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Data mining emotion in social network communication: Gender differences in MySpace

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  • Mike Thelwall
  • David Wilkinson
  • Sukhvinder Uppal

Abstract

Despite the rapid growth in social network sites and in data mining for emotion (sentiment analysis), little research has tied the two together, and none has had social science goals. This article examines the extent to which emotion is present in MySpace comments, using a combination of data mining and content analysis, and exploring age and gender. A random sample of 819 public comments to or from U.S. users was manually classified for strength of positive and negative emotion. Two thirds of the comments expressed positive emotion, but a minority (20%) contained negative emotion, confirming that MySpace is an extraordinarily emotion‐rich environment. Females are likely to give and receive more positive comments than are males, but there is no difference for negative comments. It is thus possible that females are more successful social network site users partly because of their greater ability to textually harness positive affect.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jamist:v:61:y:2010:i:1:p:190-199
    DOI: 10.1002/asi.21180
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    Cited by:

    1. Chen, Aihui & Lu, Yaobin & Wang, Bin & Zhao, Ling & Li, Ming, 2013. "What drives content creation behavior on SNSs? A commitment perspective," Journal of Business Research, Elsevier, vol. 66(12), pages 2529-2535.
    2. Ibtesam AbdulAziz Bajri & Nada Abdulmajeed Lashkar, 2020. "Saudi Gender Emotional Expressions in Using Instagram," English Language Teaching, Canadian Center of Science and Education, vol. 13(5), pages 1-94, May.
    3. F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
    4. Yulei Gavin Zhang & Mandy Yan Dang & Hsinchun Chen, 2020. "An Explorative Study on the Virtual World: Investigating the Avatar Gender and Avatar Age Differences in their Social Interactions for Help-Seeking," Information Systems Frontiers, Springer, vol. 22(4), pages 911-925, August.
    5. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 0. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 0, pages 1-19.
    6. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 2017. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 17(1), pages 101-119, March.
    7. Liwen Vaughan, 2016. "Uncovering information from social media hyperlinks: An investigation of twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1105-1120, May.
    8. Tian Tian & Stijn Speelman, 2021. "Pursuing Development behind Heterogeneous Ideologies: Review of Six Evolving Themes and Narratives of Rural Planning in China," Sustainability, MDPI, vol. 13(17), pages 1-16, September.
    9. Chmiel, Anna & Sobkowicz, Pawel & Sienkiewicz, Julian & Paltoglou, Georgios & Buckley, Kevan & Thelwall, Mike & Hołyst, Janusz A., 2011. "Negative emotions boost user activity at BBC forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2936-2944.
    10. Avi Rosenfeld & Sigal Sina & David Sarne & Or Avidov & Sarit Kraus, 2018. "WhatsApp usage patterns and prediction of demographic characteristics without access to message content," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 39(22), pages 647-670.
    11. 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.
    12. Jacqueline Ng Lane & Bruce Ankenman & Seyed Iravani, 2018. "Insight into Gender Differences in Higher Education: Evidence from Peer Reviews in an Introductory STEM Course," Service Science, INFORMS, vol. 10(4), pages 442-456, December.
    13. Li, Xianghua & Wang, Zhen & Gao, Chao & Shi, Lei, 2017. "Reasoning human emotional responses from large-scale social and public media," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 182-193.
    14. Anupriya Khan & Satish Krishnan & Jithesh Arayankalam, 2022. "The Role of ICT Laws and National Culture in Determining ICT Diffusion and Well-Being: A Cross-Country Examination," Information Systems Frontiers, Springer, vol. 24(2), pages 415-440, April.

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