Oliver Tabino

Let’s start with you. Who are you, and what lens do you bring to understanding people online?

I’m Oliver Tabino, co-founder and managing director of Q, a market research agency based in Germany. My background is rooted in qualitative research, sociology, and innovation work, but over the last 20 years, a big part of my focus has shifted toward understanding how people behave in digital and social environments.

The lens I bring is a combination of sociological depth and contemporary platform logic. I don’t look at online behavior as something fundamentally separate from “real life.” Instead, I see digital spaces as compressed, accelerated stages where human needs like belonging, status, security, identity become visible faster and often more exaggerated. Platforms don’t change people; they amplify them.

What’s important to me is context. A like, a comment, or a share only makes sense when you understand the social norms of that space, the invisible rules of the platform, and the personal stakes for the user. I’m less interested in what people say online and more in why this expression makes sense here and now.

That’s why I’m skeptical of purely data-driven interpretations. Numbers tell you what happened. Understanding people requires interpretation, empathy, and the willingness to sit with ambiguity.

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What’s a working theory you have right now about how people behave online?

One working theory I keep coming back to is that often online behavior is not about expression, but about risk management. People constantly negotiate how visible, vulnerable, or controversial they can afford to be professionally, socially, emotionally.

We often assume social media is about sharing opinions, but in reality, many users are running calculations: Who might see this? How could this be interpreted later? What does this signal about me? Especially on semi-public platforms like LinkedIn, people are less driven by authenticity and more by reputational safety.

Another part of the theory is that engagement doesn’t equal resonance. Someone might strongly agree with content and still not interact, simply because interacting would position them in a way they’re not comfortable with. Lurking, saving, or privately sharing content are often stronger indicators of impact than likes or comments.

This also explains why polarizing content spreads so well: it simplifies decision-making. It tells people exactly where to stand. Nuance requires effort and effort is expensive online.

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What’s an insight you surfaced that you still think about? What one stuck with you?

One insight that still sticks with me came from qualitative work around innovation and digital products: people are remarkably good at adapting to bad systems and that adaptation often hides the real problem.

We observed users who had developed complex workarounds, shortcuts, even emotional coping strategies to deal with tools they disliked. When asked directly, they would say, “It’s fine, I’m used to it.” But when we unpacked their behavior, it became clear how much cognitive load and frustration had been normalized.

What struck me was not just the insight itself, but how dangerous it is for companies. If you only rely on surface-level feedback or usage metrics, you might conclude everything is working. In reality, users are silently compensating.

This insight changed how I look at digital behavior in general, including social media. Just because people participate doesn’t mean they’re comfortable. Silence doesn’t equal satisfaction. Often, the most interesting signals are the things people no longer complain about because they’ve given up expecting change.

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What’s the weirdest rabbit hole your work has ever sent you down? And what did it teach you?

Two topics made a particularly strong impression on me. We regularly work for pharmaceutical companies and also in the field of mental illness. A study on people with depression and how they deal with it online repeatedly pushed me and my colleagues to our limits. These are very emotional projects.

This year, I also supervised a project involving a German brand that came under massive pressure and attack from the right-wing and right-wing conservative community because of its packaging design. The “Go-Woke-Go-Broke” narrative triggered obviously people. Everything was there, from hate speech to misogyny, racism, crude conspiracy theories and fake news. These are moments when I wonder how our society can remain capable of discourse and fair debates at all.

Above all, I have learned that I don't want to be so hateful and that each of us can help make the (online) world a better place by not behaving in this way.

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What skills or mindsets do you think the next generation of analysts will need?

The most important skill will be interpretation, not pure data handling. AI and automation will increasingly take over collection, clustering, and even basic analysis. What they won’t replace easily is judgment. The human in the loop is getting more and more important.

Next-generation analysts need to be comfortable with ambiguity and contradiction. Instead of forcing clean narratives, they should be able to hold tensions: people saying one thing and doing another, metrics pointing in different directions, insights that don’t translate neatly into actions.

Another key mindset is systems thinking. Online behavior doesn’t happen in isolation. It’s shaped by platform incentives, economic pressure, social norms, and personal biography. Analysts need to zoom out as confidently as they zoom in.

Finally, I believe empathy will become a strategic skill. The disciplined ability to truly understand perspectives that are not your own and the ability to feel even as a “neutral” analyst will be very important. Without that, analysis becomes pattern recognition without meaning.

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What’s a niche community, account, or corner of the internet you’re watching right now? And why?

Because of our work, I have already dealt with so many special topics. I am more excited to see what we will soon be working on and allowed to work on again.

What fascinates me right now is the influence of AI on human behaviour and the relationship between humans and machines, or parasocial phenomena. There are still exciting projects and experiments waiting for us.

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Last non-work thing you read that shaped your thinking?

I read at the beginning of the year Philip Hübl’s Moralspektakel. He is a German philosopher and the book shaped my thinking about social media because it gave language and structure to something I had observed intuitively for a long time. Hübl describes how moral communication increasingly becomes a form of performance, less about ethical reflection and more about public positioning. When I read the book, many dynamics of social media suddenly clicked.

Platforms reward visibility, emotional clarity, and moral certainty. That combination turns moral statements into signals: This is who I am, and this is where I stand. In social media, morality often functions less as a guide for action and more as a tool for distinction, belonging, and sometimes exclusion. Hübl’s analysis helped me see how outrage, shaming, and simplified good-versus-bad narratives are not accidents of the system, but logical outcomes of attention-driven environments.

What stayed with me most is the discomforting insight that good intentions don’t protect us from these dynamics. Even well-meaning participation can reinforce moral spectacle. Since reading Moralspektakel, I’ve become more critical of my own online behavior and more cautious about confusing moral expression with moral progress.

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