Towards Deep Learning Prospects: Insights for Social Media Analytics

Malik Khizar Hayat, Ali Daud, Abdulrahman A. Alshdadi, Ameen Banjar, Rabeeh Ayaz Abbasi, Yukun Bao, Hussain Dawood
2019 IEEE Access  
Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing. In order to the exponential development and widespread availability of digital social media (SM), analyzing these data using traditional tools and technologies is tough or even intractable. DL is found as an appropriate solution to this problem. In this paper, we keenly discuss the practiced DL architectures by presenting a taxonomy-oriented
more » ... mmary, following the major efforts made toward the SM analytics (SMA). Nevertheless, instead of the technical description, this paper emphasis on describing the SMA-oriented problems with the DL-based solutions. To this end, we also highlight the DL research challenges (such as scalability, heterogeneity, and multimodality) and future trends. INDEX TERMS Social media data, dynamic network, deep learning, feature learning. 36958 He has published about 70 papers in reputed international Impact Factor journals and conferences. He has taken part in many research projects, and is the Principal Investigator (PI) of two projects. His research interests include data mining, social network analysis and mining, probabilistic models, scientometrics, and natural language processing.
doi:10.1109/access.2019.2905101 fatcat:65mxyey3frdrfngvbfnfss3gpa