1st Empirical Studies in the Domain of Social Network Computing


This aim of this workshop is to provide a meeting point for both practitioners and researchers to discuss the empirical studies conducted to manage the social network computing aspects (such as community detection, opinion and sentiments of community, defend online harassment through community coordination) via data mining, text mining and machine learning algorithms. This workshop will provide an opportunity to present the empirical evidences regarding implications of data mining, text mining, and machine learning algorithms.

Due to repaid increase in scalability and volume of SAN (Social Area Networks) data and its implications in different domains such as telemedicine and telehealth, business, sentiment analysis, and software developments has motivated the focus of the research community and domain experts in their effective decision making. Recently, research community reported the existence of high volume of unstructured data in terms of text, audio, images and video and extraction of meaning full data for decision makers remain a hot issue for implications of data mining, text mining and machine learning techniques. In this regards, SAN research community of different domains needs to get empirical evidence regarding implications of highly effective learning technique.

This workshop will help to provide a platform for academic and industrial researchers to share their empirical evidence regarding implication of learning algorithm to manage the SAN aspects.


List of Topics

·         Data processing techniques for social network computing

·         Intelligent data discovery techniques

·         Social network computing technique for crowdsourcing based or global software

·         development

·         Social network computing technique to discover hidden patterns in online business

·         strategies

·         Text enriching approaches for social computing techniques

·         Topic modeling for social computing

·         Threat and vulnerability for social computing

·         Social computing for education

·         Social computing for business

·         Text enriching for social computing techniques

·         Data analytics issues for social computing


Organizing Committee

·         Shahid Hussain,  COMSATS Institute of Information Technology, Islamabad, Pakistan

·         Arif Ali Khan, Nanjing University of Aeronautics and Astronautics, China

·         Nafees Ur Rehman, University of Konstanz, Germany


Program Committee

·        Aoutif Amine, ENSA, Ibn Tofail University, Morocco

·        Gwanggil Jeon, Incheon National University, Korea

·        Siti Salwa Salim, University of Malaya, Malaysia

·        Jacky  Keung, City University of Hong Kong, Hong Kong

·        Manzoor Ilahi, COMSATS University, Islamabad, Pakistan

·        Mansoor Ahmad, COMSATS University, Islamabad, Pakistan

·        Muhammad Khalid Sohail, COMSATS University, Islamabad, Pakistan

·        Kifayat Alizai, Dep. Computer Science, National Univ. Computer & Emerging Sciences (FAST-NUCES), Islamabad, Pakistan

·        Abdul Mateen, Federal Urdu University of Arts, Science & Technology, Islamabad, Pakistan

·        Hanna Hachimi, Ensa of Kenitra IBN Tofail University, Morocco

·        Kwabena Bennin Ebo, City University of Hong Kong, Hong Kong

·        Wiem Khlif, University of Sfax, Tunisia

·        Muhammad Shahid, Gomal University, DIK, Pakistan

·        Salima Banqdara, University of Benghazi, Libya

·        Mariam Akbar, COMSATS University, Islamabad, Pakistan


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