HOBOKEN, NJ., March 26 — Researchers at Stevens Institute of Technology used machine learning tools and social network theory — the study of how people connect with each other—to better understand how people interact online. Using data from X, formerly Twitter, researchers probed the complex patterns of relationships and shared interests that link people together across the internet. In particular, they focused on elucidating how people form online communities, interact within those communities or leave them.
"A community in this study is not merely a collection of users tweeting about similar topics but an interactional cluster," explains Stevens Associate Professor Jose Ramirez-Marquez at the Department of Systems Engineering , who studies how communities evolve and interact. "In other words, it's a networked structure where users are thematically aligned and actively connected through retweets, mentions or replies."
In the past few decades, the concept of a community has transformed. Throughout history, human communities shaped based on geography, commonly influenced by access to water, fertile soil, other food sources and climate. For most of human history, communities formed based on location, evolving into villages, cities and countries. At the more local levels, communities formed through physical proximity — by people living in the same neighborhood, attending the same schools or working in the same places.
The arrival of the internet has altered the meaning of community by removing many of the geographic and social barriers, such as for example, discrimination, that once defined it. Today, online platforms allow individuals to form communities based on shared interests, identities or beliefs rather than location. Through social media, forums and online groups, people can connect with others across the world, creating networks that exchange information, support and opinions in real time. "Essentially, the internet has transformed communities from primarily local, place-based groups into dynamic, global networks shaped by digital communication and shared interests."
However, while social media allowed expanded opportunities for connection and collaboration, it brought new challenges—like anonymity and the ability to reach many people quickly, which can be misused. "As a member of an online community, I can disguise myself, and then I can say all sorts of things without social repercussions," says Ramirez-Marquez. "In real life there are consequences." Without digital communication platforms, there are also limits to how many people one can reach.
Some online communities may turn into echo chambers where people primarily interact with others who share similar viewpoints. Some groups may use social networks to spread extremist ideas or promote violence. In certain online communities, aggressive or hateful language can become normalized and encourage movements that challenge mainstream social values, amplifying false narratives and making them harder to correct. Studies have also found that increases in hateful online posts can sometimes occur shortly before rises in real-world hate crimes.
To understand how communities interact, Ramirez-Marquez and his PhD candidate Amirhossein Dezhboro developed a framework that tracks digital communities over time and classifies the topics they discuss, allowing the researchers to examine how conversations split into smaller subtopics and how these groups emerge and disappear. The research team did this by combining an analysis of what individual users write in their posts with how those users connect with each other, by assesing network data. The team published their findings in the paper titled Community Shaping in the Digital Age: A Risk-Focused Temporal Fusion Framework for Analyzing Information Diffusion and Fragmentation in Online Social Networks, published in the journal of Risk Analysis on March 26, 2026.
The framework they developed leverages machine learning classification models to analyze user posts and interactions, revealing underlying group structures. In the study, the research team also identified several key analytical elements based on social science theories to better understand the structure and dynamics of these online communities and how real-world events influence them.
"We found that social media interactions can create echo chambers and increase societal polarization, while the framework can help detect emerging misinformation communities and track how narratives spread over time," Ramirez-Marquez says.
Researchers emphasize that understanding how online communities work is important not only for scientists who study social interactions, but also for policymakers who make decisions about technology and society. "By studying how these online communities form, grow and interact, we may be able to identify early warning signs of harmful discourse,"Ramirez-Marquez says. "And that may help policymakers develop strategies to reduce potential risks while still supporting the positive aspects of online communication."
About Stevens Institute of Technology
Stevens is a premier, private research university situated in Hoboken, New Jersey. Since our founding in 1870, technological innovation has been the hallmark of Stevens' education and research. Within the university's three schools and one college, more than 8,000 undergraduate and graduate students collaborate closely with faculty in an interdisciplinary, student-centric, entrepreneurial environment. Academic and research programs spanning business, computing, engineering, the arts and other disciplines actively advance the frontiers of science and leverage technology to confront our most pressing global challenges. The university continues to be consistently ranked among the nation's leaders in career services, post-graduation salaries of alumni and return on tuition investment.
