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Big Data 2019 American Journal of Computer Science and Information Technology
ISSN: 2349-3917 Page 19
March 04-05, 2019
Barcelona, Spain
8th Edition of International Conference on
Big Data &
Data Science
Social Media today is a platform for millions of active users
globally to share their content. Each second, there are
thousands of messages or comments posted on different
social networks. With these staggering numbers of user
generated content (UGC), challenges are bound to surface.
One such challenge is to assess the quality of UGC in social
media because the content generated in social media could
have positive or negative impact on fellow users and common
people too. Low quality content not only impacts the user’s
content browsing experience, but also deteriorates the
aesthetic value of social media. Therefore, our aim is to assess
the quality of content accurately to promote the propagation of
high quality content. Successful assessment of quality of UGC
in social media fosters the growth of high utility UGC, which
could be used by other applications and organizations for
societal or organizational benefits. In this paper, we propose
a deep learning based model, that leverages the quality
assessment of UGC. The experimental results demonstrate
that our proposed model results in high accuracy and low loss.
Recent Publications
1. “Secure distributed adaptive bin packing algorithm
for cloud storage” in Future Generation Computer
Systems (Q1, IF:3.99), (2018).
2. “Cloud computing services for iot –analyzing the
security challenges and strategies” in international
conference on industrial internet of things and smart
manufacturing [(isbn: 978-1-912532-06-3)] (2018)
3. “Workload aware vm consolidation method (wavmcm)
in cloud computing environment” in Journal of Parallel
and Distributed Computing (Oct, 2018)
4. Contributor in a book titled “multimedia and cloud
computing-architecture and applications”, College
of Computer and Information Sciences, King Saud
University (2018).
5. Authored a chapter in book titled “industrial internet
of things and smart manufacturing”, Springer
Publications. (Due for release).
Biography
Irfan Mohiuddin received his . in Computer Science from King Saud
University, Riyadh-Saudi Arabia, where he is currently working as a Re-
searcher while pursuing his Ph.D. degree in Computer Science. His research
interests include Data Science, Social Media Data Analysis, Cloud Comput-
ing, Virtualization and Social Internet of Things.
Irfan Mohiuddin, Hassan Mathkour, Muhammad Al-Qurishi and Majed Al-Rubaian
King Saud University Riyadh, Saudi Arabia
Irfan Mohiuddin et al., Am J Compt Sci Inform Technol 2019, Volume 7
DOI: 10.21767/2349-3917-C1-008
Quality assessment of user generated content
on twitter-A deep learning based approach