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Abstract: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.INTRODUCTIONWith the explosive growth in popularity of social networking and messaging apps, online social networks (OSNs) have become a part of many people’s daily lives. Most research on social network mining focuses on discovering the knowledge behind the data for improving people’s life. While OSNs seemingly expand their users’ capability in increasing social contacts, they may actually decrease the face-to-face interpersonal interactions in the real world. Due to the epidemic scale of these phenomena, new terms such as Phubbing (Phone Snubbing) and Nomophobia (No Mobile Phone Phobia) have been created to describe those who cannot stop using mobile social networking apps. In fact, some social network mental disorders (SNMDs), such as Information Overload and Net Compulsion, have been recently noted.1 For example, studies point out that 1 in 8 Americans suffer from problematic Internet use2. Moreover, leading journals in mental health, such as the American Journal of Psychiatry, have reported that the SNMDs may incur excessive use, depression, social withdrawal, and a range of other negative repercussions. Indeed, these symptoms are important components of diagnostic criteria for SNMDs e.g., excessive use of social networking apps – usually associated with a loss of the sense of time or a neglect of basic drives, and withdrawal – including feelings of anger, tension, and/or depression when the computer/apps are inaccessible. SNMDs are social-oriented and tend to happen to users who usually interact with others via online social media. Those with SNMDs usually lack offline interactions, and as a result seek cyber-relationships to compensate. Today, identification of potential mental disorders often falls on the shoulders of supervisors (such as teachers or parents) passively. However, since there are very few notable physical risk factors, the patients usually do not actively seek medical or psychological services. Therefore, patients would only seek clinical interventions when their conditions become very severe.Existing System:Psychology has identiﬁed several crucial mental factors related to SNMDs, they are mostly examined as standard diagnostic criteria in survey questionnaires. To automatically detect potential SNMD cases of OSN users, extracting these factors to assess users’ online mental states is very challenging. For example, the extent of loneliness and the effect of disinhibition of OSN users are not easily observable.3 Therefore, there is a need to develop new approaches for detecting SNMD cases of OSN users. We argue that mining the social network data of individuals as a complementary alternative to the conventional psychological approaches provides an excellent opportunity to actively identify those cases at an early stage.Proposed System:Specifically, we formulate the task as a semi-supervised classiﬁcation problem to detect three types of SNMDs : i) Cyber-Relationship Addiction, which shows addictive behavior for building online relationships; ii)Net Compulsion, which shows compulsive behavior for online social gaming or gambling; and iii) Information Overload, which is related to uncontrollable surfing. By exploiting machine learning techniques with the ground truth obtained via the current diagnostic practice in Psychology, we extract and analyze the following crucial categories of features from OSNs: 1) social comparison, 2) social structure, 3) social diversity, 4) para social relationships, 5) online and offline interaction ratio, 6) social capital, 7) disinhibition, 8) self-disclosure, and 9) bursting temporal behavior. These features capture important factors or serve as proxies for SNMD detection. For example, studies manifest that users exposed to positive posts from others on Facebook with similar background are inclined to feel malicious envy and depressed due to the social comparison. The depression leads users to disorder behaviors, such as information overload or net compulsion.SYSTEM REQUIREMENTS:HARDWARE REQUIREMENTS: System : Pentium Dual Core. Hard Disk : 120 GB. Monitor : 15’’ LED Input Devices : Keyboard, Mouse Ram : 1 GBSOFTWARE REQUIREMENTS: Operating system : Windows 7. Coding Language : JAVA/J2EE Tool : Netbeans 7.2.1 Database : MYSQLHong-HanShuai, Chih-Ya Shen, De-NianYang, Senior Member, IEEE, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu, Fellow, IEEE andMing-Syan Chen, Fellow, IEEE “A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining”, IEEE Transactions Knowledge and data Engineering, 2018.
A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
INR 5000 INR 5000 5c9a0811fa45d90001aa20bc False 2019-03-26T11:08:01
Abstract:Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy
INR 5000 INR 5000 5c98d98a12cbcc0001e7bc9a False 2019-03-25T13:37:14