Shawn Huesman

I am a student at Northern Kentucky University majoring in Computer Science and Mathematics. I have lived in my hometown, Bellevue, Kentucky, for most of my life. I went to Bellevue High School and during my Junior year of high school, I was fortunate enough to participate in the School Based Scholars program as a full-time student at NKU. I chose to take some programming courses because I have always had an interest in computers. I loved every second of it! Before I knew it, I was deep within an exciting new world of problem solving and invention.

Not only did I fall in love with Computer Science, but I fell in love with NKU. The more time I spend at NKU, the more I feel like it is my home away from home. Professors are always easily accessible and love to talk to students. I feel like I could come up with any question and I could find someone who knows, or can help me find, the answer. Furthermore, I have been astonished by the opportunities available here. I feel like there is always something new and valuable around the corner to be involved in.

The L.I.F.E. Fellowship has helped me focus on my academic and professional goals. I have recently completed an internship at General Electric and I have been researching the spread of misinformation through Twitter for the past three semesters at NKU with Dr. Campan and Dr. Truta. Last summer, I participated in a REU (Research Experience for Undergraduates) at the University of Southern California in the Information Sciences Institute with Dr. Emilio Ferrara. I researched how Instagram images become manipulated through time during a period of societal unrest. I learned a lot about image clustering, image hashes, and parallel programming. I am immensely grateful for the opportunities that the L.I.F.E. Fellowship has given me. Thank you so much!


Spread of Misinformation Through Twitter

Faculty Advisor: Dr. Alina Campan & Dr. Marius Truta, NKU

Researched the spread of misinformation through Twitter by developing Python programs to collect, clean, and analyze tweets and utilized MongoDB database to store tweets.

Relevant Vaccine Keywords – Developed Python programs to determine the relevancy of a manually made list/lexicon of keywords related to vaccines
found in Tweets.

Presidential Candidate Sentiments – Used machine learning libraries to create programs with Python to determine the sentiment of a Tweet’s text.

Similar/Duplicate Instagram Images

Faculty Advisor: Dr. Emilio Ferrara, USC

Collected over 180K images on Instagram and developed Python programs to create image hashes and then cluster similar images together to analyze how similar images are transformed and manipulated over time on social media during a period of unrest.