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Lucia Walinchus

@walinchus.bsky.social

5K followers 600 following 1.2K posts

Managing Data Editor, NBC owned stations. Public records attorney, journalist, ice hockey player/coach. Former bylines: NY Times , Washington Post, Eye on Ohio, etc. Hearts= likes OR bookmarks

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Lucia Walinchus @walinchus.bsky.social · Nov 20

According datajournalism.com, 60% of data journalists in their last survey were male. And a recent @pewresearch.org paper showed about 2/3 of "news influencers" are men.

So please share this with anyone who might be interested in broader horizons.

Representation matters!

go.bsky.app/8gob51T

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Lucia Walinchus @walinchus.bsky.social · 17h

Ohhhhh so cool

Lucia Walinchus @walinchus.bsky.social · 21h

13/13 And big thanks to @mikestucka.bsky.social and all the @ire.org folks who encouraged me to test this out. I was very against signing up for yet another social media site until I saw what it could do!

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Lucia Walinchus @walinchus.bsky.social · 21h

12/13 Disclaimers! I am not employed by bluesky, nor do I know anyone who is. This is not an endorsement and I have not extensively tested how well this works. Please respect the rate limits; this prevents overwhelming the system.

docs.bsky.app/docs/advance...

Rate Limits | Bluesky

Rate limits help service providers keep the network secure. For example, by limiting the number of requests a user or bot can make in a given time period, it prevents bad actors from brute-forcing cer...

docs.bsky.app

Lucia Walinchus @walinchus.bsky.social · 21h

11/13 This also looks cool: You can build a big selection of follower/following connections from a initial curated list of related people.

@thoughtfulnz.bsky.social

github.com/thoughtfulbl...

GitHub - thoughtfulbloke/bskyrduckplyr: Gather a network from bluesky with bskyr and duckplyr

Gather a network from bluesky with bskyr and duckplyr - thoughtfulbloke/bskyrduckplyr

github.com

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Lucia Walinchus @walinchus.bsky.social · 21h

10/13 This mostly uses the bskyr #rstats package developed by @chriskenny.bsky.social. Check out his vignettes here: github.com/christopherk...

bskyr/vignettes/gathering_data.Rmd at main · christopherkenny/bskyr

Interact with Bluesky Social. Contribute to christopherkenny/bskyr development by creating an account on GitHub.

github.com

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Lucia Walinchus @walinchus.bsky.social · 21h

9/13 If you want to copy this code, I have it here, uploaded to @github.com

github.com/walinchus/Bl...

GitHub - walinchus/Bluesky: Fun things you can do with Bluesky

Fun things you can do with Bluesky. Contribute to walinchus/Bluesky development by creating an account on GitHub.

github.com

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Lucia Walinchus @walinchus.bsky.social · 21h

8/13 This is just scratching the surface. There's a lot of really granular data here. You can have fun with this! HMU with more ideas!

example of data from the bluesky firehose

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Lucia Walinchus @walinchus.bsky.social · 21h

7/13 Another idea: let's say you want to see not just what people like, but what others are recommending to their friends.

For example, this post doesn't have many likes, but already has a lot of reposts. And so neat! I did not know this about hippos.

bsky.app/profile/jcsv...

Jens-Christian Svenning @jcsvenning.bsky.social · 22h

Interesting! Modern hippos lived in Central Europe during the last ice age 🦏🦏🦣https://www.uni-potsdam.de/en/pressreleases/detail/2025-10-09-hippos-lived-in-europe-in-the-last-ice-age #megafauna

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Lucia Walinchus @walinchus.bsky.social · 21h

6/13 This is really fascinating to see the effects of time zones, and when certain news broke!

Chart showing the TF-IDF of words in certain hours

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Lucia Walinchus @walinchus.bsky.social · 21h

5/13 Idea number two: Looking at which words were important in each hour.

To do this, we are going to employ a neat little trick called TF-IDF.

Check out @juliasilge.com's work here for a full explanation but essentially: What words were important in each chunk?

www.tidytextmining.com/tfidf

3 Analyzing word and document frequency: tf-idf | Text Mining with R

A central question in text mining and natural language processing is how to quantify what a document is about. Can we do this by looking at the words that make up the document? One measure of how...

www.tidytextmining.com

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Lucia Walinchus @walinchus.bsky.social · 21h

4/13 Analysis idea number one: what comes up most often?

Here, we take out the most popular words (prepositions, etc.) and look only at the frequency of other words.

stop_words1 <- as_tibble(stopwords::stopwords("ja", source = "marimo")) %>% rename(word=value)
stop_words2 <- as_tibble(stopwords::stopwords("pt", source="snowball")) %>% rename(word=value)
stop_words <- bind_rows(stop_words,stop_words1,stop_words2)

post_words <- posts %>%
unnest_tokens(word, text) %>%
anti_join(stop_words)

post_words %>%
count(word) %>%
arrange(desc(n)) %>%
slice_head( n=100)
```

Then a table: word
<chr>
n
<int>
people 297
trump 258
time 215
2025 181
prize 178
10 176
day 164
peace 163
love 153
nobel 148

Lucia Walinchus @walinchus.bsky.social · 21h

3/13 You can't download all two billion posts, but you can download a whole lot more than you ever could scrolling. And you can use that data to look for patterns.

For example, what if we took a look at 100,000 posts from the last day?

latest_posts <-
bs_search_posts(
"*",
sort = 'latest',
since = '2025-10-10T00:00:00.000Z', #or whatever time you want the latest_posts. I didn't want to hardcode this but couldn't' figure out a way to put the time in that exact format.
until = NULL,
mentions = NULL,
author = NULL,
lang = NULL,
domain = NULL,
url = NULL,
tag = NULL,
cursor = NULL,
limit = 100000, # You will want to limit this!
user = "walinchus.bsky.social",
pass = Sys.getenv("BSKY_PASS"),
auth = bs_auth("walinchus.bsky.social", Sys.getenv("BSKY_PASS")),
clean = TRUE
)

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Lucia Walinchus @walinchus.bsky.social · 21h

2/ First, you're probably thinking: why spend time doing this?

We have this embedded idea that truly great content will always rise to the top. For a detailed discussion on why not, see these.

But the short answer is: quality is part of the equation but not the whole story.
@atrubek.bsky.social

Wheel graphic showing content at the core but also user connections, functional connections, and product connections.

The Content Trap book cover by Bharat Anand

Cover of Anne Trubek's "So You want to Publish a book?"

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Lucia Walinchus @walinchus.bsky.social · 21h

1/13 If the only things you're doing on @bsky.app are scrolling, liking, & posting, then you are still riding a bike with training wheels. Hear me out.

There are several simple and free tools out there which let you take advantage of bluesky's secret weapon: its open-source skeleton.

A how-to🧵:

A few firehose ideas: Pull thousands of posts to look for trends
See how key phrases change over time
See articles
others are
recommending

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Lucia Walinchus @walinchus.bsky.social · 1d

Yikes. J/K it's ~13 hours to go each way and it's $356.

Amtrak price quote for NYC to Toronto on Dec 22- Dec 27

Lucia Walinchus @walinchus.bsky.social · 1d

Yikes. Maybe the train is cheaper?

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Lucia Walinchus @walinchus.bsky.social · 1d

How much is it usually?

Lucia Walinchus @walinchus.bsky.social · 2d

I really appreciate that the @columbuslibrary.bsky.social converted their attendance stats to Ohioan.

In-person library visits were equivalent to 53 sold-out buckeye football games in 2024.

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Lucia Walinchus @walinchus.bsky.social · 4d

Paradise found?

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Lucia Walinchus @walinchus.bsky.social · 4d

Who said scientific articles were easier to read than wikipedia?😂

Lucia Walinchus @walinchus.bsky.social · 5d

Yeah, that’s what I’m saying. They had a team before it just wasn’t D1 which you don’t know from the sign. I think this is great. But also important to acknowledge the many ladies who played for years to build up their program.

Lucia Walinchus @walinchus.bsky.social · 5d

Very awesome. I’m guessing they mean like, first win for their league though? They have had a team, or at least they did have a team a while ago!

Lucia Walinchus @walinchus.bsky.social · 6d

First cousins share grandparents, second cousins share great-grandparents, third cousins share great-great-grandparents, etc.

If you are a generation removed from someone else, regardless of anyone’s age, you add that designation.

Lucia Walinchus @walinchus.bsky.social · 6d

Love this. Also just FYI your cousin’s kid is your 1st cousin once removed. It goes by your closest relative. That would be your grandparents, who for him are great-grandparents. Because you’re in different generations that’s where the “removed” part comes from.

It’s easiest to remember this way:

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Lucia Walinchus @walinchus.bsky.social · 8d

Behind the story: in theory the US is targeting criminals, but the data tells a different story.

@nbcsandiego.com's Shelby Bremer has more:

www.nbcsandiego.com/news/local/i...

Caption reads: 7x arrests with no criminal convictions in San Diego, ICE arrests rise more in San Diego than Across the US

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