Lab4_Bluesky
Nicholas Bridgman
2026-06-24
Deliverables
My Data Source
My data source was BlueSky posts related to the term “organic house,” as in the type of dance music that uses warm, instrumental harmonies along with a groove-oriented style.
My Data Collection Process
Please find at the end of this document all the code I used to collect the data. The overall steps used include the following:
- Load packages
- Extract the text column regarding “organic house”
- Clean the text
- Tokenize
- Remove stopwords
- Measure word frequency
- View top words
- Create word cloud
Summary of the Collected Data:
My scraping of the term “organic house” produced 1,885 words, with a minimum length of 2 and a maximum length of 24. The top seven words were “house” (n=199), “organic” (n=180), “https” (n=48), “melodic” (n=18), “deep” (n=16), and “progressive” (n=16). Interestingly, these top words, with the exception of https, all related directly to house music. Many of the following top words in my dataset were unrelated to the genre of music, but rather related to organic gardening, such as “fertilizer” (#10) and “garden” (#17). Furthermore, other terms were completely unrelated to either of these, such as “epsteinweb” (#22).
Text Analysis Visualization - Word Cloud:
Interpretation
The word‑frequency results for posts mentioning “organic house” show primary alignment with the music genre, as noted in my Summary. This reflects user framing of the genre falling under the broader family of melodic and deep house styles. This matches Beatport’s definition of Organic House as “warm, melodic, and nature‑inspired electronic music emphasizing acoustic textures.” It also matches Melodigging’s description of the genre’s as a “warm, earthy branch of house music that blends the steady pulse of club rhythms with acoustic timbres, hand-played percussion, and natural ambience.” However, the results show a secondary alignment with organic gardening, as also noted in my Summary. Although organic house music can indeed be inspired by nature, it is unlikely that most house musicians would use gardening imagery like philodendrons or compost as cover art or promotional campaigns. This suggests a limitation of keyword-based scraping: when terms have multiple meanings, searches can produce mixed-topic datasets (Manning, 2008).
References:
Holbrook, C. (Apr. 29, 2021). Beatport Hype Adds 3 New Genres: Organic House / Downtempo, Electronica, Indie Dance. https://www.beatportal.com/articles/444326-beatport-hype-adds-3-new-genres-organic-house-downtempo-electronica-indie-dance
Manning, C., Raghavan, P. & Shutze, H. (2009). Introduction to Information Retrieval. Cambridge University Press. http://www.informationretrieval.org/
Organic House. (n.d.). Melodigging. https://www.melodigging.com/genre/organic-house
Code Used for Scraping and Analysis
Install Packages and Load BlueSky
options(repos = c(CRAN = “https://cloud.r-project.org”)) install.packages(“atrrr”) install.packages(“tidytext”) install.packages(“wordcloud”) install.packages(“RColorBrewer”)
#jsonlite
library(atrrr)
auth(“keyboardsandcoffee.bsky.social”)
Scrape Data
myfollowers <- get_followers(actor=“keyboardsandcoffee.bsky.social”,limit=4000)
Load packages:
library(dplyr) library(tidytext) library(stringr) data(“stop_words”)
Extract text column:
texts <- organic_house_results %>% select(text)
clean_text <- texts %>% mutate(text = str_to_lower(text)) %>% # lowercase mutate(text = str_replace_all(text, “[^a-z\\s]”, ” “)) %>% # remove punctuation mutate(text = str_squish(text)) # remove extra spaces
Tokenize:
tokens <- clean_text %>% unnest_tokens(word, text)
Remove stopwords:
tokens_clean <- tokens %>% anti_join(stop_words, by = “word”)
Most common terms:
word_counts <- tokens_clean %>% count(word, sort = TRUE)
head(word_counts, 20)
Word cloud visualization:
library(wordcloud)
wordcloud( words = word_counts\(word, freq = word_counts\)n, max.words = 100, colors = brewer.pal(8, “Dark2”) )
write.csv(word_counts, “word_counts.csv”, row.names = FALSE)