A Review on Stress Detection of users on Social Interactions (original) (raw)
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International Journal of Advance Research, Ideas and Innovations in Technology, 2020
Nowadays the Psychological stress is becoming a threat to people’s health, as the time is moving stress level is increasing so quickly. With the rapid pace of life, more and more people are feeling stressed. In the proposed method, we will find the stress state of society very closely related to their relatives or friends in social media and will give work to a large-scale dataset from the real-world social platforms to study the correlation of user's stress states and social interactions. As the peoples are sharing their day to day activities on social media platforms or the interaction between the peoples makes social media so popular in these past few years. The social media platform makes it convenient to hold all the online social network data for stress detection, though the stress itself is a non-clinical and common in our day to day lifestyle. Excessive and chronic stress can be rather harmful to people’s physical or mental health. With the development of social networks...
Stress Detection Methodology based on Social Media Network: A Proposed Design
International Journal of Innovative Technology and Exploring Engineering, 2020
Mental disorders can be recognized by how a person behaves, feels, perceives, or thinks over a period of a lifetime. Nowadays, a large number of people are feeling stressed with the rapid pace of life. Stress and depression may lead to mental disorders. Work pressure, working environment, people we interact, schedule of the day, food habits, etc. are some of the major reasons behind building stress among the people. Thus, stress can be detected through some conventional medical symptoms such as headache, rapid heartbeats, feeling low energy, chest pain, frequent colds, infections, etc. The stress also may reflect in normal behavior while carrying out day-to-day activities. Individuals may share their day-to-day activities and interact with friends on social media. Thus, it may be possible to detect stress through social network data. There are many ways to detect stress levels. Some of the instruments are used to detect stress while there is a medical test to know the stress level. ...
Discovering Stress Based on Social Interaction on Social Networking Sites
Stress is essentially humans' response to various types of desires or threats. This response, when working properly, can help us to stay focused, energized and intellectually active, but if it is out of proportion, it can certainly be harmful leading to depression, anxiety, hypertension and a host of threatening disorders. Cyberspace is a huge soap box for people to post anything and everything that they experience in their day-to-day lives. Subsequently, it can be used as a very effective tool in determining the stress levels of an individual based on the posts and status updates shared by him/her. This is a proposal for a website which takes the Twitter username of the subject as an input, scans and analyses the subject's profile by performing Sentiment Analysis and gives out results. These results suggest the overall stress levels of the subject and give an overview of his/her mental and emotional state. The tool used for analysis of the social media account is Rapidminer. Rapid miner is an environment for various data mining and machine learning procedures with a very effective and simple GUI.
Detecting Stress Based on Social Networking Interactions
International journal of innovative technology and exploring engineering, 2019
Stress is a kind of demand to respond to any in your body's manner. It can be based on experiences that are both good and bad. Psychological stress threatens the health of individuals. People are used to exchanging their schedule and daily operations with colleagues on social media platforms with the reputation of a social media network, creating it possible to hold online social network information for stress detection. For a variety of applications data mining methods are used. Data mining plays a significant role in the detection of stress in sector. We proposed a new model in this article to detect stress. Initially, in this model, discover a correlation between stress states of user and effective public interactions. This describes a set of textual, visual and social characteristics related to stress from different elements and proposes a new hybrid model coupled with Convolutional Neural Network (CNN) to efficiently hold tweet content and data on social interaction to detect stress. The suggested model can enhance the detection efficiency by 97.8 percent, which is quicker than the current scheme, from the experimental outcomes.
IJERT-Detecting Stress Based on Social Interactions in Social Network
International Journal of Engineering Research and Technology (IJERT), 2018
https://www.ijert.org/detecting-stress-based-on-social-interactions-in-social-network https://www.ijert.org/research/detecting-stress-based-on-social-interactions-in-social-network-IJERTCONV6IS08019.pdf Psychological stress is threatening people's health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to share their daily activities and interact with friends on social media platforms, making it feasible to leverage online social network data for stress detection. We find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users' stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then proposed a plot .Experimental results show that the proposed model can improve the detection performance .With the help of enumeration we build a website for the users to identify their stress rate level and can check other related activities.
Stress Detection of User using Social Interaction
2019
Mental stress is becoming a threat to people's health now a days. With the rapid pace of life, more and more people are feeling stressed. It is not easy to detect users stress in an early time to protect user [1]. We determined that students stress state is firmly diagnosed with that of his/her activities in on-line lifestyles. We initially load the data from dataset named as "Sentiment_140" from Kaggle and visualize properties from different viewpoints and afterward propose a Naïve Bayes algorithm-It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors i.e. presence of a particular feature in a class is unrelated to the presence of any other feature.
IJERT-An Efficient Identification of Mental Stress by Utilizing Social Network
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/an-efficient-identification-of-mental-stress-by-utilizing-social-network https://www.ijert.org/research/an-efficient-identification-of-mental-stress-by-utilizing-social-network-IJERTCONV8IS08006.pdf The growth of social network leads to more advantages as well as problematic issues. Now days the symptoms and result of this metal stress was analyzed passively and delayed clinical intervention. To overcome this issue, an early stage of identifying online social behavior through analysis in efficient way is proposed. It is challenging to detect SNMSs because the mental status cannot be directly observed from online social activity logs. Through social networks n number of users is communicating in the form of text can't be analyzed manually to identify the stage of particular user. Manual way of analyzing user chat history will leads to privacy issues therefore this issue has been overcome through Machine Learning (ML). In ML, Natural language processing (NLP) is implemented that analysis the text commented and understand the meaning of the comment. By this analysis a system will identify whether the person is in normal, good or bad (depression) situation without affecting their privacy and confidentiality of data. Therefore, our system will read each word in a command and analysis its meaning and monitor particular person for a specific period and identify whether the person is in good or in depression situation. The person situation is not good then intimation will be send to respective person's relative mail hence this leads to saving a person life from unexpected event occurrence.
A STUDY OF STRESS CAUSED BY SOCIAL INTERACTIONS IN SOCIAL NETWORKS
Today social networks become a second world for the people. The youth population in the world are living in both virtual and real world with same emotions and feelings. The cause of sudden destruction in social medial interactions affects people's real life. It creates psychological damage and gives stress which affects day to day life. High-level stress results sudden changes in life style, hyper tension and other health complications too. Some of the lethal incidents are also caused due to stress caused by social networks. There is a standard stress monitoring tool in social networks rather it can be measured by monitoring timely posts by social media users. The proposed work is aimed to monitor stress level of social media workers in a group.
A Comparative Analysis of Psychological Stress Detection Methods
Psychological Stress and Depression have been pinpointed repeatedly as significant issues contributing to the weakening of physical and mental health. Nowadays stress is considered as the biggest threat to individual's wellbeing. However stress can be a positive aspect in our daily life, but too much stress can rather be harmful to physical and emotional healthiness where as managing it, is a major concern for populations around the world. Hence, there is significant importance to detect stress in its early stages, before it turns into severe problem. Thus, this work analyses and brings together recent research studies carried for automatic stress detection observing over the dimensions executed along the four main modalities, viz., Psychological, Physiological, Behavioral and Social Media Interaction modalities, along with appropriate measurements, in order to give hints about the most appropriate ways and means to be used for Psychological Stress Detection.
Detecting Stress Based on Social Interactions in Social Networks
IEEE Transactions on Knowledge and Data Engineering, 2017
Psychological stress is threatening people's health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users' stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model-a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9% in F1-score. By further analyzing the social interaction data, we also discover several intriguing phenomena, i.e. the number of social structures of sparse connections (i.e. with no delta connections) of stressed users is around 14% higher than that of non-stressed users, indicating that the social structure of stressed users' friends tend to be less connected and less complicated than that of non-stressed users.