We've had a visitor.
Who by the way can't spell for jack shit.
Can't even be bothered to use spell check.
Several red lines after i copy and pasted from his "research" what a moran,. kek
He took each month of Q drops and made a colorful word graphic showing most used words for that month. That's the only thing good about his Q project.
It was another hit piece that does not really explain how Q and Q research works.
Edward Tian
Edward Tian is a computer science and journalism student at Princeton University. He previously worked at a tech company in Kenya, and has interests in the MENA region, and refugee and asylum law, particularly in the U.S. and Canada
I also share some really interesting things we found on Qanon while investigating 8kun!
https://twitter.com/edward_the6/status/1349898843991777280
Bellingcat researchers have analysed a dataset containing 4,952 so-called “Q drops”, the cryptic messages that are at the heart of the conspiracy theory. These were posted by an anonymous person known simply as “Q” — whom followers believe to be a source of insider knowledge about US politics. Whenever a Q drop appears, believers around the world eagerly try to interpret its hidden meaning, connecting them to real world events.
The Q drop dataset was found on the image board 8kun, which was used by Q followers as a location to comment on Q drops. It contains posts dating back to October 2017, a time when QAnon theories were a fringe online hobby, and continues until October 2020 — by which time they were taken all too seriously.
The word clouds in the timeline show the most common words found in the Q drops over one- or three-month intervals, while the accompanying text discusses important developments during these periods, both in US politics and significant Q drops.
This chronology brings quantitative and qualitative analysis to the story of QAnon’s growth, enriching our understanding of how the conspiracy adapts to contradictions and evolves its narratives.
Methodology
This investigation illustrates key developments and discussions in the QAnon conspiracy theory over time. To identify these trends, we split the data into subsets of one to three month intervals with an average of 290 Q drops.
For each subset, we ran a clustering algorithm that grouped sentences with a similar sentiment together. Using the results of the clustering, we then summarised major topics and notable developments for each time period.
“Sentiment” was evaluated using the Universal Sentence Encoder, an academically recognised text classification model that converted each Q drop into an array of numbers — a vector — based on its meaning.
Q drops with similar meanings have similar vectors. The closeness of two vectors can be calculated by taking their dot product. Thus we were able to evaluate the “closeness” in sentiment between sentences in order to categorise the text of each Q drop.
https://www.bellingcat.com/news/americas/2021/01/29/the-qanon-timeline/