The non-obvious insight I wanted to extract from my data was if people were trying to research healthier recipes and healthy alternative recipes to cook and eat during the COVID-19 pandemic. During the pandemic, most people were not able to eat out at their favorite restaurants and pick up pre-made food at other vendors since they were closed to prevent the spread of the disease. The data which could answer this question would be found on social media since that is where people share most of their life details and daily routines with each other. The source of my network data was from Tweepy the Twitter scraping API. The network represents all of the data collected from the Twitter user dat. The nodes/vertices are, different tweet topics/ content. The edges are, the relationships between the different tweets by the Twitter users posting about their diet choices. The structure of this graph is the connections and relationships between all of the collected tweets. A set of important nodes are the ones connecting the different topics to each other to show how all the tweet’s content is related to each other. One of the software I used to facilitate the network analysis was working some in python coding and some in Network X. Some bugs I had in this process were trying to figure out what I wanted to nodes and edges to be for my topic to analyze. The limitations of my analysis are that it is hard to see change over time in these people’s diets or food choices since all the tweets focus on related hashtagged topics not change over time. The main takeaways from my network analysis are, how edges which are the relationships between the tweets greatly depend on the nodes which are the topics of those tweets in the relationships are created between.