IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0213843.html
   My bibliography  Save this article

Identifying long-term periodic cycles and memories of collective emotion in online social media

Author

Listed:
  • Yukie Sano
  • Hideki Takayasu
  • Shlomo Havlin
  • Misako Takayasu

Abstract

Collective emotion has been traditionally evaluated by questionnaire survey on a limited number of people. Recently, big data of written texts on the Internet has been available for analyzing collective emotion for very large scales. Although short-term reflection between collective emotion and real social phenomena has been widely studied, long-term dynamics of collective emotion has not been studied so far due to the lack of long persistent data sets. In this study, we extracted collective emotion over a 10-year period from 3.6 billion Japanese blog articles. Firstly, we find that collective emotion shows clear periodic cycles, i.e., weekly and seasonal behaviors, accompanied with pulses caused by natural disasters. For example, April is represented by high Tension, probably due to starting school in Japan. We also identified long-term memory in the collective emotion that is characterized by the power-law decay of the autocorrelation function over several months.

Suggested Citation

  • Yukie Sano & Hideki Takayasu & Shlomo Havlin & Misako Takayasu, 2019. "Identifying long-term periodic cycles and memories of collective emotion in online social media," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-17, March.
    • Handle: RePEc:plo:pone00:0213843
    DOI: 10.1371/journal.pone.0213843
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213843
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0213843&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0213843?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    2. Emilio Ferrara & Zeyao Yang, 2015. "Measuring Emotional Contagion in Social Media," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    3. Zheludev, Ilya & Smith, Robert & Aste, Tomaso, 2014. "When can social media lead financial markets?," LSE Research Online Documents on Economics 57376, London School of Economics and Political Science, LSE Library.
    4. Hong-Hee Won & Woojae Myung & Gil-Young Song & Won-Hee Lee & Jong-Won Kim & Bernard J Carroll & Doh Kwan Kim, 2013. "Predicting National Suicide Numbers with Social Media Data," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-6, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ortal Slobodin & Ilia Plochotnikov & Idan-Chaim Cohen & Aviad Elyashar & Odeya Cohen & Rami Puzis, 2022. "Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis," IJERPH, MDPI, vol. 19(11), pages 1-17, June.
    2. Qing Yang & Naeem Hayat & Abdullah Al Mamun & Zafir Khan Mohamed Makhbul & Noor Raihani Zainol, 2022. "Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-23, March.
    3. Rizwan Raheem Ahmed & Dalia Streimikiene & Zahid Ali Channar & Hassan Abbas Soomro & Justas Streimikis & Grigorios L. Kyriakopoulos, 2022. "The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    4. Jun Shimizu & Hiromi Kuwata & Kazuo Kuwata, 2021. "Differences in fractal patterns and characteristic periodicities between word salads and normal sentences: Interference of meaning and sound," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-13, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hyekyung Woo & Youngtae Cho & Eunyoung Shim & Kihwang Lee & Gilyoung Song, 2015. "Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood," IJERPH, MDPI, vol. 12(9), pages 1-10, September.
    2. Fan, Rui & Xu, Ke & Zhao, Jichang, 2018. "An agent-based model for emotion contagion and competition in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 245-259.
    3. Karunakaran, Arvind & Orlikowski, Wanda J. & Scott, Susan V., 2022. "Crowd-based accountability: examining how social media commentary reconfigures organizational accountability," LSE Research Online Documents on Economics 114401, London School of Economics and Political Science, LSE Library.
    4. Letchford, Adrian & Preis, Tobias & Moat, Helen Susannah, 2016. "The advantage of simple paper abstracts," Journal of Informetrics, Elsevier, vol. 10(1), pages 1-8.
    5. Hervé, Fabrice & Zouaoui, Mohamed & Belvaux, Bertrand, 2019. "Noise traders and smart money: Evidence from online searches," Economic Modelling, Elsevier, vol. 83(C), pages 141-149.
    6. Daniele Barchiesi & Helen Susannah Moat & Christian Alis & Steven Bishop & Tobias Preis, 2015. "Quantifying International Travel Flows Using Flickr," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-8, July.
    7. Andreas Spitz & Emőke-Ágnes Horvát, 2014. "Measuring Long-Term Impact Based on Network Centrality: Unraveling Cinematic Citations," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-12, October.
    8. Jeon, Hongjun & Seo, Wonchul & Park, Eunjeong & Choi, Sungchul, 2020. "Hybrid machine learning approach for popularity prediction of newly released contents of online video streaming services," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    9. Plé, Loïc & Demangeot, Catherine, 2020. "Social contagion of online and offline deviant behaviors and its value outcomes: The case of tourism ecosystems," Journal of Business Research, Elsevier, vol. 117(C), pages 886-896.
    10. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    11. Hainan Huang & Weifan Chen & Tian Xie & Yaoyao Wei & Ziqing Feng & Weijiong Wu, 2021. "The Impact of Individual Behaviors and Governmental Guidance Measures on Pandemic-Triggered Public Sentiment Based on System Dynamics and Cross-Validation," IJERPH, MDPI, vol. 18(8), pages 1-25, April.
    12. Shota Saito & Yoshito Hirata & Kazutoshi Sasahara & Hideyuki Suzuki, 2015. "Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    13. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    14. Yifan Yu & Shan Huang & Yuchen Liu & Yong Tan, 2020. "Emotions in Online Content Diffusion," Papers 2011.09003, arXiv.org, revised Mar 2022.
    15. Gonzalo Luna-Cortés & Luis Miguel López-Bonilla & Jesús Manuel López-Bonilla, 2019. "The influence of social value and self-congruity on interpersonal connections in virtual social networks by Gen-Y tourists," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-17, June.
    16. Lechner, Andreas T. & Paul, Michael, 2019. "Is this smile for real? The role of affect and thinking style in customer perceptions of frontline employee emotion authenticity," Journal of Business Research, Elsevier, vol. 94(C), pages 195-208.
    17. Souza, Thársis T.P. & Aste, Tomaso, 2019. "Predicting future stock market structure by combining social and financial network information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    18. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    19. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    20. Yara Kyrychenko & Tymofii Brik & Sander Linden & Jon Roozenbeek, 2024. "Social identity correlates of social media engagement before and after the 2022 Russian invasion of Ukraine," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0213843. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.