Browsing by Author "Shen, Chih-Ya"

Now showing 1 - 4 of 4

A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
(2018-07-01)
Shuai, Hong-Han
;
Shen, Chih-Ya
;
Yang, De-Nian
;
Lan, Yi-Feng Carol
;
Lee, Wang-Chien
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.
On Finding Socially Tenuous Groups for Online Social Networks
(2017-01-01)
Shen, Chih-Ya
;
Huang, Liang-Hao
;
Yang, De-Nian
;
Shuai, Hong-Han
;
Lee, Wang-Chien
Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph theoretical approaches for solving MkTG on general graphs effectively and efficiently. Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.
S-AKA: A Provable and Secure Authentication Key Agreement Protocol for UMTS Networks
(2011-11-01)
Huang, Yu-Lun
;
Shen, Chih-Ya
;
Shieh, Shiuhpyng Winston
;
資訊工程學系
;
電機工程學系
The authentication and key agreement (AKA) protocol of Universal Mobile Telecommunication System (UMTS), which is proposed to solve the vulnerabilities found in Global System for Mobile Communications (GSM) systems, is still vulnerable to redirection and man-in-the-middle attacks. An adversary can mount these attacks to eavesdrop or mischarge the subscribers in the system. In this paper, we propose a secure AKA (S-AKA) protocol to cope with these problems. The S-AKA protocol can reduce bandwidth consumption and the number of messages required in authenticating mobile subscribers. We also give the formal proof of the S-AKA protocol to guarantee its robustness.
"She is in a Bad Mood Now": Leveraging Peers to Increase Data Quantity via a Chatbot-Based ESM
(2019-01-01)
Chang, Yu-Lin
;
Chang, Yung-Ju
;
Shen, Chih-Ya
;
資訊工程學系
;
Department of Computer Science
The experience sampling method (ESM) is widely used for collecting in situ experiences in various domains. One known limitation, however, is its reliance on participants being receptive to ESM questionnaires at the sampled moments. At moments when participants cannot notice or respond to an ESM questionnaire, researchers cannot obtain a response. In this research, we explored the feasibility of inviting peers to provide information about participants in an ESM study. Results from a two-week experiment with a total of 27 participants and 82 peers showed that including peers' ESM responses increased ESM data quantity. Furthermore, the agreement between the peers' and the participants responses could be maintained by asking peers' confidence. Even considering only data with high confidence could increase data quantity. Moreover, inviting peers had a positive impact on the participant's compliance to respond. These results suggest that using peer ESM to obtain more in-situ data about participants is promising.