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Reading: Twitter Analytics: Bio information and popularity
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SmartData Collective > Big Data > Data Mining > Twitter Analytics: Bio information and popularity
Data Mining

Twitter Analytics: Bio information and popularity

ThemosKalafatis
Last updated: 2009/05/18 at 1:03 PM
ThemosKalafatis
5 Min Read
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In the previous post we identified words used in Tweets that appear to be associated with low number of followers: We found that when someone uses foul or negative language then his/her follower count appears to be affected negatively (see here for more).

It is time to identify the words contained in the biographies of popular Twitter users and to be more specific the biographies of users being in the top 30% (in terms of number of followers) of a random sample of 10,000 users. As I always have stated in these series of posts: Treat results as possible clues only. Please also notice how I used (in this and older posts) the words “appears” or “were found” when discussing correlation. The technique shown is the same as discussed in the previous post. Results are as follows :

  • Student appears to be correlated with low popularity accounts.
  • Engineer also appears to exist often in low popularity accounts although the correlation was not found to be as strong as for students.
  • Common words existing in popular users Bio appear to be the following: social, media, marketing, CEO, founder, author, entrepreneur, blog, twitter, news, writer, internet.

Some comments:

  • It is not suggested that by …

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In the previous post we identified words used in Tweets that appear to be associated with low number of followers: We found that when someone uses foul or negative language then his/her follower count appears to be affected negatively (see here for more).

It is time to identify the words contained in the biographies of popular Twitter users and to be more specific the biographies of users being in the top 30% (in terms of number of followers) of a random sample of 10,000 users. As I always have stated in these series of posts: Treat results as possible clues only. Please also notice how I used (in this and older posts) the words “appears” or “were found” when discussing correlation. The technique shown is the same as discussed in the previous post. Results are as follows :

  • Student appears to be correlated with low popularity accounts.
  • Engineer also appears to exist often in low popularity accounts although the correlation was not found to be as strong as for students.
  • Common words existing in popular users Bio appear to be the following: social, media, marketing, CEO, founder, author, entrepreneur, blog, twitter, news, writer, internet.

Some comments:

  • It is not suggested that by having specific words in your bio, you will get more followers. Many other things are and could be important in achieving a high follower count. Same applies for unpopular accounts.
  • Looking at the results i wondered why students were found to be associated with low follower numbers and i think that this requires more attention. One possible reason could be that students might be spending most of their social media time on FaceBook or other SM sites. There can be many pitfalls in performing a random sampling from Twitter and “Students” could be one of these cases. However, please share your comments.
  • Notice that some words that appear to be associated with high follower numbers are words that communicate authority ( ex. founder, CEO).

To recap from the last 3 posts:

1) Do not use foul language – keep your conversations positive.
2) Use “Thank you” often. “Stay tuned” seems to work well also.
3) Post frequently. Posting some links is also important.
4) Make sure you have a good Bio filled in.

Finally, if you find the contents of this blog interesting you can always have a look for more updates on my new account on Twitter @lifeanalytics and also send me your suggestions and/or comments.

Link to original post

TAGGED: random sampling, twitter analytics
ThemosKalafatis May 18, 2009
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