The Research Fields of SEOlogie

SEOlogie investigates one single, fundamental question: How does a source get found by the people who fit — in the digital world, through algorithms, through language, through trust?

This question has many dimensions. Each dimension is a research field. No field is finished. No field belongs to anyone. And a contribution may touch several fields at once — that's welcome, not a problem.

This is not a system of categories. It's a map of open questions.

Why exactly these fields?

Because being found in the digital world is more than technology. And more than content. And more than psychology.

It is the interplay of everything: Who is searching — and why? What makes sources findable? Which signals create connections? Where does visibility arise in the first place? Which systems get to decide? How does trust come about — with people and with machines? What is manipulation — and why does it fail in the long run? How do you measure whether you've truly been found? How do you keep visibility alive? What technical foundation carries all of this? What language builds bridges? And how do networks amplify or suppress visibility?

Each of these fields is a discipline in its own right. Together they make up SEOlogie.

A fourteenth field was added when one thing became clear: whoever wants to research how being found works must also research how perception works — on all sides.

The fourteen research fields

Search Behavior Research

Core question: How, where and for what do the people who fit search?

People don't search like machines. They search with intentions, with emotions, with expectations they often don't fully know themselves. They type words into search boxes, put questions to AI assistants, scroll through feeds, listen to recommendations. Search behavior changes with technology, with culture, with the circumstances of a life.

The search behavior research of SEOlogie asks: How does a search query come into being? What lies behind a keyword — which need, which situation, which emotion? How does searching change when AI answers instead of linking? How do people search in different cultures and contexts?

Whoever does research here understands the people who fit more deeply than any target-group analysis ever could.

Source Research

Core question: What makes a source findable, credible and unmistakable?

In the communication model of SEOlogie, the source is what is there and can be found — it doesn't broadcast, it lets itself be found. But what makes a source a source? What separates a genuine source from one that merely pretends to be one?

Source research examines identity, clarity, authenticity and positioning. It asks: Why are some sources easy to find and others never found — even though both exist? What does it mean to be a source in a time when AI paraphrases and summarizes instead of linking? How does a source describe itself so that the people who fit recognize it — and those who don't fit, don't?

Signal Research

Core question: What connects a question with the source that fits?

Between what a seeker asks and what a source offers lies a signal — a connection that has to be made. That signal can be a matter of language, of content, of structure or of technology.

Signal research asks: What are the signals that create connections? How do they come about? How do gatekeepers read them? What makes a signal strong — and what makes it weak or invisible? How do signals change when AI steps in between question and answer?

Placement Research

Core question: Where do people search — and where do they find?

Google is not the only place. YouTube, LinkedIn, Instagram, TikTok, Amazon, ChatGPT, podcasts, newsletters, specialist forums, review platforms, industry directories — visibility arises in very different places. And every place has its own logic, its own language, its own people who fit.

Placement research asks: Where do the people who fit a particular source really search? Which places are overrated, which overlooked? How do the rules of different platforms differ? How does the map of places keep changing — and how do you keep from chasing after every new trend?

Gatekeeping Research

Core question: How do algorithms and AI decide what becomes visible?

Between a source and the seekers, there is always a system that decides. Search engines, social media algorithms, AI assistants, rating systems, recommendation algorithms — they are all gatekeepers. They reward some things, ignore others, punish a few.

Gatekeeping research asks: How do these systems work? What do they reward — and why? How is that changing? What can sources do to be recognized — without manipulating? And what happens when AI no longer links but answers by itself?

Algorithmic Mediation Research (AI Research)

Core question: How is artificial intelligence fundamentally changing being found?

AI Optimization (AIO), Generative Engine Optimization (GEO), Answer Engine Optimization (AEO) — these aren't buzzwords. They are signs that the foundation visibility has been built on so far is shifting.

When AI gives answers instead of links — who still gets cited, and why? When search queries turn into conversations — what does that mean for signals and placements? When algorithms no longer merely select but formulate on their own — what, then, is still a source?

This research field is the youngest and the fastest growing. It needs more researchers than any other.

Trust Research

Core question: How does trust come about — with people and with machines?

Trust is the precondition of fit. No trust, no click. No trust, no decision. No trust, no being found that actually changes anything.

But trust doesn't arise from assertion. It arises from consistency, from genuineness, from reasoning you can follow. And it arises on different levels: trust in the organization, trust in the person, trust in the offer or product.

Trust research asks: How does digital trust come about? What signals credibility — for people and for gatekeeper systems? How is trust built up, and how quickly is it destroyed? What does Google's E-E-A-T concept have to do with the psychology of trust?

Manipulation Research

Core question: Which techniques produce sham visibility — and why do they fail in the long run?

Manipulation is the shadow of SEOlogie. It is the opposite of genuinely being found: short-term tricks that outwit systems instead of convincing them, that deceive people instead of informing them.

Clickbait. Keyword stuffing. Fake reviews. Artificial scarcity. Masks of authority. Dark patterns. Social proof as illusion.

Manipulation research asks: What are the concrete techniques? Why do they work in the short run — and why do they fail in the long run? What are the legitimate remedies? Where is the line between persuasion and manipulation? And how do gatekeepers detect manipulation — and how do they react to it?

Whoever understands manipulation protects themselves and their clients from it.

Impact Research

Core question: How do we measure whether the people who fit have found the source?

Being found is not an end in itself. It's not about clicks, not about impressions, not about traffic. It's about whether the people who fit have found the source — and whether a genuine connection grew out of it.

Impact research asks: What is the right measure? How do you distinguish the quantity of being found from its quality? Which signals show that fit has come about? How do you measure trust — which often doesn't show up in immediate clicks? And how do you tell correlation from causation?

Optimization Research

Core question: How does visibility stay alive — as a continuous process?

Visibility is not a state. It is a cycle: perceive, understand, plan, act — and start again. The world changes. Search behavior changes. Algorithms change. The people who fit change.

Optimization research asks: What does a working cycle look like? Which rhythms make sense? How do you prioritize measures when not everything can happen at once? What are the most common mistakes in the optimization process? And how do you keep optimization from turning into manipulation?

Technology Research

Core question: What technical infrastructure makes findability possible in the first place?

A source that is technically invisible won't be found — no matter how good its content is. Load times, crawlability, structured data, Core Web Vitals, mobile optimization, URL structures, indexing — these are the foundations everything else builds on.

Technology research asks: What are the technical preconditions of visibility? How do they change? What is indispensable, and what is overrated? And how does technology communicate with the gatekeeper systems — in a way that builds trust instead of undermining it?

Language Research

Core question: What language builds bridges between what people search for and what sources offer?

Language is the key. When a source describes its own work in a language that the people who fit it would never use — no connection arises. The language of companies and the language of the seekers are often different. Sometimes radically different.

Language research asks: How does the source speak — and how do the people who fit it speak? Where are the gaps? Which words build connection, and which destroy it? How does the language of searching change with new technologies? And how does language differ across cultures, generations and contexts?

Network Research

Core question: How do networks amplify or suppress being found?

No source is an island. Recommendations, links, mentions, social amplification — networks can raise visibility exponentially or cap it for good. And gatekeeper systems read network signals as signals of trust.

Network research asks: How do digital networks work for being found? Which network connections are relevant — and which are just noise? How do you build a network that sustains visibility instead of merely simulating activity? And how do you tell genuine connectedness from manipulated link structures?

Perception Research

Core question: What has to be perceived — and by whom — for visibility to arise and to last?

Perception research is an established scientific field at the crossroads of psychology, biology and neuroscience. It studies how living beings take in stimuli, filter them and interpret them — and how the brain, in doing so, actively constructs its own version of reality rather than passively mirroring one.

SEOlogie touches this field from three sides.

The perception of the seekers: A seeker never perceives neutrally. They bring expectations, language, experiences with them — and these shape what they even see, before they click. Why do they perceive source A and not source B — although both exist? What makes a signal perceivable? What does the brain filter away before it ever reaches consciousness?

The perception of the gatekeepers: Algorithms and AI systems are technical implementations of perception. They too select, filter, weigh. What attention is to human consciousness, the ranking algorithm is to the gatekeeper. The question is: What does a source have to give off for a gatekeeper system to perceive it at all?

The perception of the source itself: Here lies perhaps the most important dimension. A source that doesn't perceive what is changing around it — shifting search questions, new places, shifting gatekeeper rules — loses its visibility quietly and unnoticed. That is the core of the SEOlogie cycle: visibility needs structured self-perception.

The perception research of SEOlogie asks: Which signals have to be perceived? How do you tell relevant change from noise? Why is the actual achievement — the noticing — so rarely recognized as an achievement? And what are the blind spots of sources, gatekeepers and seekers?

What's still missing

These fourteen fields are not complete. They are the state of things in 2026.

Being found is a living phenomenon. New technologies open new research spaces. New developments in society pose new questions. New observations from practice show where theory is still missing.

If you notice a research field missing here — that is the first hint that it may need to be opened up.

→ Submit a thesis and join the research