ATinstagram

For this project, we wanted to discover if and how hikers use the social media platform, Instagram, to talk about Leave No Trace (LNT) principles on the Appalachian Trail. Leave No Trace principles refer to a set of guidelines that hikers should follow in order to promote conservation on trails.

The workflow to complete the project included: collecting relevant Instagram posts, performing sentiment analysis on these posts, and finally creating a series of graphs that show the different connections between posts. We started by utilizing Python, JSON objects, and Selenium to gather all of the Instagram posts with specific hashtags, such as “#AppalachianTrail” , “LeaveNoTrace”, and “LNT”. Selenium is used for the API calls, which retrieve the many Instagram posts. Information about each post, such as its geographic location, caption, and hashtag are extracted using JSON objects.

The final two parts of the project include performing sentiment analysis on the collected posts and then visualizing the data in a variety of ways. For the sentiment analysis, we analyzed each caption of every post, and assigned it a score ranging from negative one to positive one. Negative one would represent a highly negative sentiment and positive one represents a highly positive sentiment. From there, we utilized the K-Means Clustering algorithm to gather posts with similar hashtags. For the visualizations, we displayed what tags occur in the same post, connections between different hashtags, and the geolocations of the different posts. The deliverables of our project include the source code that is used to scrape the Instagram posts, perform sentiment analysis, and visualize the data, along with several folders showing the results of our data collection. These results include the scraped Instagram posts, the sentiment analysis results, and the visualizations we created. These deliverables could help our client and those interested with research relating to Instagram, Leave No Trace principles, and the Appalachian Trail.