Tag: blogging

Top-19 articles of 2019

Top-19 articles of 2019

With only one day remaining in 2019, let’s review the year. 2019 was my third year of blogging and it went by even quicker than the previous two!

Personally, it has been a busy year for me: I started a new job, increased my speaking and teaching activities, bought and moved to my new house, and got married op top of that!

Fortunately, I also started working parttime. This way, I could still reserve some time for learning and sharing my learnings. And sharing I did:

I posted 95 blogs in 2019!
That means one new post every 4 days!

paulvanderlaken.com improved its online footprint as well. We received over 100k visitors in 2019! And many of you subscribed and sticked around. Our little community now includes 55 more members than it did last year! And that is not even including the followers to my new twitter bot Artificial Stupidity!

Thank you for your continued interest!

Join 234 other followers

Now, I am always curious as to what brings you to my website, so let’s have a look at some 2019 statistics (which I downloaded via my new Python scraper).

Most read articles

There is clearly a power distribution in the quantity with which you read my blogs.

Some blogs consistently attract dozens of visitors each day. Others have only handful of visitors over the course of a year.

These are the 19 articles which were most read in 2019. Hyperlinks are included below the bar chart. It’s a nice combination of R programming, machine learning, HR-related materials, and some entertainment (games & gambling) in between.

Which have and haven’t you read?

  1. R resources
  2. R tips and tricks
  3. New to R?
  4. Books for the modern, data-driven HR professional
  5. The house always wins
  6. Visualization innovations
  7. Simple correlation analysis in R
  8. Beating battleships with algorithms and AI
  9. Regular expressions in R
  10. Simpson’s paradox
  11. Visualizing the k-means clustering algorithm
  12. Survival of the best fit
  13. Datasets to practice and learn data science
  14. Identifying dirty twitter bots
  15. Game of Thrones map
  16. Screeps
  17. Northstar
  18. The difference between DS, ML, and AI visualized
  19. Light GBM vs. XGBoost

Rising stars

Half of these most read articles have actually been published in 2017 or ’18 already. However, of the 95 articles published in 2019, some also demonstrate promising visitor patterns:

The People Analytics books, Visual innovations, and AI Battleships are in the top 19, and several others made it too.

Some of these newer blogs haven’t had the time to mature and redeem their place yet though. Regardless, I have high hopes!

Particularly for Neural Synesthesia, which was easily one of my greatest WOW-moments for ML applications in 2019. It’s truly mesmerizing to see a GAN traverse its latent space.

Reading & posting patterns

I have been posting quite regularly throughout the year. Apart from a holiday to Thailand during the start of January, and the start of my new job in February.

While I write and post most of my blogs in the weekend, I guess I should consider postponing publishing. As you guys are mostly active during Tuesdays and Wedsnesdays!

Statistical summary of 2019

What better way to end 2019 than with a statistical summary?

I have posted more and shorter blogs, and you’ve rewarded me with visits and more likes (also per post). However, we need more discussion!

Statistic2018 2019 Δ
Unique visitors5759470615+23%
Words / post518371-40%
As of 29/12/2019

2020 Outlook

It took some time to get started, but halfway 2017 my blog started attracting an audience. People stayed on during 2018, and visitor number continued to increase through 2019.

With an ongoing expansion from R into Python, and an increased focus on sharing resources, applications, and novelties related to data visualization and machine learning, I have a lot more in store for 2020!

I hope you stick around for the ride!

Please like, subscribe, share, and comment, and we’ll make sure 2020 will be at least as interesting and full of (machine) learning as 2019 has been!

Join 234 other followers

Identifying “Dirty” Twitter Bots with R and Python

Past week, I came across two programming initiatives to uncover Twitter bots and one attempt to identify fake Instagram accounts.

Mike Kearney developed the R package botornot which applies machine learning to estimate the probability that a Twitter user is a bot. His default model is a gradient boosted model trained using both users-level (bio, location, number of followers and friends, etc.) and tweets-level information (number of hashtags, mentions, capital letters, etc.). This model is 93.53% accurate when classifying bots and 95.32% accurate when classifying non-bots. His faster model uses only the user-level data and is 91.78% accurate when classifying bots and 92.61% accurate when classifying non-bots. Unfortunately, the models did not classify my account correctly (see below), but you should definitely test yourself and your friends via this Shiny application.

Fun fact: botornot can be integrated with Mike’s rtweet package

Scraping Dirty Bots

At around the same time, I read this very interesting blog by Andy Patel. Annoyed by the fake Twitter accounts that kept liking and sharing his tweets, Andy wrote a Python script called pronbot_search. It’s an iterative search algorithm which Andy seeded with the dozen fake Twitter accounts that he identified originally. Subsequently, the program iterated over the friends and followers of each of these fake users, looking for other accounts displaying similar traits (e.g., similar description, including an URL to a sex-website called “Dirty Tinder”).

Whenever a new account was discovered, it was added to the query list, and the process continued. Because of the Twitter API restrictions, the whole crawling process took literal days before Andy manually terminated it. The results are just amazing:

After a day, the results looked like so. Notice the weird clusters of relationships in this network. [original]
The full bot network uncovered by Andy included 22.000 fake Twitter accounts:

At the end of the weekend of March 10th, Andy had to stop the scraper after running for several days even though he had only processed 18% of the networks of the 22.000 included Twitter bots [original]
The bot network on Twitter is probably enormous! Zooming in on the network, Andy notes that:

Pretty much the same pattern I’d seen after one day of crawling still existed after one week. Just a few of the clusters weren’t “flower” shaped.

Andy Patel, March 2018, link

Zoomed in to a specific part of the network you can see the separate clusters of bots doing little more than liking each others messages. [original]
In his blog, Andy continues to look at all kind of data on these fake accounts. I found most striking that many of these account are years and years old already. Potentially, Twitter can use Mike Kearney’s botornot application to spot and remove them!

Most of the bots in the Dirty Tinder network found by Andy Patel were 3 to 8 years old already. [original]
Andy was nice enough to share the data on these bot accounts here, for you to play with. His Python code is stored in the same github repo and more details around this project you can read in his original blog.

Fake Instagram Accounts

Finally, SRFdata (Timo Grossenbacher) attempted to uncover fake Instagram followers among the 7 million followers in the network of 115 important Swiss Instagram influencers in R. Magi Metrics was used to retrieve information for public Instagram accounts and rvest for private accounts. Next, clear fake accounts (e.g., little followers, following many, no posts, no profile picture, numbers in name) were labelled manually, and approximately 10% of the inspected 1000 accounts appeared fake. Finally, they trained a random forest model to classify fake accounts with a sensitivity (true negative) rate of 77.4% and an overall accuracy of around 94%.

One year of paulvanderlaken.com

One year ago, I registered the domain paulvanderlaken.com with three reasons in mind: (1) I wanted an online environment to store and showcase my pet projects, (2) to share and promote some of the great blogs and research others had been writing, and (3) to show others what I was doing on my path to “data science“. The year has been just amazing. I could not have imagined the amount of positive sentiment I received from friends, family, acquaintances, and old classmates. But, most of all, the nice reactions from complete strangers across the globe! Thank you all so much for the positive response.

To my surprise, some of my stuff actually got read!

Some random stats:

In one year, I wrote 103 blogs which got over 42,000 views by nearly 30,000 visitors. 97.5% of these views occurred in the last six months. Most referrals came via Google (45%), reddit (18%), LinkedIn (8%), Facebook (8%), and Twitter (4%), and my blogs were shared a total of 241 times. Now, 51 people follow my blog, which is best viewed on Tuesdays (31%) and around 15:00h CET (6%).

My views between January 2017 and 2018, made with ggplot2 in R.

Although my personal learning is still the main reason I maintain this blog, I am very glad people seem to enjoy tagging along. Hopefully, I can continue to discover and write about data (analysis) during the coming 12 months. For now, I’d want to thank my readers for their continued interest and, in particular, my girlfriend for coping with the numerous evenings and weekend I have wasted on my pet projects. Nonetheless, it was definitely worth the effort!

Hope to see you again soon,