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METHODOLOGY
distant readings
This project utilises a preprocessed dataset of approximately 50,000 r/legaladvice submissions, focusing the analysis on the selftext column where each post represents an individual observation.
To get a lay of the land, the project will begin with distant reading using the raw data, TF-IDF, topic modelling, and t-SNE word embeddings. This will not only show the kinds of topics that the board discusses, but also what kinds of posts are valued by the community.
We will then zoom in on relevant individual posts. This is where meaning can truly be extracted via close reading and interpretation — I found this step irreplaceable. Due to the nature of certain computational models, this process is especially necessary. Consider TF-IDF, used to create the chart below. It inherently takes words out of context. “Pay” here could refer to money (“I got paid”) or violence (“you will pay for this”). If you don’t look back at the full text, you won’t be interpreting the corpus at all, but some distorted version of it.
I thus engaged with the dataset through Gerbaudo’s distant/close reading framework: first reading posts as data points, then as part of a conversation, then as part of broader social discourse.
Word Embeddings (t-SNE)
Map semantic clusters and visualise the split between operational (sue, damages, court) and emotional vocabulary (fair, fault, jerk).
Revealed that the r/legaladvice community perceived emotional/procedural language as fundamentally divided, with very little overlap.
Topic Modelling (LDA):
Segment the corpus into 11 thematic sub-domains
I then identified the 3 best-fitting posts from two categories (employment vs interpersonal), conducting close readings to determine similarities, patterns, and differences.
The two categories were selected because employment-related terms have come up again and again throughout my research. Specifically, work, company, and pay tend to surface.
Regex-Based Corpus Segmentation
Filter and divide the dataset into two distinct sub-corpora: Update Posts vs. Regular Posts. This enabled direct comparisons of tone, vocabulary, and community engagement
Update category search terms are very strong— all descriptors are characteristic of that group, meaning the update posts have a coherent vocabulary. Regular did not have this.
Not much difference in subject matter: both have a blend
Update category had https, amp, location, and comments due to update post conventions
TF-IDF (+Differential Vocabulary)
Extract high-frequency terms across the corpus
Updated TF-IDF score analysis demonstrates differences in post topics/language between the top-upvoted and other, regular posts.
RAW CORPUS/DATASET
WORD EMBEDDINGS
TF-IDF
TOPIC MODELLING
SUB-CORPUS REGEX COMPARISON
roads not taken
HYPOTHESIS
Moderation in r/legaladvice is so aggressive that the vast number of off-topic sympathetic or emotionally helpful posts are permanently deleted by mods, leading to ire from community members.
HYPOTHESIS
Community members would likely shift from being advisors on clinical posts to an emotional audience on narrative posts. I also believed update posts would likely receive far fewer comments than regular initial queries, as 1) most update posts are locked by moderators since they lack laegal questions or 2) any off-topic (emotional) responses generated will be deleted, and since update posts are generally more narrative it would induce more emotional responses.
I wanted to prove both these suspicions quantitatively through my dataset, so I attempted to link the comments and submissions datasets to measure comment volume, vocabulary, and thread-locking behaviour across registers. As I had no experience doing this, I consulted the UC-licensed Gemini AI, which informed me that because my dataset lacked “parent-submission foreign keys (link_id)”, direct thread-mapping was impossible. I then double-checked with my TA, who confirmed that my comment .csv file lacked parent IDs that related it to my submissions, foreclosing comment analysis as a technique I could pursue with my current dataset. This limitation was disappointing but not entirely unexpected; I'd learned that what techniques and data you can pursue emerge bottom-up from what information is made available.
Couldn’t determine how to overcome this gap, at least with the resources available to me. If all I can see are a chain of [deleted comments], I can't tell if those remarks were simply empathetic or if they were rude and unhelpful. The question got complicated when this Reddit post was analysed, when I conducted close readings on some of the top 15 highest scoring posts. Ran into an nterpretive hurdle when attempting to determine what exactly the dividing line between deleted comments and “off-topic but alright” comments is.