The discussion about language quality and its downfall is not recent and has been ever present since humans first started to talk. Just like Molière's writing was not considered, at his time, to be of the same quality as his peers, he is now considered the standard of the french language, showing how standards evolve over time. However, it is interesting to note that while social media platforms have always been the target of mockery when talking about language quality, we do see sentiments of users criticising its downfall within the platform itself. Are these concerns verifiable, or can high-quality language still be found within Reddit?

1. Dataset

Hyperlinks

Our primary dataset consists of the "Subreddit Hyperlinks Network." This is a massive collection of posts where one subreddit links to another. While it does not provides us with the raw text body of the posts, it does provide timestamps, sentiment labels and 86 different linguistic metrics (from low-level metrics such as the number of characters in the post, to high-end metrics derived from LIWC, a state-of-the-art linguistics model), allowing us to analyze in-depth the way users express themselves.

Embeddings

Along with the initial subreddit hyperlinks dataset, we used another dataset: the embedding vectors of subreddits.

These embeddings are high-dimensional vectors (in our case, 300 dimensions). They are designed to represent similarities between data points in a complex space. Specifically, each of the ~50,000 subreddits is assigned a 300-dimensional vector. These vectors indicate similarity based on user behavior: if many users post in the same group of subreddits, those subreddits will be closer to each other in this 300-dimensional space.

These embeddings are very useful for our research on linguistic quality and our goal to determine "who speaks the best". They allow us to identify clusters of subreddits where users share similar interests, effectively grouping subreddits into distinct communities.

Weakness

While these embeddings are powerful and perform well, the dataset faces challenges due to the nature of Reddit communities and the high number of data.

Indeed communities are not perfectly separated into 'clean' clusters where we can easily say: 'These users are only interested in politics.' Most users have diverse interests and post in a wide variety of subreddits. This creates a significant amount of overlap between vectors. Consequently, some subreddits appear very close to many others in the 300-dimensional space, which can create 'noisy' links and blur the boundaries between different communities.

Visualizing the Embedding Weaknesses

The following graphs illustrate the "fuzzy" nature of these communities. On Figure 1, a 2D projection (using dimensionality reduction) of the 50,000 subreddits shows the 300-dimensional vectors in a 2D-space. Each point represents a subreddit (put your mouse on a point to see which one). We see that the subreddits are not clustered into distinct groups, but rather forming a large, dense circle with significant overlap. We can still identify isolated clusters like on (5, -5) which is quite defined. By zooming and looking at subreddits names, we can see that it should be related to pop culture or video-games with subreddits like capecodegaming, interactivecinema or virtualrealitygaming. Nevertheless it is hard to confirm this since there is a lot of noise. There is also subreddits as snowskating or abstraction. This visualization confirms that many communities are not clearly separated.

On Figure 2, the graph displays the distribution of close neighbors. The x-axis represents the number of neighbors as the y-axis shows the number of subreddits. A close neighbor is defined using cosine similarity. Cosine similarity measures the orientation of two vectors in the 300-dimensional space, ranging from -1 (opposite) to 1 (identical). In order to be closed, a neighbor needs 80% similarity. The distribution reveals a non-negligible number of subreddits that have more than 10,000 neighbors with over 80% similarity.

Figure 2: Distribution of Close Neighbors

2. What Is Language Quality?

Feature Analysis

The histograms above show distinct characteristics. For example, lexical_richness shows a sharp spike at 1.0. This artifact represents very short posts (e.g., "Yes", "lol") where every word is unique. This confirms why simple ratios fail and why we need robust metrics. Meanwhile, cognitive_depth often spikes at zero, indicating that a significant portion of Reddit communication is purely phatic or descriptive, lacking explicit reasoning words.

Figure 4: Correlation Matrix of Linguistic Features

Orthogonality of Features

An essential check was to ensure our metrics weren't just measuring the same thing four times. As the correlation matrix shows, our chosen features have low overlap.

Interestingly, Structural Complexity and Cognitive Depth have the highest amount of correlation between our four metrics. While it is low enough to understand that they measure different dimensions of the "linguisic profile", it can be explained from the fact that posts with a complex argument will tend to make use of more complex words and sentence structures.

Moreover, the correlation between LQI and different sentiment metrics (such as Link_Sentiment, LIWC_Swear and vader_compound) is quite low. This will be subject to an in-depth analysis in section 6, but it already hints to a low influence of sentimentality on linguistic quality.

3. Linguistic Quality Within Specific Subreddits

Let's try to validate our instincts by checking up on the "best" and "worst" performing subreddits.

4. Linguistic variation within a topic/community

Methodology

Clustering Strategy and Challenges

The primary challenge in this analysis stems from "bridges": subreddits frequented by users from vastly different backgrounds. These bridges exhibit a high number of neighbors, making them notoriously difficult to classify. In standard clustering, these points either force the creation of massive, noisy clusters (K-means) or are discarded entirely as outliers (HDBSCAN).

Refined Clustering Approach

At this stage, we realized our fundamental assumption was flawed: we were looking for predefined topics (e.g., "politics", "video games"). In reality, embeddings capture community behaviors. A cluster might not represent a single subject, but rather a specific demographic of users who share multiple interests.

To refine the methodology, three key improvements were implemented:

1. Scope Restriction: Clustering performed exclusively on subreddits in our dataset.
2. Recursive Refinement: Clusters were manually reviewed, and noisy groups were re-processed to achieve finer granularity.
3. Representative Sampling: Only the 250 subreddits closest to each cluster's centroid were retained.

The first improvement significantly enhanced overall results while reducing the computational load (RAM usage). By filtering out unnecessary data, the number of active subreddits was reduced from ~50,000 to approximately 17,000.

The second improvement expanded the diversity of identified groups. By performing a second clustering pass on the previously discarded "noise," we successfully identified 13 additional communities, bringing the total to 36 relevant clusters.

The third improvement ensured higher community consistency. Given that the embeddings were dense and spread out, large clusters naturally accumulated noise. To counter this, we focused on the "core" of each community: by retaining only the subreddits closest to the centroids, we preserved the most representative samples. Ultimately, the final dataset consists of 6,562 subreddits distributed across 36 high-cohesion clusters.

Results (Pass 1)

Figure 6: Distribution before filtering (High Noise)

Figure 7: Distribution after keeping 250 best subs (Clean)

Figure 6 illustrates the distribution following the first clustering pass. Clusters were ranked by size, ranging from 8,070 subreddits in Cluster 0 to 52 in Cluster 29. A significant volume of data points was identified as outliers due to their high distance from the respective centroids.

Figure 7 demonstrates a clear improvement following the distance-based pruning. By retaining only the subreddits closest to each centroid, we achieved a substantial noise reduction, with most distances falling below a 0.5 threshold.

Following this automated refinement, a manual labeling process was conducted. To facilitate this, the ten subreddits closest to each centroid were analyzed for every cluster. This allowed us to validate the thematic consistency of each group and retain only the clusters representing genuine, well-defined communities.

Then we took all the subreddits from the remaining clusters and ran clustering again leading to these new clusters:

Figure 8: Box plots of the clusters generated from noise (2nd Pass)

New clusters found

• 100: R4R / Personals
• 103: Politics / Academics
• 107: Adult Content
• 108: US States and Cities
• 109: Radical Politics
• 111: Adult Content
• 112: India
• 113: Image Of
• 114: Germany
• 115: News Auto
• 116: Radical Politics
• 117: Sweden
• 120: Russia

Final community map

5. Linguistic variation across Reddit

The Hierarchy of Intent

By aggregating subreddits into their parent topics (based on the hierarchical tree presented above), we observed a "staircase" of linguistic quality. This confirms that communities self-regulate their speech patterns to fit the social norms of their genre.