Anxious Depression Prediction in Real-time Social Data

Kumar, Akshi; Sharma, Aditi; Arora, Anshika

doi:10.2139/ssrn.3383359

Cited by 67 publications

(47 citation statements)

References 28 publications

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“…The potential of social media data analysis for reliable assessment of behavioral health disorders such as social media addiction, and symptoms of depression, anxiety, and stress has been studied recently. These approaches are based on statistical analysis (De Choudhury et al 2013a, 2013bSchwartz et al 2014) and more recently on machine learning techniques (Almeida et al 2017;Mowery et al 2017;Yazdavar et al 2017;Kumar et al 2019). Machine learning-based approaches are now being used widely in the domain of behavioral health analysis using social media data as they provide accurate results.…”

Section: Introductionmentioning

confidence: 99%

Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India

Arora

Chakraborty

Bhatia

et al. 2020

J. technol. behav. sci.

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The COVID-19 pandemic and the lockdowns to contain it are affecting the daily life of people around the world. People are now using digital technologies, including social media, more than ever before. The objectives of this study were to analyze the social media usage pattern of people during the COVID-19 imposed lockdown and to understand the effects of emotion on the same. We scraped messages posted on Twitter by users from India expressing their emotion or view on the pandemic during the first 40 days of the lockdown. We identified the users who posted frequently and analyzed their usage pattern and their overall emotion during the study period based on their tweets. It was observed that 222 users tweeted frequently during the study period. Out of them, 13.5% were found to be addicted to Twitter and posted 13.67 tweets daily on an average (SD: 4.89), while 3.2% were found to be highly addicted and posted 40.71 tweets daily on an average (SD: 9.90) during the study period. The overall emotion of 40.1% of the users was happiness throughout the study period. However, it was also observed that users who tweeted more frequently were typically angry, disgusted, or sad about the prevailing situation. We concluded that people with a negative sentiment are more susceptible to addictive use of social media.

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Section: Introductionmentioning

confidence: 99%

Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India

Arora

Chakraborty

Bhatia

et al. 2020

J. technol. behav. sci.

Self Cite

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show abstract

“…Further, the tensor-based systems can be improved with the help of DBN and CNNs for a better accuracy. Similar studies are done in [18][19][20], wherein methods like anxiety analysis, deep-learning and AI are proposed. From the review, we are able to identify that deep-learning and machine-learning techniques like DBN & CNN are most effective for depression analysis, but limited work has been done on fuzzy-rule-based systems.…”

Section: Literature Reviewmentioning

confidence: 83%

Improving Accuracy of Sentiment Analysis for Depression Recommendation using Multi-Domain Fuzzy Rules

Mamtora*,

Ragha

2020

IJITEE

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Social media & e-commerce has opened up the doors for human behavioral analysis in ways which were not possible before. Companies have the ability to track user’s mood and suggest advertisements which can trigger buying decisions based on it. This is possible due to user’s real time social media updates. Users nowadays are willing to provide information like their location, their age, nearby friends information, their mood, their buying patterns, etc. Companies do not intentionally collect all this information, but it has become a matter of social pride to post it as social media status and updates. The information available can be put to use in multiple forms- predict election results, movie success, product liking/disliking, travel destination recommendation, health care, etc. In our work, we utilize this textual information posted by different users and analyze their depression level focusing on negative sentiments. In order to perform this task, we have considered user’s tweets, any links which they might have posted, the time of the tweet, their age group and any previous depression history of the user. All these parameters are given to a novel fuzzy decision tree that uses sentiment analysis and game theory-based scoring in order to evaluate the depression score for the user. We analyzed the system on different real-time users, and observed that the system predicts depression level with more than 90% accuracy. Our work can be used to generate a prototype to identify if a person is in a depressive state and figure out the intensity of his/her depression.

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“…Reports have noted that “during quarantine, iPhone screen time reports are through the roof” 13 . Social media provides a means of self-expression and facilitates measurement of the psychological status of those sharing their feelings which are hard to articulate in traditional means 14 . The huge amount of people’s experience, opinion, and emotion on social media provides a great opportunity for automatic mining and analysis of psychological dynamics 15 .…”

Section: Introductionmentioning

confidence: 99%

The psychological effects of quarantine during COVID-19 outbreak: Sentiment analysis of social media data

Yuan

et al. 2020

Preprint

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We rely on social distancing measures such as quarantine and isolation to contain the COVID-19. However, the negative psychological effects of these measures are non-negligible. To supplement previous research on psychological effects after quarantine, this research will investigate the effects of quarantine amid COVID-19. We adopt a sentiment analysis approach to analyze the psychological state changes of 1,278 quarantined persons with 214,874 tweets over four weeks spanning the period before, during, and after quarantine. We formed a control group of 1,278 unquarantined persons with 250,198 tweets. The tweets of both groups are analyzed by matching with a lexicon to measure the anxious depression level changes over time. We discovered a clear pattern of psychological changes for quarantined persons. Anxious depression levels significantly increased as quarantine starts, but gradually diminished as it progresses. However, anxious depression levels resurged after a 14-day quarantine. It was found that quarantine has a negative impact on the mental health of quarantined and unquarantined people. Whilst quarantine is deemed necessary, proper interventions such as emotion management should be introduced to mitigate its adverse psychological impacts.

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Anxious Depression Prediction in Real-time Social Data

Cited by 67 publications

References 28 publications

Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India

Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India

Improving Accuracy of Sentiment Analysis for Depression Recommendation using Multi-Domain Fuzzy Rules

The psychological effects of quarantine during COVID-19 outbreak: Sentiment analysis of social media data

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