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INTRODUCTION OF THE PROJECT
The explosive growth in popularity of social networking results in the problematic usage. An increasing number of social network mental disorders (SNMDs), like Cyber-Relationship Addiction, Information Overload, and Net Compulsion, are recently noted. Symptoms of those mental disorders are usually observed passively today, leading to delayed clinical intervention. during this paper, we argue that mining online social behavior provides a chance to actively identify SNMDs at an early stage. it's challenging to detect SNMDs because the mental status can't be directly observed from online group action logs. Our approach, new and innovative to the practice of SNMD detection, doesn't believe self-revealing of these mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network mental disturbance 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 replacement SNMD-based Tensor Model (STM) to enhance the accuracy. to extend the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3126 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.
EXISTING SYSTEM
King investigate the matter of simulated gambling via digital and social media to research the correlation of various factors, e.g., grade, ethnicity. Baumer report the web user behavior to research the rationale of addiction. Examine the danger factors associated with Internet addiction.
Kim investigate the association of sleep quality and suicide attempt of Internet addicts. On the opposite hand, recent research in Psychology and Sociology reports variety of mental factors associated with social network mental disorders. Research indicates that children with narcissistic tendencies and shyness are particularly susceptible to addiction with OSNs. However, the above research explores various negative impacts and discusses potential reasons for Internet addiction. against this , this paper proposes to automatically identify SNMD patients at the first stage consistent with their OSN data with a completely unique tensor model that efficiently integrate heterogeneous data from different OSNs.
Change employ an NLP-based approach to gather and extract linguistic and content-based features from online social media to spot Borderline mental disorder and manic depression patients. Saha et al. extract the topical and linguistic features from online social media for depression patients to research their patterns.
Choudhury et al. analyze emotion and linguistic sorts of social media data for Major clinical depression (MDD). However, most previous research focuses on individual behaviors and their generated textual contents but don't carefully examine the structure of social networks and potential Psychological features.
Disadvantages
- There's no temporal behavior features to trace Mental Disorders.
- There are not any techniques for offline interaction.
PROPOSED SYSTEM
Within the proposed system, the system aims to explore data processing techniques to detect three sorts of SNMDs Cyber-Relationship (CR) Addiction, which incorporates the addiction to social networking, checking and messaging to the purpose where social relationships to virtual and online friends become more important than real-life ones with friends and families; 2) Net Compulsion (NC), which incorporates compulsive online social gaming or gambling, often leading to financial and job-related problems; and 3) Information Overload (IO), which incorporates addictive surfing of user status and news feeds, resulting in lower work productivity and fewer social interactions with families and friends offline.
Accordingly, the system formulates the detection of SNMD cases as a classification problem. We detect each sort of SNMDs with a binary SVM. during this study, the system proposes a two-phase framework, called Social Network mental disturbance Detection (SNMDD). the primary phase extracts various discriminative features of users, while the second phase presents a replacement SNMD-based tensor model to derive latent factors for training and use of classifiers built upon Transductive SVM (TSVM).
Two key challenges exist in design of SNMDD: i) we aren't ready to directly extract mental factors like what are done via questionnaires in Psychology and thus need new features for learning the classification models;4 ii) we aim to take advantage of user data logs from multiple OSNs and thus need new techniques for integrating multi-source data supported SNMD characteristics.
Advantages
- The system develops a machine learning framework to detect SNMDs, called Social Network mental disturbance Detection(SNMDD).
- Social diversity based features (SDiv) Researchers have observed that diversity improves the depth of individuals thinking for both majority or minority
