The rise of Uber

In just a few years, Uber and its service model have become inescapable. Compared to taxis, Uber rides are often significantly cheaper and easier to arrange. However, Uber's popularity has also caused apparent dissatisfaction from taxi drivers and companies, and some people worry about security using Uber. In Europe, Uber has run into numerous challenges, most notably in the form of the French taxi strike aimed at Uber in June 2015.

How were Uber's rise and its challenges covered in the media in 2015? More generally, does the news coverage of Uber and regular taxis reflect their respective strengths and weaknesses?

To analyze these questions, I set up two categories of words. The first category consisted of the word 'Uber' alone. The second category consisted of the words 'taxi' and 'cab', because those two words are largely interchangeable (I included singular and plural, as well as capitalized and uncapitalized versions of the words). I then looked for sentences containing these words in the corpus of all articles published in the New York Times in 2015.

In total, 2008 sentences in this corpus mention 'Uber' (0.08% of all sentences), and 1511 mention 'taxi' or 'cab' (0.06%). Perhaps surprisingly, Uber is the more frequently mentioned of the two categories, with 1.3 mentions for very sentence that mentions taxis or cabs. The words most strongly associated with each category (compared to a baseline of all sentences not mentioning either) are shown below:

 

Associated with Uber Associated with taxis/cabs
black-car, car-hailing, verifications, for-hire, ride-booking, ride-hailing, best-in-class, ride-sharing, misrepresenting, app-based, delves, drivers, on-demand, livery, rides, summon, mapping, sobering, verification, vigilant, checks, wheelchair-accessible, contemplating, intimidation, unicorns, valuation, robotics, stricter, contractors, medallion, congestion, driver, self-driving, third-party, stringent, background, endeavor, fares, rape, kidnapping, class-action, confessed, raped, start-ups, screened, riders, classification, capitalists, operates black-car, dashboard-mounted, livery, tuk-tuks, wheelchair-accessible, limousine, tuk-tuk, for-hire, medallion, medallions, narrates, microcosm, naturalist, hailing, complimentary, ride-sharing, impressively, drivers, ride-hailing, hail, driver, app-based, cocoa, storyteller, boundary, rides, yellow, flagged, stringent, vans, carts, slippery, songwriters, fares, wry, adventures, buses, dashboard, passengers, pretend, hailed, contested, motorcycle, riders, ride, fleet, groceries, fare, crashing, passenger

 

As we would expect, given that Uber is intended to compete directly with regular taxis, many of the words are the same. However, discussions of Uber are more likely to talk about particular features -- ride-booking, app-based (also on the taxi list, but further down), on-demand, contractors -- and specific concerns -- kidnapping, rape -- that are particularly salient for Uber riders. In addition, words associated with discussions of Uber's business model also appear: unicorns, valuation, capitalists

In contrast, the list of words for taxis and cabs are centered more about the taxis and their riders themselves: medallions, drivers, hail, hailing, rides, yellow, flagged, fares, riders, fleet, passenger, etc.

We can also generate a word cloud of the sentences in question. These will show all words in these sentences, not just those that stand out relative to a baseline of unrelated text.

 

Uber
Taxis and cabs

 

The word cloud for Uber further demonstrates the fact that it gets mentioned both as a competitor of regular taxis and as a model of a particular business approach. In the latter category, Lyft and Google now appear, as well as words like employee, data, growth, company/companies, and billions. Moreover, we get a sense of some key cities where Uber is active: New York and San Francisco, but also around the world, such as in New Delhi, India.

Meanwhile, the second word cloud appears more focused both on what taxis do and on New York City: city, street, hotel, driving, people, airport, vehicle, etc., plus New York, Manhattan, mayor, and the Taxi & Limousine Commission.

To improve this analysis, I would look for ways to make sure 'cab' captures only references to taxicabs, and not to cabinets or Cabernet.

 

 

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