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

Yukie Sano; Hideki Takayasu; Shlomo Havlin; Misako Takayasu

doi:10.1371/journal.pone.0213843

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

PLoS One. 2019 Mar 21;14(3):e0213843. doi: 10.1371/journal.pone.0213843. eCollection 2019.

Authors

Yukie Sano¹, Hideki Takayasu^{2

3}, Shlomo Havlin⁴, Misako Takayasu³

Affiliations

¹ Department of Policy and Planning Sciences, University of Tsukuba, Ibaraki, Japan.
² Sony Computer Science Laboratories, Inc., Tokyo, Japan.
³ Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.
⁴ Department of Physics, Bar-Ilan University, Ramat-Gan, Israel.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Earthquakes
Emotions*
Humans
Memory, Long-Term*
Seasons
Social Media*
Surveys and Questionnaires

Grants and funding

This work was supported by JST and MOST, SICORP Japan-Israel Cooperative Scientific Research on ICT for a Resilient Society (YS, HT, SH, MT) and METI Development Project of New Indicators Utilizing Big Data and its Analysis Technology (YS, HT, MT). This work was partially supported by JSPS KAKENHI Grant Number 17K12783 (YS). There was no additional external funding received for this study. Sony Computer Science Laboratories, Inc. provided support in the form of salaries for author HT, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of HT is given in the ‘author contributions’ section.