In this post i want to focus on the stats I gathered about the distribution of twitter updates. The data simply represents % of the posts made with each service. Twitter-addicts usually use clients and update more frequently so if we would want to track unique users instead of posts ammounts I expect the weight of web-updates would rise dramatically. I'll digg more inside the stats as i move on with my project.
Please note, that "TwitterFeed" actually updates Twitter automatically without user interaction. So as i move on in this post, I'll break data into smaller sections.
| update method | popularity | | web | 46,83% | | im | 5,83% | | txt | 5,17% | | twitterfeed | 6,67% | | twit | 6,50% | | twhirl | 6,33% | | twiterrific | 5,17% | | P3:PeraPeraPrv | 2,33% | | twitterfox | 1,83% | | twinkle | 1,67% | | movattwitter | 1,50% | | other | 10,17% |
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Let's see how it looks like if we compare the services that twitter owns vs. 3d party clients. RSS updates are excluded from the calculation.| update method | Twitter + TXT + IM | 3d party clients | | popularity | 62% | 38% |
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And the most popular Twitter clients in May were:
| update method | popularity | | twhirl | 17,84% | | twiterrific | 14,55% | | twit | 18,3% | | twitterfox | 5,16% | | P3:PeraPeraPrv | 6,57% | | movattwitter | 4,22% | | twinkle | 4,69% | | other | 28,63 |
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Japanise language Twitter clients got an impressive 10,33% share.
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