Innovative Social Data Science research based on digital methods, computational tools, qualitative analyzes and
statistical techniques
Monitored Ecosystem
NetLab's multidisciplinary team developed its own infrastructure for data collection and constant monitoring of platforms and websites. This system works in continuous updating, to adapt to frequent changes in the platforms' data sharing policies and interfaces.
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Our infrastructure, unique in Brazil, is capable of uninterruptedly monitoring profiles and themes defined according to the Laboratory's research agenda.
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We currently collect data from X/Twitter, Instagram, Facebook, YouTube, TikTok, WhatsApp, Telegram, Google Ads and Meta Ads , as well as professional news portals, local vehicles and junk news.
Collect
& Analysis
Despite the importance that digital platforms have today in shaping public opinion, transparency and public knowledge about how they work are not proportional to their potential impact on social life.
From the digital data we collect, the information is processed and analyzed by our researchers. From this process, we provide evidence and insights into the the phenomenon of online disinformation to support the fight against public opiniom manipulation strategies in Brazil, informing the adoption of public and governance policies that generate a real impact on society.
Methods, Analysis & Development
NetLab UFRJ develops social science methods to expand, create and implement research strategies in order to empirically and critically investigate the effects of the media ecosystem on public opinion - including mass media, alternative media, hyperpartisan media, news websites fakes and social media.
Based on the theoretical-conceptual framework of digital methods, our research combines different qualitative and quantitative analyzes from a non-obtrusive approach. In non-obstructive observation, data are collected without researchers interfering with the object of study. Called “digital traces”, this data provides indicators about the form and volume of social interactions regarding the use of platforms.
In an interdisciplinary perspective, we combine traditional social science research approaches with innovative approaches, including the development of AI, algorithms and computational solutions for social data analysis. We develop customized machine learning computational tools to detect harmful agents, fraudulent strategies and identify problematic content.