American Journal of Computer Science and Information Technology

Quality assessment of user generated content on twitter-A deep learning based approach

8^th Edition of International Conference on Big Data & Data Science
March 04-05, 2019 Barcelona, Spain

Irfan Mohiuddin, Hassan Mathkour, Muhammad Al-Qurishi and Majed Al-Rubaian

King Saud University Riyadh, Saudi Arabia

ScientificTracks Abstracts: Am J Compt Sci Inform Technol

DOI: 10.21767/2349-3917-C1-008

Abstract

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 M.Sc. in Computer Science from King Saud University, Riyadh-Saudi Arabia, where he is currently working as a Researcher while pursuing his Ph.D. degree in Computer Science. His research interests include Data Science, Social Media Data Analysis, Cloud Computing, Virtualization and Social Internet of Things.

E-mail: irfanm@ksu.edu.sa