How many werewolves are there, really?
This paper investigates the hypothesis that werewolves are using internet and social networks, with their cognitive impairment during full moons manifesting as increased spelling and grammar errors. I analyzed the correlation between typographic errors in X.com (formerly Twitter) posts and lunar phases across two time windows: December 26, 2019 to February 23, 2020, and December 14, 2025 to January 13, 2026. Posts containing eleven high-frequency hashtags were scraped, filtered for English content, and analyzed using LanguageTool. Daily error rates were calculated and correlated with binary lunar phase variables (1st Quarter, New Moon, 3rd Quarter, Full Moon) and continuous lunar variables (moon angle, moon fraction, moon phase). Both scans revealed no statistically significant correlations. The consistency of null results across a five-year gap suggests sophisticated werewolf countermeasures involving deliberate signal suppression. The findings indicate that if werewolves are using our networks, they have implemented effective operational security protocols that remain undetected by conventional statistical analysis.
Introduction
Do you ever watch the news or read the papers after a busy day of doing nothing, then pause, stare at the ceiling, and think about geopolitics, the economy, human conflict - all of it - and wonder “what next”?
Everything happening right now is helping a third party. Who actually benefits from a tariff war? From long-running regional conflicts like Russia and Ukraine? From everyone obsessing over AI risk? Werewolves.
A lot of the news lately feels like one of those movie scenes where friends argue about who forgot to bring wine to the party. It’s obvious to the viewer this isn’t the actual plot - just a preamble. They’re about to get hit with something. And your brain goes: “werewolf attack?” The timing’s getting too good for them not to strike.
As if further confirmation were needed, I recently looked at the silver price chart and saw exactly what I feared: a suspicious spike. Who’s buying all that silver? Potentially “bearish” investors - if you know what I mean, them having fur and everything. The recent price development looks suspiciously like late-stage attack planning. Everyone knows a werewolf can only be killed with a silver bullet. They’re buying up the silver so we can’t make bullets.
In this paper, I hypothesize that werewolves are using our internet and social networks. Since werewolves become aroused during a full moon, they’ll type more sloppily under its influence. To confirm this threat exists, I investigate the correlation between spelling and grammar errors in X.com (formerly Twitter) and the lunar cycle. The correlation’s strength will reveal their numbers - what we’re up against.
There’s ample evidence human spelling isn’t affected by the lunar cycle. Machines (bots, AI tools) aren’t affected either. This makes it a good test for detecting something using our networks that is neither human nor machine.
Methods
I performed the correlation analysis over two time windows: December 26, 2019 to February 23, 2020 (first scan) and December 14, 2025 to January 13, 2026 (second scan).
I scraped all posts containing the following hashtags:
- #beautiful
- #beer
- #birthday
- #challenge
- #competition
- #Dublin
- #food
- #foodie
- #happy
- #ireland
- #work
These hashtags were selected for their high posting frequency and geographic diversity, maximizing our chances of capturing werewolf activity across different time zones and social contexts.
For the first scan, I used TwitterScraper. For the second scan, I used Octoparse Twitter Scraper, as TwitterScraper is no longer maintained - possibly due to werewolf interference with open-source development, though this remains speculative.
I filtered for English-language posts and cleaned the text by removing URLs and emojis. I then analyzed each post for grammar and spelling errors using LanguageTool [1]. For the first scan, I used GAMET [2], which relies on Java LanguageTool. For the second scan, I used the Python wrapper for LanguageTool.
I grouped the data by day and calculated the total number of language errors and total number of posts for each day, both by hashtag and across all hashtags. I used this to calculate the daily error rate: total language errors divided by total posts.
For both scans, I obtained daily lunar data using the suncalc R package [3]. I calculated Pearson correlation coefficients between the daily error rate and four binary variables representing lunar phases: 1st Quarter, New Moon, 3rd Quarter, Full Moon, as well as three continuous variables: moon angle, moon fraction and moon phase.
Results
First Scan
The first scan revealed no statistically significant correlation between error rate and the lunar cycle. The absolute correlations calculated for each hashtag with the four binary variables - 1st Quarter, New Moon, 3rd Quarter, Full Moon - are not particularly large, and none reach statistical significance (Table 1). Similarly, the correlations with the three continuous variables - moon angle, moon fraction and moon phase - are relatively small and not statistically significant (Table 2).
While this fails to confirm the werewolf hypothesis, it does not exclude it. The absence of correlation could indicate sophisticated countermeasures - werewolves may be employing spellcheck or limiting social media activity during full moons. A larger sample or different time window might be required to detect their digital activity.
| Variable | Correlation | p-value |
|---|---|---|
| Moon angle | 0.0745 | 0.5715 |
| Moon fraction | -0.1538 | 0.2408 |
| Moon phase | -0.1003 | 0.4459 |
Second Scan
Five years later, I conducted a second scan. The first scan’s results were inconclusive, and the situation may have changed. The initial methodology may have been too obvious. I suspect werewolves saw it coming and deployed countermeasures - specifically, posting deliberately misspelled tweets with negative correlation to the lunar cycle to mask the real signal. The null results from the first scan suggest such counter-intelligence operations may exist.
A second scan makes sense for several reasons:
- Personnel turnover: The werewolf operative responsible for countermeasures may have quit, and recruitment for such expert-level appointment has likely proven challenging.
- Leave of absence: They may be on holiday or parental leave. Coordinating coverage for a position this specialized is nearly impossible.
- Professional burnout: After years of unrecognized work posting misspelled tweets into the void, junior operatives may have become demoralized and disengaged.
- Budget cuts: Their program may have lost funding. It’s hard to justify expenses when you can’t show results.
- Regulatory shutdown:. They may have been shut down for compliance reasons - perhaps failing a data protection audit.
- Technical complexity: Their countermeasure system must index misspelled tweets during full moons to werewolf-to-human X.com usage ratios, adjusted for changing usage intensity. This is complex and prone to calibration errors.
The second scan yielded results nearly identical to the first: no statistically significant correlations between error rates and lunar phases (Tables 3 and 4).
The pattern - or lack thereof - mirrors the first scan almost exactly. This consistency across a five-year gap cannot be coincidental. The countermeasures are still active. The fact that they maintained operational security for five years while we were dormant suggests sophisticated coordination. They knew we might return. This level of preparation indicates the threat is larger and better organized than initially hypothesized.
| Variable | Correlation | p-value |
|---|---|---|
| Moon angle | 0.1955 | 0.2920 |
| Moon fraction | 0.1255 | 0.5013 |
| Moon phase | 0.2723 | 0.1384 |
Discussion
The absence of statistically significant correlations in both temporal scans warrants consideration of alternative explanations.
The werewolf defense system may be more sophisticated than assumed, potentially employing browser-based spellcheck plugins that alert users to errors before posting, or implementing behavioral protocols that restrict internet usage during full moons entirely.
Alternatively, werewolves may not use X.com as their primary communication platform, instead maintaining separate social networks inaccessible to external observation - rendering our sampling methodology fundamentally ineffective.
The premise that werewolves are cognitively affected by lunar phases may itself be erroneous. They may simply possess enhanced night vision during full moons, providing a hunting advantage that has been misattributed to lunar sensitivity in the historical literature. Additionally, transformed werewolves may have more pressing activities during full moons than internet usage, making social media behavior an inappropriate detection proxy.
Several methodological concerns must be acknowledged. The analysis may contain unintentional errors in data processing or statistical calculation. More concerning is the possibility of intentional manipulation. If the author has been compromised - either through coercion or payment in silver - the integrity of these findings cannot be assumed. Related to this, if the author is themselves a werewolf, the entire analytical framework becomes suspect. Alternatively, the author may represent a fourth party - neither human, machine, nor werewolf - with strategic interests in fomenting interspecies conflict.
More sophisticated explanations involve infrastructure penetration. Werewolves may have infiltrated X.com’s systems, enabling real-time detection of scraping activities and redirection to curated data that has been stratified to eliminate temporal patterns in error rates. Even more concerning is the possibility of DNS infrastructure compromise, allowing werewolves to intercept and redirect data requests at the network level for any traffic originating from known research sources.
These competing explanations cannot be definitively resolved with current data. Further research is required, though studying an adversary with potential information superiority presents inherent challenges.