1. Analyzing Twitter Data
SEAP/NREIP Student:
Vatsal Ojha
Dougherty Valley High School
12th Grade
Mentor:
Dr. Ralucca Gera
Department of Mathematics, NPS
Project Dates: 7/20– 8/12
Results / Accomplishments / Next Steps:
We demonstrated that the average number of cliques (i.e. where all nodes are connected to each other) was the best determining factor in defining social networks specifically. Currently, with respect to the data, our preliminary results show little structural differences between networks.
Goals: to implement the unsupervised learning algorithm to determine classifications for communities and find textual differences between these classifications for communities.
Project Objective and Research Approach:
Aim: classify communities and determine which attributes are deterministic in separating different communities based on their types.
Data: to use a large database of twitter networks
Methodology: split up the data into usable communities (Louvain community detection), and
run unsupervised machine learning algorithms on them to classify multiple networks into categories based on patterns in the metrics obtained on the communities.
Distribution type Type