CEGON TECHNOLOGIES CEGON TECHNOLOGIES ( We Rise By Lifting Others) Detecting Stress Based on Social Interactions in Social Networks (original) (raw)
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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.
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.
Stress Based Detection on Social Interactions in Social Networks
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
Psychological stress is ominous person’s health. It is non-trivial to detect stress timely for proactive care. With the attractive of social media, person are used to sharing their daily task and communicating 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 condition 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 examine the connection of users’ stress condition’s and social interactions. We first define a set of stress-related textual analysis, 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 better the detection performance by 6-9% in F1-score. By further analysing 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% more 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 us.
RECOGNIZING STRESS BASED ON SOCIAL INTERACTIONS IN SOCIAL NETWORKS
Mental pressure is compromising individuals' wellbeing. It is non-unimportant to recognize pressure opportune for proactive consideration. With the prominence of online life, individuals are accustomed to sharing their every day exercises and communicating with companions via web-based networking media stages, making it plausible to use online interpersonal organization information for stress recognition. In this paper, we find that clients stress state is intently identified with that of his/her companions in web based life, and we utilize an enormous scale dataset from genuine social stages to efficiently study the relationship of clients' pressure states and social communications. We initially characterize a lot of pressure related printed, visual, and social properties from different perspectives, and after that propose a novel cross breed model -a factor chart model joined with Convolutional Neural System to use tweet substance and social connection data for stress discovery. Test results demonstrate that the proposed model can improve the discovery execution by 6-9% in F1-score. By further examining the social cooperation information, we likewise find a few interesting wonders, for example the quantity of social structures of meager associations (for example with no delta associations) of pushed clients is around 14% higher than that of non-focused on clients, demonstrating that the social structure of focused on clients' companions will in general be less associated and less entangled than that of non-focused on clients.
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.
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.
What Does Social Media Say about Your Stress?
2016
With the rise of social media such as Twitter, people are more willing to convey their stressful life events via these platforms. In a sense, it is feasible to detect stress from social media data for proactive health care. In psychology, stress is composed of stressor and stress level, where stressor further comprises of stressor event and subject. By far, little attention has been paid to estimate exact stressor and stress level from social media data, due to the following challenges: 1) stressor subject identification, 2) stressor event detection, and 3) data collection and representation. To address these problems, we devise a comprehensive scheme to measure a user's stress level from his/her social media data. In particular, we first build a benchmark dataset and extract a rich set of stress-oriented features. We then propose a novel hybrid multi-task model to detect the stressor event and subject, which is capable of modeling the relatedness among stressor events as well a...
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...
User-level psychological stress detection from social media using deep neural network
Proceedings of the 22nd ACM international conference on Multimedia, 2014
It is of significant importance to detect and manage stress before it turns into severe problems. However, existing stress detection methods usually rely on psychological scales or physiological devices, making the detection complicated and costly. In this paper, we explore to automatically detect individuals' psychological stress via social media. Employing real online micro-blog data, we first investigate the correlations between users' stress and their tweeting content, social engagement and behavior patterns. Then we define two types of stress-related attributes: 1) low-level content attributes from a single tweet, including text, images and social interactions; 2) user-scope statistical attributes through their weekly micro-blog postings, leveraging information of tweeting time, tweeting types and linguistic styles. To combine content attributes with statistical attributes, we further design a convolutional neural network (CNN) with cross autoencoders to generate user-scope content attributes from low-level content attributes. Finally, we propose a deep neural network (DNN) model to incorporate the two types of userscope attributes to detect users' psychological stress. We test the trained model on four different datasets from major micro-blog platforms including Sina Weibo, Tencent Weibo and Twitter. Experimental results show that the proposed model is effective and efficient on detecting psychological stress from micro-blog data. We believe our model would be useful in developing stress detection tools for mental health agencies and individuals.
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.