Twitter vs Thomson Reuters: Apple buys Primesense

The rumours had been around for a while, but now it was official – Apple Confirmed the acquisition of 3-D Sensor Startup PrimeSense.

On Thomson Reuters (Eikon) we looked for the news in both the Apple feed (AAPL.O) and in mergers and acquisitions. In both cases, the first article was the one highlighted below which appeared at 06:43:14 (London).

Twitter was significantly faster. The earliest message we ingested into our system was from @allthingsd, note the second image which shows the timestamp at 23:22 (London) on Sunday night.

It’s quite a big time difference. If we missed an earlier article on Eikon, please do let us know.




2 thoughts on “Twitter vs Thomson Reuters: Apple buys Primesense

  1. The question is where this story emerged first. Reuters sources Bloomberg for the article (something they no doubt don’t like to do, and visa versa). So it’s simply a bit of cheap shot to say: hey, Twitter beat Reuters! I’m sure Reuters could easily come up with many examples of the opposite.

    And it’s especially cheap, and also completely misses the point of the comparison, if you don’t compare Twitter to Bloomberg, because we know they were the source of the Reuters story.

    So what you’ve said here is: on one overnight story, someone on Twitter beat a journalist at a big name agency….but maybe not a journalist from a different big name agency! Who knows, maybe the Twitter article is derived from the Bloomberg piece….?

    If you want to play this game of gotcha (and I’m not sure it’s a good game to play), then at least get the ground rules straight.

    • Valid point. However, most users who subscribe to a professional news service would tend to subscribe to just one, certainly at an individual level. So if Reuters was behind both Bloomberg and Twitter does that really help your case?

      Clearly there are many examples where Reuters and Bloomberg are faster/better & more accurate than social data but then that isn’t the point of the exercise (and you would expect this when you pay ~$2k per month). The point is to find the value in social data and where it adds value. While there is hype and examples like the AP hack or Carl Icahn AAPL tweets, we’re looking for more day to day examples of where social is useful. One on its own, one isn’t enough, but we’ll add a few every week as we find them. Hopefully we can start to build a solid, demonstrable argument that social data and particularly Twitter data is truly an invaluable source for trading.


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