From a Conference in Brazil to a 4-Country Study: How NodeXL Is Changing the Way Researchers Map Social Representations

Discover how one researcher used NodeXL and Gephi at Brazil’s JIRS 2026 conference to combine network analysis and Social Representations Theory and what happened next.

When a Four-Hour Workshop Changes Research Plans

A room full of qualitative researchers in Natal, northeastern Brazil, opening network analysis software for the first time. No prior experience. No technical background. Just curiosity and four hours to figure out whether tools like NodeXL and Gephi had anything meaningful to offer their field.

By the end of the afternoon, they did. And one of those conversations led to something nobody planned for: a cross-national research collaboration spanning Brazil, Paraguay, and Colombia.

That’s the kind of thing that happens when you bring the right methodology into the right room.

What Is NodeXL and Why Should Social Researchers Care?

If you’ve ever wanted to understand how ideas spread, how communities form around a topic, or how certain voices dominate a conversation, network analysis is the methodology you’ve been missing.

NodeXL is one of the most accessible entry points into that world. Unlike platforms that require steep learning curves, NodeXL works directly inside Microsoft Excel. If you can build a spreadsheet or make a pie chart, you can start mapping networks. It lets you visualize relationships between people, topics, and ideas turning raw data into something you can actually see and interpret.

Gephi is a related powerful and advanced network visualization and metrics tool. Together, the two tools give researchers something genuinely powerful: the ability to map the structure of meaning, not just its content.

For anyone working in Social Representations Theory, that distinction matters enormously.

The Minicourse That Started It

At the XIII International Conference on Social Representations (JIRS 2026), held at UFRN in Natal, Brazil, researcher Anderson da Silveira led a four-hour minicourse titled Social Network Analysis Using Gephi and NodeXL. The session ran on April 30, 2026, from 1:00 to 5:00 PM, hosted in the Mathematics Laboratory of UFRN’s Education Block.

The workshop started with the basic concepts: nodes, edges, degree centrality, what it actually means when two things are “connected” in a network and moved quickly into hands-on practice. Participants imported data, ran network metrics, generated visual layouts, and learned to read those visualizations with a critical eye rather than just an aesthetic one.

What made it work wasn’t just the software. It was the framing. Anderson was clear from the start: network analysis doesn’t replace interpretive research. It adds a layer. It makes the relational architecture of a social representation visible in ways that qualitative analysis alone simply can’t achieve at scale.

Why NodeXL Works So Well for This Kind of Research

Social Representations Theory, developed by Serge Moscovici, has always been interested in how ideas travel how scientific concepts move into everyday life, how they get anchored to familiar images, how communities collectively make sense of new phenomena. What it hasn’t always had is a way to trace those movements through large, complex datasets.That’s where NodeXL earns its place in a researcher’s toolkit.

Here’s what network analysis can actually help you do:

Map how topics cluster. NodeXL reveals which ideas tend to appear together, who shares them, and which themes sit at the center of a conversation versus the periphery.

Identify key actors. In any social network, some nodes are more connected than others. NodeXL makes those hubs visible whether they’re influential voices in a social media dataset or recurring themes in interview data.

Visualize change over time. Run the same network analysis on data from different time periods and you can watch representations shift which is exactly the kind of longitudinal insight SRT researchers are looking for.

Lower the barrier to entry. Because it lives inside Excel, NodeXL doesn’t require researchers to learn a new environment. The methodology is new; the interface is familiar. That matters when you’re introducing a method to a skeptical or time-pressed audience.

What Happened After the Workshop

As the session wrapped up in Natal, one conversation turned into an invitation. Anderson left the conference with a proposal to lead a collaborative study bringing together researchers from northern and southern Brazil, Paraguay, and Colombia all focused on the social representations and everyday practices surrounding the use of weight-loss injection pens.

It’s a timely topic. As GLP-1 medications become increasingly common across Latin American societies, understanding how communities construct meaning around them who trusts them, who fears them, what cultural narratives form around their use is a genuine public health question. And network analysis will be a core part of how that research gets done.

One workshop. Four countries. That’s not a bad afternoon’s work.

The lesson here for your own research

The point of sharing this isn’t just to document what happened in Natal. It’s to make a case for something broader.

If your research involves understanding how meaning moves through communities how beliefs form, spread, get challenged, or get reinforced network analysis belongs in your toolkit. And NodeXL is one of the most practical, least intimidating ways to get started.

You don’t need to be a data scientist. You don’t need to code. You need a dataset, an Excel file, and a willingness to look at your research questions from a different angle.

The researchers in that room in Natal came in as skeptics and left as converts. Not because network analysis replaced what they already knew how to do but because it gave them a new way to see it.

That’s what the right tool does.

Want to Explore NodeXL for Your Own Research?

Whether you’re working in social psychology, public health, communication studies, or any field where relationships and representations matter, network analysis has something to offer you. Start with NodeXL. Download it, open a dataset you already have, and see what the structure of your data looks like when you can actually see it.

The learning curve is gentler than it looks. The harder part is just deciding to begin.

Anderson da Silveira is a researcher at the Laboratório de Psicologia Social da Comunicação e Cognição (LACCOS), Programa de Pós-Graduação em Psicologia, Universidade Federal de Santa Catarina, Brazil. His research focuses on Social Representations Theory, computational social science, and digital research methods.