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Emotions, Everyday Life, and the Social Web: Age, Gender, and Social Web Engagement Effects on Online Emotional Expression

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  • Roser Beneito-Montagut

Abstract

Emotional expression is key to the maintenance and development of interpersonal relationships (IR) online. This study develops and applies a novel analytical framework for the study of emotional expression on the social web in everyday life. The analytical framework proposed is based on previous ethnographic work and the self-reported measurement of the visual cues, action cues, and verbal cues that people use to express emotions on the social web. It is empirically tested, using an online survey of Spanish frequent Internet users (n = 301). The analysis focuses particularly on how age, gender, and social web engagement relate to emotional expression during online social interactions. We find that both personal characteristics (age and gender) and levels of social web usage affect emotional communication online. The effect size is particularly strong for gender. This article illustrates and reflects upon the potential of the proposed analytical framework for unveiling norms and strategies in online interaction rituals.

Suggested Citation

  • Roser Beneito-Montagut, 2017. "Emotions, Everyday Life, and the Social Web: Age, Gender, and Social Web Engagement Effects on Online Emotional Expression," Sociological Research Online, , vol. 22(4), pages 87-104, December.
  • Handle: RePEc:sae:socres:v:22:y:2017:i:4:p:87-104
    DOI: 10.1177/1360780417732955
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    References listed on IDEAS

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    1. Sonia Livingstone & Ellen Helsper, 2010. "Balancing opportunities and risks in teenagers' use of the internet: the role of online skills and internet self-efficacy," LSE Research Online Documents on Economics 35373, London School of Economics and Political Science, LSE Library.
    2. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    3. Mike Thelwall & David Wilkinson & Sukhvinder Uppal, 2010. "Data mining emotion in social network communication: Gender differences in MySpace," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(1), pages 190-199, January.
    4. Sendhil Mullainathan & Marianne Bertrand, 2001. "Do People Mean What They Say? Implications for Subjective Survey Data," American Economic Review, American Economic Association, vol. 91(2), pages 67-72, May.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    6. Mike Thelwall & David Wilkinson & Sukhvinder Uppal, 2010. "Data mining emotion in social network communication: Gender differences in MySpace," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(1), pages 190-199, January.
    7. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
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