A Dataiku Web App with Twitter and Public Transportation

Data Visualization| Technology| fun | | Pauline Brown

Public transportation and Twitter: what type of relationship? A brief investigation powered by a Dataiku Data Science Studio (DSS) web app.

It all started a few weeks ago when a colleague helped me make a word cloud in Data Science Studio. For my first cloud, I decided to use the RER A twitter stream. Why? Because if there is one place on social media where you can be sure to find strong emotional words, it's definitely on french public transportation Twitter streams.

So on November 19, I started scrapping RER A's mentions on Twitter. In 10 days, I'd gathered approximately 1,500 tweets. This is what I found:

Word Cloud RERA1

The above word cloud shows the 50 most tweeted words for #RERA, #RERA_RATP, @galeRERAfr. Most of the words refer to slow traffic and delays. It is also interesting to note that the station names that appear the most, i.e. Cergy, Poissy, Nanterre, La Défense, are those in the western end of RER A.

These results immediately intrigued me. Was this word cloud giving me noteworthy information on RER A's propensity to delays, or was it hinting at the simple fact that people tweet more when they are upset? I decided to dig a little deeper.

Below, you’ll find two graphs showing tweet activity. The first shows the variations of tweet activity per day...

Tweets per week day

...and the second shows tweet activity per hour.

Tweets per time of day

According to these visual representations of tweet activity, people tweet the most about RER A Monday through Friday between 7am - 10am and 5pm - 7pm. From this information, I can safely conclude that people tweet while they are going or coming from work/home, i.e. when they are in or near the RER A. Let’s dig a little deeper.

Here is graph of the number of tweets per hour:

Tweets per hour

When I first generated this graph I noticed some extreme peaks in activity at seemingly random times. Because these peaks didn't seem to follow a particular pattern (besides morning and evening typical increases in activity), I assumed that they reflected isolated incidents. To be sure, I used DSS to build an interactive web-app that would let me zoom into specific time periods in the "tweets/hour" graph and immediately see the associated word cloud:

Word Cloud web-app

To validate my hypothesis about incidents and tweet volume correlation, I used the web-app to zoom into the morning of November 25th where almost 100 tweets were published in under an hour.

Word Cloud web-app Nov 25

As I had guessed, during the November 25th tweet peak, people's comments were negatively connoted and seemed to reflect a traffic or technical incident: "ralenti" (=slowed), "trafic", "intervention", "repercussion", "defense", "technique", etc.

To be sure this wasn't just a coincidence, I decided to compare this word cloud with that of November 24th, a day on which users only generated 163 tweets (i.e. equivalent to the number of tweets generated in only 2 hours on the 25th).

Word Cloud web-app Nov 24

Sure enough, in this word cloud, the most recurring words are generally positive: “bien” (good), “ponctualité” (punctuality), “bonne” (feminine form of good), etc. Furthermore, in this word cloud, it is hard to pinpoint one underlying subject matter or theme.

My conclusions from this first RER Line A Twitter Triggers investigation is three-fold:

  • People tweet about RER A traffic conditions when they are not happy.... with more data, we'll see if this "unhappiness" is regular or punctual.
  • Tweet activity in volume (regardless of content) is a telling indicator of traffic conditions.
  • Building a web app on DSS is fun and pretty easy, and it's not just for data scientists! Stay tuned for an upcoming blog post on how you can build one too.

Stay tuned!

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