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Seeking common ground among the divided

OPPi is a new way of crowdsourcing opinions and ideas, finding out what others think and creating consensus about issues we care about, both quantitatively and qualitatively. OPPi aims to find common ground where people seem to be divided. We locate shared values, not what divides us. Through deep tech, OPPi aims to sieve out these commonalities among people.

Surveys and focus groups are traditionally used to sieve out people’s opinions on a particular issue or topic. These tools require physical interactions and questions have to be drawn up by investigators. This often shapes and guides the participants’ views. With this traditional methodology, there is an element of bias and the solutions often reside within the survey creators’ space.

Increasingly, social media listening is also becoming one of the potential market research tools. However, it is plagued by potential foreign agents and fake comments that the loudest and most negative voices may dominate the forum.

OPPi offers a differentiated approach where answers and solutions are co-created dynamically with participants. Our tool instantly clusters people according to how similar they think, while preserving minority opinions. Through consensus-building on issues, both participants and survey organisers are able to achieve self-awareness, and they often realise that they have much more in common than they think.

Case study: migrant workers

In April, Singapore made international headlines on its high number of migrant workers that contracted COVID-19 due to their crowded living conditions. The undesirable living conditions of migrant workers drew heavy criticism not just within Singapore, but also from a wider international audience. There were many different views raised by members of the public, NGOs and the government on how to tackle the problem. Human rights groups blamed the government for not doing enough to take care of migrant workers. and employers were complaining that they have already provided the necessary welfare for the workers based on existing budgets and costs. With so many conflicting voices, we thought that this would be theperfect opportunity to use OPPi to examine people’s thoughts on this issue.

Numbers at a glance

How the survey was formulated

What we did

Before we started this poll, there were a lot of negative sentiments posted on various online sources like social media platforms and opinion articles. The social media space was overly negative in portraying the government in a bad light. Given the escalating COVID-19 situation and the discussion that was happening on social media, our initial impression was that the general public fully blamed the government for not having the foresight to take care of migrant workers’ welfare. In our seed statements, we included a few statements to reflect that view and assumed that it would be the most commonly agreed statement.

Participants’ responses to the statements (the numbers indicate the different statements)


We posed the question

How might we encourage the public to embrace migrant workers’ needs in Singapore during and after the pandemic?

The purpose of this question was to increase the awareness of the issues faced by migrant workers and identify areas that can be improved for the benefit of migrant workers. Through the initial set of 11 seed opinion statements, there were 15 other statements that were suggested by participants that were subsequently added into the voting pool for other participants to vote on. 41 others proceeded to comment on why they agree or disagree with the statements and provided further suggestions.

Demographic of participants

In order to ensure that we had a basic understanding of our participants, standard demographic questions were asked to ensure representation.

Demographic representation of participants

The unexpected outcome

The statement that saw the most divide among participants

At the start of the survey, based on what we saw on social media, hordes of netizens were calling for the government to take full responsibility to take care of migrant workers. There were many articles from the media that highlighted blind-spots that were overlooked by the government. Even though this study only consisted of 100 participants, from the results, we were surprised that people were divided on whether the government should take full responsibility for the migrant workers. Contrasting it to the statement below:

Employers should take responsibility in ensuring that the migrant workers’ needs are taken care of

This statement garnered the most support. The results yielded reflect a need for shared responsibility between the government and employers. This comment below shared and supported by the participants provides further evidence that participants hope to see society collectively bearing the responsibility to resolve this problem.

I don’t think one particular organisation should be responsible for the migrant workers. The government, companies and community have to do it together.

This is where OPPi provides a space for minority opinions where even the smallest and most unpopular of views are able to be captured. Such a design enhances the quality of the survey in comparison to a set of pre-determined questions intended to only capture responses from participants in a standardised fashion.

What’s interesting

It can be observed that participants’ views eventually converged into three separate opinion groups with approximately equal distribution.

In group A, it is clearly reflected that the older group (> 34 years old) disagreed with the statement that “attitudes of marginalisation, racism and abuse of these foreign workers stem from the older generation”.

In group B, participants shared the view that employers should be responsible for their migrant workers and basic wages should be allocated for these workers to survive on their own.

In group C, while largely similar to group B in their opinions, participants from Group C distinctly recognised that migrant workers are transient workers in Singapore and are not here to stay in the long-run.

Both groups B and C expressed strong opinions in disagreeing that Singapore should move away from depending on migrant workers. Unlike group A, the demographic profiles for these two groups were mixed.

Finding common ground

We found that as participants were taking the survey and establishing a common view that society should be collectively responsible for taking care of the needs of migrant workers, there was another commonality that emerged.

When statements such as “migrant workers should be integrated with the general population and not be isolated in dorms” and “Singaporeans can take the first step in allowing their homes to be rented to migrant workers” were posed, all three opinion groups expressed uncertainty, with at least 30% of participants abstaining from agreeing or disagreeing. This shows that while the participants wanted society to take responsibility in taking of the migrant workers, they were not comfortable if the issues affected them personally. We are glad that the tool is able to surface general concerns such as the Not in my Backyard (NIMBY) effect quantitatively.

There was a sense of self-awareness among the participants that while the migrant workers' issue was a collective responsibility, there was a limit to how much of the backyard would they wish to share as a society. This insight also provides an additional layer for authorities to look into as a follow-up session to perhaps provide more public education and communication about the issue.

Even though it seems that people are split on some opinions, there are actually much more in common than we think. Decision-makers can take action based on these consensuses and take the issue further. This is one of the main objectives we hope to achieve with OPPi.

Further thoughts on using OPPi

The opinion group sorter (i.e. groups A, B, C) indicates where people are divided on in their critical opinions. It enables the investigator to understand that within each group, there are varying points of agreement and disagreement. It allows the investigator to sieve out points of view that would divide people. Traditional survey methodologies already allow investigators to achieve these, but what OPPi does is take things further. Knowing what divides the public is but one aspect, and this does not allow investigators to move the needle forward as it would seem that the divide presents a deadlock to the issue. However, OPPi opens up a solution from the ground up for investigators to look at common ground opinions and bring people together in a collaborative manner.

Why is finding common ground so important?

  1. It provides the first step to resolution.
  2. It allows investigators to build trust with people by identifying common ground.
  3. It starts the process of self-awareness among investigators and participants.

Potential uses for OPPi

Through our first poll, we are confident that OPPi can be expanded into other areas of interest. Brands can utilise OPPi to gather consumers' feedback on directions on a new product and garner more substantial suggestions on improving their existing range of products. Companies can also use this tool to engage with their employees on HR-related company matters to crowdsource ideas and for employees to self-reflect on the issues facing the companies. With OPPi, the possibilities are endless.

What still needs to be fixed?

We are glad that the results of OPPi turned out better than we expected. This is still a beta launch, but we have gathered wonderful feedback from our participants. In the next version of OPPi, we will also provide deeper insights into participants’ opinions to provide greater flexibility for poll organisers to share results quickly with their target audience in a well-presented manner.

If you would like to be informed of OPPi’s official launch or want to know more about how to work with us, drop us a message at hello@oppi.live.



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AI-powered opinion crowdsourcing tool to help people to understand, participate and share perspectives about issues, places and ideas.