Abstract
Online social media services, such as Facebook and Twitter, have recently increased in popularity. Although determining the subjects of individual posts is important for extracting users’ interests from social media, this task is nontrivial because posts are highly contextualized, informal, and limited in length. To address this problem, we propose a deep-neural-network-based approach for predicting user interests in social media. In our framework, a word-embedding technique is used to map the words in social media content into vectors. These vectors are used as input to a bidirectional gated recurrent unit (biGRU). Then, the output of the biGRU and the word-embedding vectors are used to construct a sentence matrix. The sentence matrix is then used as input to a convolutional neural network (CNN) model to predict a user’s interests. Experimental results show that our proposed method combining biGRU and CNN models outperforms existing methods for identifying users’ interests from social media. In addition, posts in social media are sensitive to trends and change with time. Here, we collected posts from two different social media platforms at different time intervals, and trained the proposed model with one set of social media data and tested it with another set of social media data. The experimental results showed that our proposed model can predict users’ interests from the independent data set with high accuracies.
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Acknowledgements
This research was supported by the Bio-Synergy Research Project (NRF-2016M3A9C4939665) of the Ministry of Science, ICT and Future Planning through the National Research Foundation, and by Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research & Development Program 2016.
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Kang, J., Choi, H. & Lee, H. Deep recurrent convolutional networks for inferring user interests from social media. J Intell Inf Syst 52, 191–209 (2019). https://doi.org/10.1007/s10844-018-0534-3
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DOI: https://doi.org/10.1007/s10844-018-0534-3