ArticlePloS one2019
Identifying long-term periodic cycles and memories of collective emotion in online social media.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Let's Talk About It in the Morning: How Circadian Rhythms Impact Information Sharing on Social Media.Affective science · 2024Article
- Unveiling the dynamics of emotions in society through an analysis of online social network conversations.Scientific reports · 2023Article
- Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis.International journal of environmental research and public health · 2022Article
- Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19.Scientific reports · 2022Article
- Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach.PloS one · 2022Article
- Article
- Neuromarketing as an Emotional Connection Tool Between Organizations and Audiences in Social Networks. A Theoretical Review.Frontiers in psychology · 2020Review
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Authors and funding
4 authors.
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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.
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