A passion-driven capstone project is a transformative experience that can apply your experiences, skills, and interests to meaningful influence.
Sharmaine Lindahl graduated with a Master of Science in Big Data Analytics from SDSU Online in the spring of 2025.
Her capstone project blended her passions for data analytics, social justice, and the rise of misinformation in the digital world. She applied her skills to make a real-world impact during her research that focused on the misinformation and bias in social media discourse of the Hamas and Israel war and the Ukraine and Russia war.
This comprehensive academic experience helped guide Lindahl toward where she wants to take her expertise in the workforce.
We asked Lindahl about her experience working on her degree, capstone project, and what’s next for her.
What was your overall experience in the SDSU Global Campus Big Data Analytics master’s program, and what stood out most to you?
My experience in the Global Campus program was really positive. The biggest thing for me was that the program let me work independently and pursue research topics I actually cared about instead of being told what to study. I could work at my own pace and go deep into areas that interested me, like misinformation detection and geopolitical data analysis. That kind of structure works really well for how I learn but you absolutely must be self motivated or you will fall behind quickly. The program also taught me how to manipulate really large datasets, how to manage and store them securely, and how to keep data secure. Those skills were massive for my professional development. Through this, I learned how to find patterns and underlying trends in vast amounts of data, which is what made my capstone research possible. I finished with a 4.0 GPA and achieved the academic excellence award, which I think shows both that the global campus program was rigorous and that it gave me room to focus on meaningful work that mattered to me.
Your research focused on misinformation and bias in Twitter data surrounding the Hamas/Israel conflict and the Russia/Ukraine war, what drew you to this topic?
I have a very deep interest in humanity, geopolitical conflicts, society, civilization, history, and current events, so I’m naturally drawn to this stuff. I became really curious about the effects of social media after seeing so much misinformation, especially during the last election cycle. Things like Trump talking about Haitians eating cats and dogs on the debate stage based off massive misinformation that went viral on X. It became obvious that social media isn’t just reflecting what’s happening, it’s warping it. I wanted to understand how that works and if mitigation efforts are effective, especially when it comes to serious global conflicts where misinformation has real consequences. Since X locked down their API access behind an expensive paywall for researchers, I worked around that, and I used their publicly available Community Notes data sets to analyze its performance.
What were some of your most important and impactful findings from that research?
My research showed that Community Notes don’t correct misinformation fast enough to matter. It’s smoke and mirrors. There are bottlenecks in how the system scores and displays notes, and it creates this illusion that the system works because eventually high-profile viral posts do get corrected. But it can take hours or even days for those corrections to show up, and by then the misinformation has already spread everywhere. People have moved on, they’re not coming back to check for corrections, they’ve already absorbed the false information. The correction arrives too late to stop the damage. Community Notes end up being more of a historical record than an actual real-time fix, which matters when you’re thinking about how to handle misinformation during fast-moving situations like wars or political crises. It turned out that in my specific research, most notes were never publicly published.
How has your academic experience in big data analytics influenced your professional aspirations or current work today?
The program really solidified that I want to stay in research. It pushed me deeper into thinking about how data systems affect society, especially around misinformation. I’ve decided to pursue a PhD and right now I’m looking at programs that match my interests in AI red teaming, AI safety, and ethics around AI systems. I like reverse engineering things and testing systems until they break, which feels like a logical next step from the investigative work I did during my capstone. While I’m figuring out which programs and labs are the right fit, I’ve been taking additional courses in math, statistics, and ethical hacking to keep building my technical foundation. The big data analytics masters program taught me how to ask better questions about how information systems work and where they fail. Now I want to apply everything I’ve learned about big data into making AI systems more reliable and trustworthy for all users. My fear of these systems harming people is what currently keeps me up at night and I will pursue this path in the hopes of making serious impacts in how these models function in the general population. I’m also interested in working on and helping prevent model collapse.
What advice would you give to current or future students who want to work in data science, especially in areas involving social media, misinformation, or global issues?
In this program you need to take on a lot of personal responsibility and be a self-starter. If you’re not a person who has a lot of drive and ambition, you may find the program incredibly difficult. When the material gets tough, don’t give up. Make sure you contact your professors. Even though this is a virtual environment, professors still have office hours and they’re there to help. Make sure you contact people if you’re having trouble. Seek out help if you have trouble understanding, and do not rely on AI to answer everything for you. You will learn best if you do the work yourself. If you’re interested in misinformation and social media research specifically, be prepared for everything to change constantly. Platforms change their data policies, algorithms get updated, what’s true about how information spreads today might not be true six months from now. You have to be adaptable and resilient. The tools and technology in this field are always evolving, so your education doesn’t end when you graduate. You’re going to have to keep learning and upskilling continuously. If you can’t deal with constant change and teaching yourself new things, this probably isn’t the right field.
Is there anything else you would like to share about your professional or personal journey?
I want to tell people not to be afraid, especially not to be afraid if you’re making a jump from a non-computer science major that you did in your undergrad. I come from a wildlife biology and conservation background which was math and stats heavy, but I was able to make that transition very well. The information and the way that I learned in my undergrad degree actually made me a very strong student in this program. I don’t want people to think they can’t do something just because they didn’t start out with a comp sci degree. If you have quantitative skills and genuine curiosity about how systems work, you can succeed in this field regardless of where you started. Don’t let the lack of a traditional computer science degree convince you that you can’t do this work. The diversity of perspectives from different academic backgrounds makes the field stronger, and programs like this are designed to help you build the technical skills you need.
