AO3: Author Popularity within the Star Wars Fandom

IDC 5131: Distributed Computing

Sarah Nash, Raymond Blaha, Paul Hwang, Fehmi Neffati, Joshua Ingram

Project Overview

Alice enjoys reading and writing Fan Fiction within the online community An Archive of Our Own (AO3). She has noticed that some authors generate a large and loyal following in online fandoms. She aspires to gain popularity in these communities. She has enlisted your team to help her to understand current trends in what successful authors are doing in Fan Fiction communities.

Project Contributions

Joshua Ingram

• Project Management
• Analysis of Author Post Frequency (R)
• Predicting Author Popularity (R)
• Data Munging (R)
• Presentation Slides

Sarah Nash

• Data Collection through Web Scraper (Python)
• Data Munging (R)
• Popular Characters and Pairings (R)
• Presentation Slides

Paul Hwang

• Determining and Analyzing Metrics for Author Popularity (R)
• Predicting Author Popularity (Python)
• Data Munging (R)
• Presentation Slides

Raymond Blaha

• Analysis of Story Texts (Python)
• Sentinment Analysis of Stories (Python)
• Data Munging (SQL)

Fehmi Neffati

• Data Collection on Popular Tags (Python)
• Data Munging (Python)

Project Summary

Using the BeautifulSoup4 webscraper, we collected data on the AO3 General Audiences Star Wars Fandom Stories. We used the Number of Hits as a way to determine story and author popularity. We analyzed the relationships author average post ratings had with their post frequency, story tags, characters and pairings, and story structure. Upon completion of our analysis, we recommend the following to Alice:

1. Post frequently (≥ 1 story/month)
2. Write longer stories (> 4000 words)
3. Write stories that include Darth Vader, Anakin Skywalker, Obi-Wan Kenobi, Ahsoka Tano, The Mandalorian & Grogu (Baby Yoda), Mace Windu (Important Characters from The Mandalorian and the Clone Wars series)
4. Consider exploring stories about time-travel in the Star Wars Universe

About this Repository

This repository is broken up into folders based on focus.

analysis - Files focused on analyzing and visualing data. Some data munging.

data - Data files.

data_munging - Data munging files.

documentation - PDFs, slides, logbook, etc.

webscraper - Webscraper code for data collection.