Benjamin Smith Add us as a preferred source on Google

Mastodon @bensstats@fosstodon.org

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Account age

1 yrs

🎁 Free analysisfor Benjamin Smith

🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

Known for

I'm happy to share that my work on Community Detection Algorithms in Social Network Analysis has been published in the Canadian Journal of Statistics!
This work has been quite the journey, starting with from initial exploration to the development an algorithm, an R package, a published abstract, arXiv article, and now, a journal article!
Huge thank you to my co-authors Tyler Pittman and Wei Xu!
h

3 views I'm happy to share that my work on Community Detection Algorithms in Social Network Analysis has been published in the Canadian Journal of Statistics! This work has been quite the journey, starting with from initial exploration to the development an algorithm, an R package, a published abstract, arXiv article, and now, a journal article! Huge thank you to my co-authors Tyler Pittman and Wei Xu! h

Giving our R package repository a makeover before the paper FINALLY publishes.
This hex sticker best represents the algorithm (node + edge betweenness).
Glad we have something to share about it soon.
This research has been a journey!
https://github.com/benyamindsmith/ig.degree.betweenness
#rstats #OpenScience #DataScience

2 views Giving our R package repository a makeover before the paper FINALLY publishes. This hex sticker best represents the algorithm (node + edge betweenness). Glad we have something to share about it soon. This research has been a journey! https://github.com/benyamindsmith/ig.degree.betweenness #rstats #OpenScience #DataScience

WOW!
The ig_degree_betweenness python module has hit 747 downloads just 2 days after its release!
If you work with social network analysis and want to detect clusters with two major popularity metrics, check out the ig_degree_betweenness - available in Python and R!
GitHub repositories in the comments below!
#datascience #python #rstats #igraph #socialnetworkanalysis

1 views WOW! The ig_degree_betweenness python module has hit 747 downloads just 2 days after its release! If you work with social network analysis and want to detect clusters with two major popularity metrics, check out the ig_degree_betweenness - available in Python and R! GitHub repositories in the comments below! #datascience #python #rstats #igraph #socialnetworkanalysis

📊 Post engagement

📊 Activity & format

Posting cadence

0.21 / week

A lower-frequency account — each post lands with more weight.

Content mix

Mostly text

Recent: 9 text · 3 image · 0 video.

Follower / following

1×

Follows 42 back. A more reciprocal / networked account.

Recent posts

View on Mastodon ↗

🔥 Top post Why is it that in 2026 Statistics/ML researchers are not making the code to their papers open source/available for all to see? #RStats #DataScience #Statistics #MachineLearning ★ 14

RE: https://fosstodon.org/@bensstats/116948812726330521 Fool me with the simulation - shame on me, I can maybe figure it out. But Fool Me With the Data ... #RStats #Statistics #Research #MachineLearning ★ 7 RE: https://fosstodon.org/@jonthegeek/116873538350841293 Thank you @jonthegeek and the for the opportunity to contribute to the TidyTuesday project! Happy to see `{fightr}` featured! #rstats #python #datascience ★ 1

I'm happy to share that my work on Community Detection Algorithms in Social Network Analysis has been published in the Canadian Journal of Statistics! This work has been quite the journey, starting with from initial exploration to the dev… ★ 3 @rOpenSci @ufc @Posit @posit_glimpse Check it out! The UFC is about to have one of the wildest data moments in sports: a card at the White House. 🥊📊 So I built fightr, a small R package for MMA analytics that lets you pull updated fight + athlete data and explore win probabilities in R. Pe… ★ 1

Giving our R package repository a makeover before the paper FINALLY publishes. This hex sticker best represents the algorithm (node + edge betweenness). Glad we have something to share about it soon. This research has been a journey! ht… ★ 2 A small manuscript on reviewing a Debaised LASSO approach is out! https://arxiv.org/abs/2604.00848 #machineLearning #arXiv Went back recently to do some work on the {randngen} R package and found this great blog from 2020 by @coolbutuseless https://coolbutuseless.github.io/2020/07/07/the-quality-of-rs-random-number-generators/ ★ 10 Happy to share my 1st journal publication! Check out 'fairmetrics: An R package for group fairness evaluation' doi.org/10.21105/joss.08497 #Rstats ★ 12 Star the ig_degree_betweenness python module repo here: https://github.com/benyamindsmith/ig_degree_betweenness_py Star the {ig.degree.betweenness} R package repo here: https://github.com/benyamindsmith/ig.degree.betweenness/

WOW! The ig_degree_betweenness python module has hit 747 downloads just 2 days after its release! If you work with social network analysis and want to detect clusters with two major popularity metrics, check out the ig_degree_betweenness -… ★ 1

🐘 Community & instance

Home server

fosstodon.org

A open-source software server — its members and audience skew toward that niche, so expect a community-aligned, engaged following.

On Mastodon since

Nov 2024

An established account with real history on the platform.

💡 Facts

🗓️Joined Mastodon in 2024 — 1 year ago.

👁️Averages 6 views per post.

📤Posts about 0.2× per week.

🕵️ Fake follower check

Estimated

59/100

Fair Credibility score

84%

Real Real audience

Low Fake-follower risk

Medium Data confidence

Est. 84% real, active audience · Low fake-follower risk.
Engagement (~0.1× of followers engage each post) is around typical for Mastodon.

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

#rstats enthusiast

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🔀 Audience overlap

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Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

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Benjamin Smith Add us as a preferred source on Google

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🐘 Community & instance

💡 Facts

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