Technologies using artificial intelligence (AI) have been implemented as services to solve various social problems. However, the contributions of AI to people's mentality and unknown/unobserved events have not been extensively discussed. In this study, we focus on people's mental changes caused by the COVID-19 pandemic and discuss the origin of data sources for well-being using marketing research online communities (MROCs) and variable quest (VQ). In the experiment, we selected 15 females aged between 20 and 40 who were interested in exploring how daily life has changed since the emergence of COVID-19 using MROCs. The analysis results by VQ revealed that the variable sets of the events differed with the situations, mental states, and attitudes, while not being featured in any of the MROC topics as keywords. The result suggests that abstracting the features of unobserved events as variable sets, can help us acquire information potentially contributing to unexplored data discovery for human well-being from texts not containing any information related to the data.
tions for issues that manifest in the physical world. However, AI’s contribution to people’s mentality and unknown/unobserved events has not been sufficiently investigated. Various industries worldwide have been severely affected by the coronavirus since the end of 2019, revealing gaps between social systems and enforcing major transformations in our lives. Data consisting of accumulated records of past events may not be effective in calculating infection risk or preventing wider infections. Moreover, not much is understood regarding the emotional issues that accompany changes in daily life.
This research considers accomplishing well-being data
origination of unexplored data. Data origination is the act of
data design/acquisition/utilization that reflects the
subjective knowledge and diversity of perspectives of humans and
aims to elucidate as well as support this process
MROCs are research methods for extracting consumer
insights by building a closed online community of people who
are knowledgeable about or interested in specific themes
The advantage of using VQ in this study is that the sets of variables relevant to the event in question can be accessed from data-related collective intelligence by providing a text outline of an event that has not yet been noticed, or observed, even when keywords related to variables are not included.
We selected 15 females aged between 20 and 40, who were interested in exploring changes in daily life since the emergence of COVID-19 and received 314 comments regarding seven topics with MROCs. To understand how families, friends, and workplaces have changed, we chose the comments of the three topics as follows: ・ Topic 2: Feelings about lifestyle changes due to the
COVID-19 pandemic (66 comments) ・ Topic 5: How cohabiting with relatives, colleagues, and
friends has changed (30 comments) ・ Topic 6: How feelings have changed after spending half a year living with COVID-19 (25 comments) ・While most companies allowed working from home, it was scary to go into office, “Attitudes about health,” “work-related attiand I felt different from everyone else. […] I was haunted by this constant anxiety, tude,” “attitudes about money,” “occupaso even on my days off, I couldn’t get much emotional respite. tion,” “number of steps,” “age,” “number of ・I avoided behaviors that seemed to harm my physical condition […] and became cases,” “number of deaths,” “number of suisensitive to changes in my body. cides,” “prefecture,” … ・Even if you’re asking about something small, putting it into writing in an email or “Annual salary,” “sales,” “labor hours,” “afchat takes a surprisingly long time. filiation,” “email address,” “email sender,” Topic ・More contact via email and phone, and more pressure to respond from family, “occupation,” “number of steps,” “age,” 5 friends, colleagues—everyone in general. Maybe it’s feelings of unease, going out “number of cases,” “number of deaths,” less, and spending more time at home. “number of suicides,” “prefecture,” … ・Even in my daily life, I’m thinking about the future, a career change, studying; it “Occupation type,” “author name,” “knowfeels like everything is looking toward the future. ing about an event in advance,” “purchase To6pic ・Ibuutsseidnctoe cwheo’ovseebweghuenrelitvoinggowbaitshetdhleocoosreolynaovnirhuys,pwedh-eunpevpelarcIenseaenddtoporupnulaanr eervreanntds,, hhiesatlothry,”,” “w“obrkir-trhedlaatye,d” att“iatuttditeu,d”es“attiatubdoeust I actively go to places with fewer people. about money,” …
COVID-19 pandemic; in particular, many participants referred to changes in their physical condition, daily habits, and the shift to remote working (Table 1). This suggests that it is important to capture these variables when observing how work habits have changed, including in remote working. Moreover, a variable group was considered important for grasping changes in attitudes toward work and lifestyles. For example, “attitudes about health,” “work-related attitudes,” and “attitudes about money” were found to be a particularly important variable for grasping and understanding the state of mental health. Furthermore, “occupation,” “number of steps,” and “age” can provide an idea of the context of insufficient exercise as a result of remote working. Next, “number of cases,” “number of deaths,” “number of suicides,” and “prefecture” form a group of variables that reflect news about the alleged rise in suicide and daily infection rates during the pandemic. These variables provide more quantifiable data than the other variables.
Topic 5 deals with variables about changes in participants’ personal circles and shared the same variables as remote working and news in Topic 2. Compared with Topic 2, the variables for Topic 5 did not concern psychological issues, instead centering on events occurring in the physical world, such as “annual salary,” “sales,” and “labor hours”.
Topic 6 discussed the changes since the first declaration of emergency in Japan in April 2020. There were still many comments regarding the discomfort regarding work and daily life; however, contrary to Topic 2, there were many positive statements about accepting societal changes, beginning new activities, and gradually adjusting feelings. Accordingly, such variables appeared as “occupation type,” owing to career changes, “author name,” “knowing about an event in advance,” and “purchase history” from new hobbies and activities.
tracted as unexplored data differed among the three topics. Participants often made comments in Topics 2 and 6 that centered on themselves, thereby reflecting changes in their mental states. Therefore, such topics can be characterized by having many variables that originate from psychological changes, including attitudes. Meanwhile, for Topic 5, participants often mentioned changes in other people; consequently, easily quantifiable variables such as “annual salary,” “sales,” and “labor hours” appeared more than variables stemming from mental states.
Moreover, the number of variables obtained by entering the comments for each topic into the VQ were 24, 25, and 27, respectively. However, the number of cases for all three topics in which these variables were included as words in the set of comments collected by the MROCs was zero. In other words, the keywords representing the variables were not included in any of the words spoken by the subjects. These variables are from past data such as the “Sense of Crisis Database,” “Suicide Statistics Data,” or “Event and Shopping Mall Sales Data” and are thus difficult to apply directly on ongoing events such as the COVID-19 pandemic. Nonetheless, decomposing the past data in variable units and reconstructing them using VQ could contribute to unexplored data discovery, as a set of variables from texts not containing any information related to the data.
This study was supported by NEC Corporation. We would like to thank PLUG-Inc. in the survey design of MROCs.