXiv:1901.02860Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (21)
  11. 11.Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding (2018). arXiv preprint arXiv:1810.04805Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (22)
  12. 12.Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: NAACL Volume 1 (Long and Short Papers), pp. 4171–4186. ACL, Minneapolis, Minnesota (2019)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (23)
  13. 13.Ferragina, P., Scaiella, U.: TAGME: on-the-fly annotation of short text fragments (by wikipedia entities). In: ICIKM, pp. 1625–1628. CIKM 2010, ACM, New York, NY, USA (2010)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (24)
  14. 14.f*ckui, A., Park, D.H., Yang, D., Rohrbach, A., Darrell, T., Rohrbach, M.: Multimodal compact bilinear pooling for visual question answering and visual grounding (2016). arXiv preprint arXiv:1606.01847Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (25)
  15. 15.Gallo, I., Ria, G., Landro, N., La Grassa, R.: Image and text fusion for UPMC food-101 using BERT and CNNs. In: 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1–6. IEEE (2020)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (26)
  16. 16.Gupta SKundu SInteraction graph, topical communities, and efficient local event detection from social streamsExpert Syst. Appl.202323210.1016/j.eswa.2023.120890Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (27)Digital Library
  17. 17.Gupta, S., Yadav, N., Sainath Reddy, S., Kundu, S.: FakEDAMR: Fake news detection using abstract meaning representation (2023)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (29)
  18. 18.Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: GhostNet: more features from cheap operations. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1577–1586 (2020)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (30)
  19. 19.He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (31)
  20. 20.Holzinger AMalle BSaranti APfeifer BTowards multi-modal causability with graph neural networks enabling information fusion for explainable AIInf. Fusion202171283710.1016/j.inffus.2021.01.008Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (32)Cross Ref
  21. 21.Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (34)
  22. 22.Huang, Z., Zeng, Z., Liu, B., Fu, D., Fu, J.: Pixel-BERT: Aligning image pixels with text by deep multi-modal transformers (2020). arXiv preprint arXiv:2004.00849Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (35)
  23. 23.Hubenthal, M., Kumar, S.: Image-text pre-training for logo recognition. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1145–1154 (2023). DOI: https://doi.org/10.1109/WACV56688.2023.00120Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (36)Cross Ref
  24. 24.Hunt KWang BZhuang JMisinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checksNat. Hazards2020103186188310.1007/s11069-020-04016-6Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (38)Cross Ref
  25. 25.Joshi GWalambe RKotecha KA review on explainability in multimodal deep neural netsIEEE Access20219598005982110.1109/ACCESS.2021.3070212Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (40)Cross Ref
  26. 26.Kiela, D., Bhooshan, S., Firooz, H., Perez, E., Testuggine, D.: Supervised multimodal bitransformers for classifying images and text (2019). arXiv preprint arXiv:1909.02950Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (42)
  27. 27.Kiela, D., Grave, E., Joulin, A., Mikolov, T.: Efficient large-scale multi-modal classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 32 (2018)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (43)
  28. 28.Kim, W., Son, B., Kim, I.: ViLT: vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning, pp. 5583–5594. PMLR (2021)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (44)
  29. 29.Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks (2014). CoRR abs/1404.5997Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (45)
  30. 30.Kwan, J.S.L., Lim, K.H.: Understanding public sentiments, opinions and topics about COVID-19 using twitter. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 623–626. IEEE (2020)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (46)
  31. 31.Li, G., Duan, N., Fang, Y., Gong, M., Jiang, D.: Unicoder-VL: a universal encoder for vision and language by cross-modal pre-training. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 11336–11344 (2020)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (47)
  32. 32.Li, L.H., Yatskar, M., Yin, D., Hsieh, C.J., Chang, K.W.: VisualBERT: A simple and performant baseline for vision and language (2019). arXiv preprint arXiv:1908.03557Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (48)
  33. 33.Liang, T., Lin, G., Wan, M., Li, T., Ma, G., Lv, F.: Expanding large pre-trained unimodal models with multimodal information injection for image-text multimodal classification. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15471–15480 (2022). DOI: https://doi.org/10.1109/CVPR52688.2022.01505Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (49)Cross Ref
  34. 34.Long, S., Han, S.C., Wan, X., Poon, J.: GraDual: graph-based dual-modal representation for image-text matching. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2463–2472 (2022). DOI: https://doi.org/10.1109/WACV51458.2022.00252Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (51)Cross Ref
  35. 35.Mao, X., et al.: Towards robust vision transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12042–12051 (2022)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (53)
  36. 36.Moraliyage HSumanasena VDe Silva DNawaratne RSun LAlahakoon DMultimodal classification of onion services for proactive cyber threat intelligence using explainable deep learningIEEE Access202210560445605610.1109/ACCESS.2022.3176965Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (54)Cross Ref
  37. 37.Nazura, J., Muralidhara, B.L.: Semantic classification of tweets: a contextual knowledge based approach for tweet classification. In: 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–6 (2017). DOI: https://doi.org/10.1109/IISA.2017.8316358Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (56)Cross Ref
  38. 38.Petsiuk, V., et al.: Black-box explanation of object detectors via saliency maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11443–11452 (2021)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (58)
  39. 39.Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860 (2010)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (59)
  40. 40.Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626 (2017). DOI: https://doi.org/10.1109/ICCV.2017.74Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (60)Cross Ref
  41. 41.Shu, K., Zhou, X., Wang, S., Zafarani, R., Liu, H.: The role of user profiles for fake news detection. In: IEEE/ACM ASONAM, pp. 436–439. ASONAM 2019, Association for Computing Machinery, New York, NY, USA (2020)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (62)
  42. 42.Singh, A., et al.: FLAVA: a foundational language and vision alignment model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15638–15650 (2022)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (63)
  43. 43.Tahayna BAyyasamy RAkbar RContext-aware sentiment analysis using tweet expansion methodJ. ICT Res. Appl.202216213815110.5614/itbj.ict.res.appl.2022.16.2.3Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (64)Cross Ref
  44. 44.Vielzeuf, V., Lechervy, A., Pateux, S., Jurie, F.: CentralNet: a multilayer approach for multimodal fusion. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (66)
  45. 45.Wang, Y., Xu, X., Yu, W., Xu, R., Cao, Z., Shen, H.T.: Combine early and late fusion together: a hybrid fusion framework for image-text matching. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2021)Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (67)
  46. 46.Yang ZDai ZYang YCarbonell JSalakhutdinov RLe QVXLNet: Generalized Autoregressive Pretraining for Language Understanding2019Red Hook, NY, USACurran Associates Inc.Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (68)
  47. 47.Zadeh, A., Chen, M., Poria, S., Cambria, E., Morency, L.P.: Tensor fusion network for multimodal sentiment analysis (2017). arXiv preprint arXiv:1707.07250Google ScholarCrisisKAN: Knowledge-Infused andExplainable Multimodal Attention Network forCrisis Event Classification | Advances in Information Retrieval (69)

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      Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part II

      Mar 2024

      504 pages

      ISBN:978-3-031-56059-0

      DOI:10.1007/978-3-031-56060-6

      • Editors:
      • Nazli Goharian

        https://ror.org/05vzafd60Georgetown University, Washington, WA, USA

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      • Nicola Tonellotto

        https://ror.org/03ad39j10University of Pisa, PISA, Pisa, Italy

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      • Yulan He

        https://ror.org/0220mzb33King's College London, London, UK

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      • Aldo Lipani

        https://ror.org/02jx3x895University College London, London, UK

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      • Graham McDonald

        https://ror.org/00vtgdb53University of Glasgow, Glasgow, UK

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      • Craig Macdonald

        https://ror.org/00vtgdb53University of Glasgow, Glasgow, UK

        ,
      • Iadh Ounis

        https://ror.org/00vtgdb53University of Glasgow, Glasgow, UK

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          • Published: 24 March 2024

          Author Tags

          • Multimodal Network
          • Explainable
          • Knowledge Infusion
          • Crisis Detection

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