Detecting Stress Based on Social Interactions in Social Networks

Lin, Huijie; Jia, Jia; Qiu, Jiezhong; Zhang, Yongfeng; Shen, Guangyao; Xie, Lexing; Tang, Jie; Feng, Ling; Chua, Tat-Seng

doi:10.1109/tkde.2017.2686382

Cited by 131 publications

(127 citation statements)

References 37 publications

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“…Huijie Lin et.al. [1] proposes stress detection model to detect social media user's stress levels by improving the performance by 6-9 percent in F1 score. They highlighted that social structure of stressed users is around 14% higher than that of non-stressed users.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Stress Level in Social Media users using Cnn-Fg Model

Nargis*,

Saurabh

2019

IJRTE

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Psychological stress has become a common condition in today's world owing to the busy life style and competitive environment. This has led to increase of suicidal rates in the recent years. Lately, there has been a tremendous increase in interactions in the social networking sites. As people are spending long hours in the virtual world it is easier to detect and analyze the stress levels of the social media users. In this paper, we have proposed a hybrid approach which is a combination of Factor Graph (FG) model and Convolutional Neural Network (CNN) to analyze the textual contents in social media users' tweets and posts to detect the level of stress of a user. The tweets of an individual user are gathered from Twitter platform which is preprocessed and passed to the cross autoencoder embedded CNN Model which outputs user level attributes. These are then input to the Factor Graph model that detects the stressed tweets. A mechanism has been proposed to inform the friends or relatives of the concerned stressed user if the detected stress level is above the given threshold.

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Section: Related Workmentioning

confidence: 99%

“…With the rapid growth of social media, expressing views, emotions and feelings in the virtual world is an ongoing trend. As people are spending long hours in the virtual world it is easier to detect and analyze the stress levels of the social media users [1]. In social media, the new words created are widely spread.…”

Section: Introductionmentioning

confidence: 99%

Detection of Stress Level in Social Media users using Cnn-Fg Model

Nargis*,

Saurabh

2019

IJRTE

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“…The hybrid system presented in [18] combines a factor graph model and convolutional neural network (CNN) and estimates the relationship between users' psychological stress levels and their social network interactions. This method improves stress detection by 6-9% in terms of F1-score and was used to investigate the social interactions of stressed and non-stressed users.…”

Section: Identification Of Stress and Relaxation From Social Media Comentioning

confidence: 99%

Detection of Stress and Relaxation Magnitudes for Tweets

Pillai

Thelwall

Orăsan

2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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The ability to automatically detect human stress and relaxation is crucial for timely diagnosing stress-related diseases, ensuring customer satisfaction in services and managing human-centric applications such as traffic management. Traditional methods employ stress-measuring scales or physiological monitoring which may be intrusive and inconvenient. Instead, the ubiquitous nature of the social media can be leveraged to identify stress and relaxation, since many people habitually share their recent life experiences through social networking sites. This paper introduces an improved method to detect expressions of stress and relaxation in social media content. It uses word sense disambiguation by word sense vectors to improve the performance of the first and only lexicon-based stress/relaxation detection algorithm TensiStrength. Experimental results show that incorporating word sense disambiguation substantially improves the performance of the original TensiStrength. It performs better than state-of-the-art machine learning methods too in terms of Pearson correlation and percentage of exact matches. We also propose a novel framework for identifying the causal agents of stress and relaxation in tweets as future work. CCS CONCEPTS•

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“…DCASE 2013 is live office dataset and ITC-IRST datasets are used. Huijie Lin et al, [3] experimented stress detection based on the data sets from the real world social platform through the messages from and to the friends of the user. Based on this proposed a combination of factor graph model and a novel hybrid model with Convolution neural network to leverage the tweet content.…”

Section: Related Workmentioning

confidence: 99%

Secure Decision-Making Approach to Improve Knowledge Management Based on Online Samples

Kaliyan¹,

Kothandaraman²

2018

IJIES

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Data is bombarding in every area of the industry and information gathered from the available data is the raw material for the online society. By the evolution of the telecom sector and various user-preferred applications, the internet has become inevitable. It, one of the huge technologies that are going to dominate the internet society, is the much expected technological evolution of this decade. Since everything is made online, all the activity happening & happened should be stored/retrieved in an efficient way. In this paper, we propose a decision process that has decision-making process and game theory along with a coding technique to acquire confident knowledge from the available data set in a secure way. Our mechanism is simulated and the results show a considerable improvement in the decision-making process. Also, analysis proves our algorithm is better in terms of quality parameters than the existing algorithms.

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Detecting Stress Based on Social Interactions in Social Networks

Cited by 131 publications

References 37 publications

Detection of Stress Level in Social Media users using Cnn-Fg Model

Detection of Stress Level in Social Media users using Cnn-Fg Model

Detection of Stress and Relaxation Magnitudes for Tweets

Secure Decision-Making Approach to Improve Knowledge Management Based on Online Samples

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