Our Conclusions


Candlestick Chart and Interactive Plot

Candlestick Chart

The candlestick chart illustrates the stock’s extreme volatility, with significant price swings reflecting investor sentiment’s rapid shifts. This period is characterised by a sharp increase in trading activity, followed by a noticeable stabilisation and subsequent decline in volume, indicating a return to more typical market conditions after the frenzy.

Interactive Plot

Our interactive plot also displays a distinct correlation between intensified Reddit discussions and pivotal changes in GME’s stock price and trading volume. This pattern underscores the substantial impact of social media sentiment on the stock market, demonstrating how online discussions can drive investor behaviour and significantly influence market dynamics.


Mobilisation Insights

Word Clouds

They served as our initial compass, highlighting prevalent terms and granting us a bird’s-eye view of the dominant discourse. While informative, word clouds merely scratch the surface, offering a glimpse rather than a deep dive.

LDA Analysis

Advancing to LDA provided a more structured understanding of the conversations taking place. It revealed clusters of discussion around topics like “GME,” “squeeze,” and “hold,” which reflect facets of the community’s collective action. However, the true significance of these topics requires context that goes beyond their frequency and distribution.

Sentiment Analysis

Our sentiment analysis painted a narrative of rising and falling emotional tides within the GME dialogue. Peaks in sentiment seemed to walk hand-in-hand with stock price movements, but this visual waltz should not be mistaken for a causal relationship. Multiple unseen variables could be at play, and sentiment is but one actor on a crowded stage.

Ultimately, with the current insights, we can surmise interesting patterns and correlations across the data. Yet, we acknowledge that these are not definitive conclusions.


Further Analysis

While the word cloud, LDA, and sentiment analysis have yielded valuable insights into the GME saga, providing a glimpse into the collective mindset and topics of discussion, there’s room for deeper exploration. If, for example, we had the ability to track individual user behaviour over time, access more granular transaction data, and monitor comment threads in real-time, we could pursue a more comprehensive analysis.


By implementing these, and approaching the data from different angles such as through time-series analysis, or machine learning models we could potentially:

Understanding the speed and magnitude of the market’s reaction to real-time events.

Identify the causative influence, if any, of sentiment on price movements.

Forecast future stock price trends based on current sentiment data.


And thus:

Understand the predictive power of sentiment over stock prices.

Assess the degree to which sentiment reflects broader market trends or specific events.

Provide actionable intelligence for traders and market analysts, enhancing decision-making processes.


With access to longitudinal data, user-level interactions, and the capability to parse the subtleties of sentiment in real-time, we could potentially unravel the causative threads of market movements.

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