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, 12 tweets, 4 min read
Over ten years ago, @guardian published a story about cults and their charismatic leaders. They included a list of behaviors that should be warning signs of an unsafe group or leader.

I'll list them in this thread, along with sample news stories about Trump that align with them.
1) Absolute authoritarianism without meaningful accountability.

Hitler, Mussolini, and Mao Zedong curated dysfunction in their respective governments, in order to gain power.

foreignpolicy.com/2019/10/02/the…
2) No tolerance for questions or critical inquiry.

Trump's constant railing at the impeachment process itself betrays his intolerance for critical inquiry, as discussed in this Situation Room clip.

3) No meaningful financial disclosure regarding budget or expenses, such as an independently audited financial statement.

He famously promised to disclose his returns, and then fought like a rabid animal to prevent their lawful disclosure to Congress.

nbcnews.com/politics/donal…
4) Unreasonable fear about the outside world, such as impending catastrophe, evil conspiracies and persecutions.

The resident has regularly promoted thoroughly-debunked conspiracy theories. Some more outlandish than others.

businessinsider.com/donald-trump-c…
5) There is no legitimate reason to leave, former followers are always wrong in leaving, negative or even evil.

Don McGahn is just one example in a long list of targets for Trump's ire.

politico.com/story/2019/05/…
6) Former members often relate the same stories of abuse and reflect a similar pattern of grievances.

Some went on to write their own books (like the infamous op-ed "resistance" author), and others contributed to accounts like this one:

theatlantic.com/ideas/archive/…
7) There are records, books, news articles, or broadcast reports that document the abuses of the group/leader.

I'll just refer you to the brilliant "What the Fuck Just Happened Today", which aggregates all that fuckery daily.

whatthefuckjusthappenedtoday.com
8) Followers feel they can never be "good enough".

I'll admit, I couldn't find any specific examples of this one in the news.

I spent some time looking for Fox and Friends sycophancy in action and decided that worked. So, you know, watch their show. I'm not linking to it.
9) The group/leader is always right.

See replies to Trump tweets, and the endless parade of surrogates breathlessly defending even his most ridiculous gaffes.

Oh, and Lindsey Graham.

huffpost.com/entry/trump-ly…
10) The group/leader is the exclusive means of knowing "truth" or receiving validation, no other process of discovery is really acceptable or credible.

Remember this one? "What you're seeing and what you're reading is not what's happening."

esquire.com/news-politics/…
Original story from @Guardian, dated May 27, 2009:

theguardian.com/commentisfree/…
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➖Dustin Miller➖
@spdustin
Update on Part 2 of my article series discussing #NLP/data science analysis of the Mueller Report:

• new "clean" source of OCRed text found, and will be used for all further articles in the series
• I found an excellent "coreference" tool that I will demonstrate and train

1/6
"coreference": when a noun (e.g. full name) is used once in a paragraph, and that noun is referred to later using another form (e.g. last name, or pronoun). This screenshot from a web-based demo shows how three "coreferences" have been de-referenced to their root.

2/6 Screenshot showing the phrase
"Manafort" and "He" are shown with arrows pointing to "Paul Manafort", indicating that those words were de-referenced back to the full name." src="/images/1px.png" data-src="https://pbs.twimg.com/media/D5I9_PiWsAAtSaH.jpg">
I have trained this particular tool using the list of references provided at the end of the report, and it works well.

• de-referencing relative dates, and how I did it
• showing only relevant tweets from the correlated timeline using simple text classification tricks

3/6
Read 6 tweets
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➖Dustin Miller➖
@spdustin
Mueller Report #NLP analysis, live update: I've started cleaning up the code, and writing my article.

For my non-tech tweeps: the article will include code, but I promise to explain it all! Plus, you'll see the promised timeline of events from the report, tweets, and more!

1/5
For my geek followers: here's what to expect: the article is being written as a Jupyter notebook running with a Python 3.7 kernel. I'll include instructions to set up a venv with a separate kernel, if that's how you roll. I'll also include the requirements.txt.

2/5
If you're using the Anaconda Python distribution (which is awesome), you can run Jupyter in a new conda environment too; instructions will be provided. The notebook and associated artifacts will be posted to GitLab.

3/5
Read 9 tweets
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➖Dustin Miller➖
@spdustin
Today, as I get back to the analysis exercise referenced in the old thread below, I’ll be updating here, in a new one.

First step: “fish or cut bait” on the full extraction and linking of footnotes. I may punt it to later…but I have a few ideas I’ll try

Then: the ethics of using machine learning approaches to guess at what will fit under the redaction boxes.

Short answer: technically feasible (for some definition) with language models that are trained with every indictment, sentencing memo, etc. from Mueller. Ethically wrong.
I’ve been reading a number of posts/pages on the topic, and now that I’m well past the honeymoon portion of this endeavor, the novelty of having a model make even one successful prediction has lost its appeal.

Ethics—now, more than ever—matter.

I’ll share some links later. 👍
Read 21 tweets

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One of the most dangerous ideas ever created by Boomers is the idea that you need to spend your twenties fucking around before you meet the perfect woman (or man). All this does is make it exponentially more likely that you'll end up alone or in a dysfunctional marriage.
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Here is a thread to help people understand the growth of the #gigeconomy in #highereducation in the U.S., or what Kim Moody calls the “proletarianization” of college teachers.
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2) Federal student loans & grants spurred the “massification” of higher education in the 1960s-70s, which dramatically increased access to college for ordinary Americans. Recessions and tax revolts in the 1970s-80s made maintaining access more difficult. web.stanford.edu/group/ncpi/doc…
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