Temporal Dynamics of Fragmentation in Reddit Meme Stock Communities: A Network Analysis of the GameStop Event

Mei Si — Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY, USA (sim@rpi.edu)

Keywords: Reddit Event Analysis; Semantic Similarity Networks; Social Media Network Fragmentation

Session: Late Breaking Results Pages: 1–4 Conference: HT ’25 Adjunct: Adjunct Proceedings of the 36th ACM Conference on Hypertext and Social Media, Chicago, IL, USA

Abstract

This paper examines how episodic events, such as the GameStop short squeeze, disrupt and reorganize the structure of online discourse. Focusing on Reddit’s meme stock community, we analyze how content-level thematic coherence changes over time by comparing full-period post similarity networks with daily snapshots. We find that, despite widespread lexical diversity, a dominant semantic cluster emerges when data are aggregated, while daily networks reveal sharp spikes in fragmentation and entropy aligned with key moments in the event. These findings demonstrate how structural patterns in online content can reflect collective narrative dynamics, even in the absence of user interaction data.

Introduction

Episodic events often induce rapid and large-scale shifts in online discourse, with significant implications for the structure of social networks. Political protests [4], public health emergencies [3], and financial market anomalies [6] can disrupt existing communication patterns, fragment communities, and reconfigure information flows. The January 2021 GameStop short squeeze—driven by viral coordination within Reddit’s r/WallStreetBets—offers a vivid example. GameStop’s price spike to nearly $500 was propelled not only by trading activity but also by a surge of meme-laden discourse, collective framing, and symbolic resistance [2,5].

While social network analysis has traditionally focused on user-level connections or interaction graphs, content-based structural dynamics remain underexplored in high-volatility episodes. Particularly when user metadata is unavailable or obfuscated, alternative methods are needed to examine how information structures evolve during such disruptions.

In this paper, we analyze the temporal dynamics of content-level fragmentation using semantic similarity networks constructed from Reddit post titles. Rather than modeling interpersonal links, we treat posts as nodes and infer thematic coherence through pairwise text similarity. This allows us to investigate structural signatures of coordination, convergence, and disintegration in discourse, using minimal and privacy-preserving input.

Our approach builds on techniques in community detection and network entropy [1], applying them to temporally segmented content networks across the GameStop event window. By comparing an aggregated similarity network with daily snapshots, we identify a dominant semantic core during the event’s peak and sharp spikes in fragmentation aligned with key moments such as Robinhood’s trading halt (Section3.3). As illustrated in Figures4and5, these structural changes reflect narrative churn and collective disorganization—offering new insights into how online communities reconfigure under pressure.

Methodology

Data Collection

2.1

We used Reddit submission data from the publicly available Kaggle dataset“Reddit WallStreetBets Posts”, covering the period from January 28 to August 16, 2021. The dataset includes 53,187 posts, each with a timestamp, post title, and post score. No author-level information is provided, which restricts the analysis to content-level features and enables a privacy-preserving approach to structural analysis.

Title Similarity Network Construction

2.2

To analyze content-level discourse structure without relying on user interactions, we constructed semantic similarity networks based on Reddit post titles. As detailed in Algorithm 1, titles were first vectorized using TF-IDF, and pairwise cosine similarity was computed. An undirected edge was added between two posts if their similarity exceeded a thresholdθ= 0.5. Each day’s post set yields a daily networkGt= (Vt,Et), where nodes represent post titles and edges reflect thematic alignment.

Algorithm 1. Procedure for constructing title-similarity networks from Reddit post titles.

Algorithm 1. Title similarity network construction.

Temporal Segmentation and Metric Computation

2.3

We segmented the dataset into daily intervals to track how the structure of discourse evolved throughout the GameStop event. For each dayt, we built a similarity network and computed the following structural metrics:

• Post volumeNt

• Number of connected componentsCt

• Size of the largest componentLt

• Entropy of component sizes:

• Average clustering coefficient$bar{C}t$

These metrics capture key aspects of thematic cohesion, fragmentation, and local structure in daily discourse. The full procedure is summarized in Algorithm 1.

Results and Discussion

Temporal Posting Dynamics

3.1

Figure1shows the daily volume of GameStop-related posts on r/WallStreetBets. Activity peaks sharply on January 29, 2021, at the height of the short squeeze and media coverage. Post volume rises from just over 1,000 on January 28 to more than 1,500 on the peak day, then drops rapidly to fewer than 300 per day in early February. This surge and decline reflect the event’s explosive but short-lived impact and motivate our analysis of how the network structure evolved in response.

Daily Post Count

Daily Post Count

Title Similarity Network Structure

3.2

Global Structure of the Aggregated Similarity Network.

3.2.1

To capture overall thematic organization during the event period, we constructed a post similarity network using TF-IDF vectorization and cosine similarity between Reddit post titles. Each node represents a post, and edges connect pairs with similarity scores above a threshold of 0.5. This method captures lexical overlap and repeated phrase structures common in meme-driven discourse.

The resulting network, built from all 53,187 GameStop-related posts, reveals a structure composed of both dense and sparse regions. It contains 5,307 connected components, with a strikingly dominant component comprising 47,790 posts—nearly 90% of the dataset. In contrast, 5,276 components are singletons, accounting for 99.42% of all components but only a small fraction of the total posts.

This structure reflects a heavily repeated core of thematically consistent posts, likely sustained by meme reuse and narrative convergence (e.g., “hold the line”, “GME to the moon”). Surrounding this core is a periphery of outlier posts with little lexical overlap. Figure2visualizes this pattern, showing a densely connected center surrounded by a fringe of disconnected or loosely connected nodes.

Visualization of the title similarity network using TF-IDF cosine similarity (threshold = 0.5) on a randomly sampled 2,500-post subset.

Visualization of the title similarity network using TF-IDF cosine similarity (threshold = 0.5) on a randomly sampled 2,500-post subset.

Sample Post Titles from the Largest Component and Their Relevance

Title	Relevance	Notes
SPCE 81.53% short... Let’s get em bois	High	Meme stock style, short-squeeze narrative
Reviewing my GME investment	Moderate	Personal update, common format
AMC to the moon!	High	Shared language with GME rallying memes
Forced my way into 3 more shares...	High	Personal sacrifice theme, emoji-laden
Quick TA DD on CLOV as of 4/29/21	Peripheral	Meme-stock adjacent, technical format
TAKE YOUR MONEY OUT OF ROBINHOOD...	Moderate	Emotional reaction to trading halt
The DKNG Legislative Gamble	Weak	Unrelated to meme narratives
GME YOLO	High	Classic WallStreetBets framing
RH vs TD	Moderate	Response to brokerage conflict
DCBO... Talk dirty to me	Peripheral	WSB tone, but off-narrative

Content Themes in the Core TF-IDF Cluster.

3.2.2

To further interpret the structure of the dominant component in the similarity network, we examined a sample of high-degree nodes—posts that were most thematically connected to others. As suggested by the network topology in Figure2, this cluster forms the semantic core of the community’s discourse during the peak of the GameStop event.

The titles in this cluster frequently reuse a narrow set of slogans, memes, and emotionally expressive phrases. Common expressions include“GME YOLO,” “AMC to the moon,”and“Let’s get em bois,”which contribute to high lexical overlap and dense connectivity in the similarity graph.

Table1shows a sample of ten representative post titles from this cluster, annotated with relevance and thematic notes. These posts reflect the community’s collective voice: blending performative conviction in meme stocks like GME, AMC, and SPCE with anger at institutional actors and platforms such as Robinhood.

To reinforce these qualitative findings, we generated a word cloud (Figure3) from the titles in the largest TF-IDF component. Prominent words includeGME,Robinhood,AMC,buy,hold, andmoon—terms that are not only frequent but also culturally significant within the r/WallStreetBets community. Their repetition reflects an emotionally charged, meme-driven discourse that goes beyond simple lexical similarity. The prevalence of slang, slogans, and emojis underscores that thematic cohesion in this period was shaped not just by word overlap, but by collective cultural identity and shared narrative framing.

Word cloud generated from post titles in the largest cluster. Common stopwords were removed.

Word cloud generated from post titles in the largest TF-IDF component. Common stopwords were removed.

Together, these results support the interpretation that the central cluster represents a thematically consistent narrative, reinforced by repeated slogans and community rituals.

Temporal Network Fragmentation and Clustering Trends

3.3

Entropy and Clustering Trends Over Time.

3.3.1

To examine discourse evolution during the GameStop event (January 28–August 16), we constructed daily title similarity networks. Four metrics—number of connected components, largest component size, component size entropy, and average clustering coefficient—were computed for each. Figures4and5show these metrics revealing a pronounced fragmentation spike during the peak event window (January 25–28), followed by partial thematic consolidation.

Timeline of structural fragmentation metrics for GameStop-related Reddit posts. Red dashed lines mark key events: GameStop’s peak stock price on Jan 29, trading halts on Jan 28, and the start of post volume decline on Feb 2. Entropy (blue bars) reflects thematic diversity, while the number of components and largest component size capture shifts in network cohesion.

Timeline of structural fragmentation metrics for GameStop-related Reddit posts. Red dashed lines mark key events: GameStop’s peak stock price on Jan 29, trading halts on Jan 28, and the start of post volume decline on Feb 2. Entropy (blue bars) reflects thematic diversity, while the number of components and largest component size capture shifts in network cohesion.

Long-term structural fragmentation metrics for GameStop-related Reddit posts (January–August 2021). Blue bars indicate the entropy of component size distributions, while green and orange lines represent the number of components and largest component size, respectively. Entropy remains elevated even after post volume declines, suggesting persistent thematic disorganization.

Long-term structural fragmentation metrics for GameStop-related Reddit posts (January–August 2021). Blue bars indicate the entropy of component size distributions, while green and orange lines represent the number of components and largest component size, respectively. Entropy remains elevated even after post volume declines, suggesting persistent thematic disorganization.

Entropy (blue bars, Figure4), measuring cluster size distribution in the daily similarity network, peaks with high discourse fragmentation—i.e., when many similarly sized clusters exist and no dominant theme emerges. Entropy rose sharply around the GameStop event’s peak, then gradually declined with discussion consolidation.

Unlike metrics sensitive only to component quantity, entropy reflects the relative balance of cluster sizes. For instance, 100 components, mostly singletons with few large ones, can yield lower entropy than 100 equally sized clusters. Thus, entropy can remain high even with fewer components if thematic diversity persists.

Average clustering coefficient of daily post similarity networks.

Average clustering coefficient of daily post similarity networks.

The average clustering coefficient of daily title similarity networks (Figure6) spiked sharply around January 27–28, coinciding with the GameStop event’s peak. This suggests abundant, mutually reinforcing, and tightly interconnected clusters of thematically similar posts during peak collective attention. Post-peak, the coefficient declined rapidly, remaining near zero subsequently. This drop indicates sparser, more fragmented post similarity, with few tight clusters persisting beyond the initial coordinated discourse. Sustained low clustering through spring and summer reflects a long-tail phase of weakened thematic cohesion and diversified community attention.

Structural Fragmentation on January 28, 2021.

3.3.2

A striking observation is the dramatic drop in the largest connected component’s size on January 29, 2021 (Figure4). On January 28, the network showed high semantic cohesion, with the largest component including most GameStop-related posts. This cohesion collapsed after Robinhood and other brokerages announced restrictions on GME share purchases.

The largest component size plummeted while disconnected components surged, suggesting sudden thematic divergence. Discourse previously aligned by coordinated memes and slogans (e.g., “hold the line”) fragmented into varied reactions like outrage, legal speculation, technical confusion, and emotional venting. This fragmentation is mirrored by the entropy metric, which spiked with the component count, indicating no single dominant theme.

Actual Reddit posts corroborate this shift, like one titledRH allowing limited buying of GME ”(discussing Robinhood’s reversal) and another,GME YOLO update — Jan 28, 2021 ”(users posting massive losses or defiant gains). These examples show how the external trading halt fractured a unified semantic field into competing narratives, structurally captured by the drop in network cohesion.

Long-Term Stability and Disorganization

3.4

Comparing short-term (Jan 28–Feb 28) and long-term (through August 2021) patterns reveals clear phases. Short-term, the network is highly unstable: the largest component collapses and entropy spikes, indicating intense fragmentation followed by slow thematic reorganization. Long-term data, however, shows component count and largest component size stabilizing at low values, while entropy remains erratic. This suggests that despite fewer posts and declined structural cohesion, the remaining discourse remains topically scattered. The system transitions from a large, overloaded community into a smaller, thematically diffuse one.

Conclusion and Future Work

This study investigated how the GameStop short squeeze disrupted thematic structure within Reddit’s meme stock community. Using content-based similarity networks constructed from post titles, we analyzed changes in network fragmentation over time. Our findings reveal a dominant thematic cluster across the full period, contrasted with high entropy and fragmentation during the event peak. The average clustering coefficient dropped sharply after the event, indicating a loss of local coherence as the discourse diversified.

Future Work.Future directions include incorporating Reddit comment threads to capture dialogic structure, comparing across different event types (e.g., political, health-related), and integrating topic modeling or community detection to track narrative shifts within and across clusters.

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