IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. Read the full scope of TKDE


Expand your horizons with Colloquium, a monthly survey of abstracts from all CS transactions! Replaces OnlinePlus in January 2017.


From the July 2018 issue

A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

By Hong-Han Shuai, Chih-Ya Shen, De-Nian Yang, Yi-Feng Carol Lan, Wang-Chien Lee, Philip S. Yu, and Ming-Syan Chen

Featured Article The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.

download PDF View the PDF of this article      csdl View this issue in the digital library


Editorials and Announcements

Announcements

  • TKDE now offers authors access to Code Ocean. Code Ocean is a cloud-based executable research platform that allows authors to share their algorithms in an effort to make the world’s scientific code more open and reproducible. Learn more or sign up for free.
  • We are pleased to announce that Xuemin Lin, a Scientia Professor in the School of Computer Science and Engineering at the University of New South Wales, Australia, has been named the new Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering starting in 2017.

Editorials


Guest Editorials


Reviewers List


Annual Index


Access recently published TKDE articles

RSS Subscribe to the RSS feed of recently published TKDE content

mail icon Sign up for e-mail notifications through IEEE Xplore Content Alerts

preprints icon View TKDE preprints in the Computer Society Digital Library

Computing Now