Social Seismography is a new emerging discipline of social science research, pioneered by OPPi (www.oppi.live), that uses innovative social listening tools to help leaders and citizens to gain a deeper understanding of fault-lines and glue-lines of social issues in our society. By gaining a deeper understanding of the underlying root causes, fault-lines or undercurrents that have been festering invisibly for a long time, we are able to design more effective policy, cultural and civic solutions to address these issues sustainably over the long-term before it is too late.
Within the discipline of social seismography lies the use of emergent surveys or wiki-surveys. Wiki or emergent surveys are different from traditional surveys in that they co-create the survey questions together with participants. Respondents to an OPPi conversation have a say in shaping the trajectory of the discourse, diagnosis or survey because survey design is both top-down and bottom-up.
By combining the scale and reach of a survey with the open-ended discovery of a focus group discussion, insights often surprise organisers as they challenge their preconceived ideas or cognitive biases about a particular issue. Through this unique discipline of social seismography and emergent surveys, OPPi helps a complex system to become more self-aware.
How does AI enable structured citizen-crowdsourcing at scale?
OPPi is an AI-powered engagement tool that leverages the power of emergent or wiki-surveys to help leaders in gathering the pulse of the people and facilitating high quality decision-making for complex societal issues. As part of OPPi’s corporate social responsibility, OPPi also helps to bring the voices of the people and marginalized communities to decision-making tables in the public sector, parliament and private sector globally.
OPPi combines quantitative and qualitative methods with advanced statistical techniques to identify opinion tribes based on respondents’ views and visualise correlations between opinions and respondents. OPPi learns patterns from respondents in real-time to help leaders identify fault lines and common ground.
How does an OPPi conversation work?
Participants are given a psychological safe virtual space to answer a series of “seed statements” posed by the OPPi web platform. These “seed statements” were determined through SGMHM’s PC2020, which involves a series of surveys and discussions with Singaporeans on access, affordability and quality of mental healthcare in Singapore. PC2020 was conducted by members of the mental health community and was developed in line with the philosophy of community-based participatory research (CBPR). The votes or responses of the participants are kept confidential. No one is able to trace back any individual response to any of the participants.
A moderator selectively enables comments submitted by participants as “crowdsourced statements” which circle back into the conversation for other participants to vote on. At the end of the conversation, participants are shown a real-time summary of the results of the entire conversation and which opinion group participants fall under. This has 3 benefits. First, it helps individuals in an organization to cultivate self-awareness and collective awareness. Second, it shifts the ownership or burden of the issue from the organisers to the community i.e. leaders and community respondents. Third, participants start to contribute more meaningful comments and statements to build common ground with their fellow peers. OPPi has proven to “gamify” consensus building and common ground for complex conversations.
This is in sharp contrast to traditional platforms for discourse which amplify echo-chambers, silos and divisions. Often, the loudest and most provocative voices win. OPPi, on the contrary, preserves minority opinions while bringing to light the views of the silent majority. In doing so, OPPi actually levels the playing field for the loud minority and the silent majority. Overall, the SGMHM reviewed the comments from participants regularly, and added an additional seed statement on suicide prevention mid-way through the poll, in response to new insight generated by participants. Most other comments provided and crowdsourced among participants were aligned with existing topics and questions covered by the poll.
Variables and Statements
We collected demographic information from all participants; namely age (Categories of <24, 25-34, 35-44, 45-54, and 55+), gender (Male, Female, Others), sexual orientation (Heterosexual, Non-Heterosexual), residence status (Singapore Citizen, Singapore PR, Non-Singapore Resident), occupation (Unemployed, Self-Employed, Professionals, Admin/Clerical, Blue Collar, Unclassified), income levels (No income, <SGD2999, SGD3000-SGD4999, SGD5000-SGD8999, SGD9000+), religion (Has a religion, Has no religion), mental health experience (Never had any mental health challenges, Ever had but never sought professional help, Ever had and sought professional help).
The questions on the OPPi poll covered topics on perceptions of mental healthcare, vulnerable communities, COVID-19 on mental health, and mental health and society. The statements were developed through discussions among members of SGMHM, and were informed by SGMHM public consultation 2020 (PC2020), which provided deeper insight into issues of access, affordability, and quality of mental healthcare in Singapore.
#AreWeOkay OPPi Poll Statements
* Statement 22 was added in at a later stage of the poll, and thus had fewer responses
We adopted both OPPi’s proprietary analytic capabilities, as well as separate statistical analyses to explore the nuances in our data.
The OPPI algorithm analyses all votes and determines the landscape of opinions according to the voting patterns of the poll participants. Two features stand out for quick analysis. Firstly, OPPi's decision-matrix (or So-What Chart) allows leaders to instantly analyse the noise in the data to distil actionable insights and signals. The real-time statements dashboard allows leaders to understand why people tend to sway in a particular direction when they “agree” or “disagree” with a particular statement. Secondly, at a deeper level, using Artificial Intelligence, OPPI clusters and discovers the various tribes based on how similar or different the respondents vote. Statements that uniquely express the sentiments of different tribes, personas or archetypes are identified by the algorithm. These OPPi opinion clusters simplify a leader's understanding of the “lay of the land” or the tribes that people fall under so that targeted communications, engagement or policy decisions can be formulated for different tribes
Additionally, we adopted descriptive statistics to delineate patterns in the data, as well as bivariable (chi-square tests) and multivariable (poisson regression with robust sandwich variances) statistical techniques to elucidate associations between variables. Data were organized using STATA and SPSS. Statistical significance was set at p<0.05 for statistical analyses.