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Social Media Trends and Prediction of Subjective Well-Being: A Literature Review

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Advances in Computer and Computational Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 553))

Abstract

Everybody is now addicted to the online social media. Social media sites have been used by millions of people globally. Each individual expresses his thoughts, daily life events, and opinions on social media. The individual’s expressions on social media are mostly in the text form. The text contains sentiments, opinions, attitudes and emotions of the individuals, which are largely related to the happiness in the personal life of individuals. Extensive usage of social media affects the happiness, which can be either on the positive or negative level. Happiness level is normally measured by self-report and often been indirectly characterized by more readily quantifiable economic indicators such as gross development product (GDP) or genuine progress indicator (GPI). However, the growing importance of linguistic text analysis of social media gives a direction to predict the happiness of individuals and is termed as subjective well-being (SWB). SWB is the scientific term used to describe happiness and quality of life of individuals. It includes emotional reactions and cognitive judgments and is of great use to public policy-makers as well as economic, sociological, and psychological research. The richness and availability of social media make it an ideal platform to conduct psychological research in the topic SWB. In this paper, at last, the evidence of the importance of the social media analytics has been provided followed by identification of major factors involved in SWB. Further, the effects of social media usage on the SWB of individuals have been elaborated.

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Notes

  1. 1.

    Social Media Definition, “Merriam-Webster”.

  2. 2.

    http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users.

  3. 3.

    https://www.als.net/icebucketchallenge/.

  4. 4.

    http://edition.cnn.com/2015/11/03/health/teens-tweens-media-screen-use-report/.

  5. 5.

    http://searchbusinessanalytics.techtarget.com/denition/social-media-analytics.

  6. 6.

    http://books.google.com/googlebooks/library/index.html.

  7. 7.

    https://www.google.co.in/trends/.

  8. 8.

    http://web.stanford.edu/dept/psychology/bandura/pajares/Bandura2004Me-dia.pdf.

  9. 9.

    10.1037/0003-066X.36.4.343.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Guru Nanak Dev University, Regional Campus, Jalandhar, 144007, Punjab, India

    Simarpreet Singh & Pankaj Deep Kaur

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  1. Simarpreet Singh
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  2. Pankaj Deep Kaur
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Correspondence to Simarpreet Singh .

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Editors and Affiliations

  1. Department of Computer Science, University of Missouri, Columbia, Missouri, USA

    Sanjiv K. Bhatia

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  4. Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Vivek Kumar Singh

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Singh, S., Kaur, P.D. (2017). Social Media Trends and Prediction of Subjective Well-Being: A Literature Review. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_70

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  • DOI: https://doi.org/10.1007/978-981-10-3770-2_70

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