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To Study and Analyse Human Behaviour on Social Media Platforms and their Interest Inference using Deep Learning

Rezni S, Ganga Gyatso, Devarshi Kothari, Churhchan Rai Rai, Divyansh Swami

Abstract


In this age of digital world, where data are generated at an exponential rate there are many applications which require a highly accurate ways of inferencing a user’s interest from those data. The data are usually collected from multiple social network platforms that users engaged in.  User interest exhibits dual-heterogeneities: it is complementarily and multiple social networks reflects them comprehensively; interest of user are not independent of each other rather inter-correlated in a nonuniform way. Although past approaches towards these problems had great success that considers the dual heterogeneities simultaneously, consistency of sources, uses multi-sourced and multi-tasked learning scheme. However, in this project we propose regularizing the source confidence of data extracted from different social network platform, because each source contributes differently to the prediction of user interest. Comprehensive experiment results of inclusion of source confidence showed better accuracy of existing models. In addition, we will deploy an android application that will take users input data from multiple social networks and predict their interest.

 


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References


Jia, Y., Song, X., Zhou, J., Liu, L., Nie, L., & Rosenblum, D. S. (2016, February). Fusing social networks with deep learning for volunteerism tendency prediction. In Thirtieth AAAI conference on artificial intelligence.

Song, X., Nie, L., Zhang, L., Liu, M., & Chua, T. S. (2015, June). Interest inference via structure-constrained multi-source multi-task learning. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

Song, X., Nie, L., Zhang, L., Akbari, M., & Chua, T. S. (2015, August). Multiple social network learning and its application in volunteerism tendency prediction. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 213-222).

Hayat, M. K., Daud, A., Alshdadi, A. A., Banjar, A., Abbasi, R. A., Bao, Y., & Dawood, H. (2019). Towards deep learning prospects: insights for social media analytics. IEEE Access, 7, 36958-36979.

Amin, F., Ahmad, A., & Choi, G. S. (2018, April). To study and analyse human behaviours on social networks. In 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC) (pp. 233-236). IEEE.

‘‘ Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.

Van Gerven, M., & Bohte, S. (2017). Artificial neural networks as models of neural information processing. Frontiers in Computational Neuroscience, 11, 114..

Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127.

Larochelle, H., & Bengio, Y. (2008, July). Classification using discriminative restricted Boltzmann machines. In Proceedings of the 25th international conference on Machine learning (pp. 536-543).


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