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Deep Learning Approaches for Socially Contextualized Acoustic Event Detection in Social Media Posts

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Good Practices and New Perspectives in Information Systems and Technologies (WorldCIST 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 990))

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Abstract

In recent years, social media platforms have become an essential source of information. Therefore, with their increasing popularity, there is a growing need for effective methods for detecting and analyzing their content in real time. Deep learning is a machine learning technique that teaches computers to understand complex patterns. Deep learning techniques are promising for analyzing acoustic signals from social media posts. In this article, a novel deep learning approach is proposed for socially contextualized event detection based on acoustic signals. The approach integrates the power of deep learning and meaningful features such as Mel frequency cepstral coefficients. To evaluate the effectiveness of the proposed method, it was applied to a real dataset collected from social protests in Iran. The results show that the proposed system can find a protester’s clip with an accuracy of approximately 82.57%. Thus, the proposed approach has the potential to significantly improve the accuracy of systems for filtering social media posts.

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References

  1. Souza, R.C.S.N.P., de Brito, D.E.F., Cardoso, R.L., de Oliveira, D.M., Meira, W., Pappa, G.L.: An evolutionary methodology for handling data scarcity and noise in monitoring real events from social media data. In: Bazzan, A.L.C., Pichara, K. (eds.) IBERAMIA 2014. LNCS (LNAI), vol. 8864, pp. 295–306. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12027-0_24

    Chapter  Google Scholar 

  2. Liang, Y., Caverlee, J., Cao, C.: A noise-filtering approach for spatio-temporal event detection in social media. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 233–244. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16354-3_25

    Chapter  Google Scholar 

  3. Aiello, L.M., Schifanella, R., Quercia, D., Aletta, F.: Chatty maps: constructing sound maps of urban areas from social media data. Royal Soc. Open Sci. 3(3), 150690 (2016). https://doi.org/10.1098/rsos.150690

  4. He, X., Lu, D., Margolin, D., Wang, M., Idrissi, S.E., Lin, Y.-R.: The signals and noise: actionable information in improvised social media channels during a disaster. In: Proceedings of the 2017 ACM on Web Science Conference, pp. 33–42 (2017). https://doi.org/10.1145/3091478.3091501

  5. dos Santos Marques, J.M., Valente, L.F.G., Ferreira, S.B.L., Cappelli, C., Salgado, L.: Audio description on Instagram: evaluating and comparing two ways of describing images for visually impaired. In: ICEIS, issue 3, pp. 29–40 (2017). https://doi.org/10.5220/0006282500290040

  6. Callcut, R.A., Moore, S., Wakam, G., Hubbard, A.E., Cohen, M.J.: Finding the signal in the noise: could social media be utilized for early hospital notification of multiple casualty events? PLOS one 12(10), e0186118 (2017). https://doi.org/10.1371/journal.pone.0186118

  7. Tindall, D.B., Robinson, J.L.: Collective action to save the ancient temperate rainforest: social networks and environmental activism in Clayoquot sound. Ecol. Soc. 22(1) (2017). https://doi.org/10.5751/ES-09042-220140

  8. Gasco, L., Clavel, C., Asensio, C., de Arcas, G.: Beyond sound level monitoring: exploitation of social media to gather citizens subjective response to noise. Sci. Total Environ. 658, 69–79 (2019). https://doi.org/10.1016/j.scitotenv.2018.12.071

    Article  Google Scholar 

  9. Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S.-Y., Sainath, T.: Deep learning for audio signal processing. In: IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 2, pp. 206–219 (2019). https://doi.org/10.1109/JSTSP.2019.2908700

  10. Kumar, M., et al.: An event detection technique using social media data (2019). http://hdl.handle.net/10603/285467

  11. Ye, S., et al.: Turning information dissipation into dissemination: Instagram as a communication enhancing tool during the Covid-19 pandemic and beyond. J. Chem. Educ. 97(9), 3217–3222 (2020). https://doi.org/10.1021/acs.jchemed.0c00724

  12. Belcastro, L., et al.: Using social media for sub-event detection during disasters. J. Big Data 8(1), 1–22 (2021). https://doi.org/10.1186/s40537-021-00467-1

    Article  Google Scholar 

  13. Verlin, S.: Abbreviation establishment in Instagram social media. ETDC: Indonesian J. Res. Educ. Rev. 1(4), 588–598 (2022). https://doi.org/10.51574/ijrer.v1i4.753

  14. Bahuguna, R., Nisha Chandran, S., Gangodkar, D.: Recent trends in event detection from twitter using multimodal data. In: AIP Conference Proceedings, vol. 2481, no. 1. AIP Publishing (2022). https://doi.org/10.1063/5.0104560

  15. Li, Q., Chao, Y., Li, D., Lu, Y., Zhang, C.: Event detection from social media stream: methods, datasets and opportunities. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3509–3516. IEEE (2022). https://doi.org/10.1109/BigData55660.2022.10020411

  16. Kolajo, T., Daramola, O., Adebiyi, A.A.: Real-time event detection in social media streams through semantic analysis of noisy terms. J. Big Data 9(1), 1–36 (2022). https://doi.org/10.1186/s40537-022-00642-y

    Article  Google Scholar 

  17. Mredula, M.S., Dey, N., Rahman, M.S., Mahmud, I., Cho, Y.-Z.: A review on the trends in event detection by analyzing social media platforms’ data. Sensors 22(12), 4531 (2022). https://doi.org/10.3390/s22124531

  18. Singh, J., Pandey, D., Singh, A.K.: Event detection from real-time twitter streaming data using community detection algorithm. Multimedia Tools Appl., 1–28 (2023). https://doi.org/10.1007/s11042-023-16263-3

  19. Lasri, I., Riadsolh, A., Elbelkacemi, M.: Real-time twitter sentiment analysis for Moroccan universities using machine learning and big data technologies. Int. J. Emerging Technol. Learn. 18(5), (2023). https://doi.org/10.3991/ijet.v18i05.35959

  20. Zou, H., Si, Y., Chen, C., Rajan, D., Chng, E.S.: Speech emotion recognition with co-attention based multi-level acoustic information. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7367–7371. IEEE (2022). https://doi.org/10.1109/ICASSP43922.2022.9747095

  21. Bai, J., Chen, J., Wang, M.: Multimodal urban sound tagging with spatiotemporal context. IEEE Trans. Cogn. Dev. Syst., 555–565 (2022). https://doi.org/10.1109/TCDS.2022.3160168

  22. Hajihashemi, V., Alavigharahbagh, A., Oliveira, H.S., Cruz, P.M., Tavares, J.M.R.S.: Novel time-frequency based scheme for detecting sound events from sound background in audio segments. In: Tavares, J.M.R.S., Papa, J.P., González Hidalgo, M. (eds.) CIARP 2021. LNCS, vol. 12702, pp. 402–416. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93420-0_38

    Chapter  Google Scholar 

  23. Hajihashemi, V., Gharahbagh, A.A., Cruz, P.M., Ferreira, M.C., Machado, J.J., Tavares, J.M.R.: Binaural acoustic scene classification using wavelet scattering, parallel ensemble classifiers and nonlinear fusion. Sensors 22(4), 1535 (2022). https://doi.org/10.3390/s22041535

    Article  Google Scholar 

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Acknowledgements

This article is partially a result of the project Sensitive Industry, co-funded by the European Regional Development Fund (ERDF), through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement. The first author would like to thank “Fundação para a Ciência e Tecnologia” (FCT) for his Ph.D. grant with reference 2021.08660.BD.

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Authors and Affiliations

  1. Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal

    Vahid Hajihashemi & Abdorreza Alavi Gharahbagh

  2. Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal

    Marta Campos Ferreira, José J. M. Machado & João Manuel R. S. Tavares

Authors
  1. Vahid Hajihashemi
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  2. Abdorreza Alavi Gharahbagh
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  3. Marta Campos Ferreira
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  4. José J. M. Machado
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  5. João Manuel R. S. Tavares
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Corresponding author

Correspondence to João Manuel R. S. Tavares .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. Institute of Information Technology, Lodz University of Technology, Łódz, Poland

    Aneta Poniszewska-Marańda

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Hajihashemi, V., Gharahbagh, A.A., Ferreira, M.C., Machado, J.J.M., Tavares, J.M.R.S. (2024). Deep Learning Approaches for Socially Contextualized Acoustic Event Detection in Social Media Posts. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-031-60328-0_35

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