Social Media, Corporate Decisions and Analytics

June 8, 2009
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Over the past six months we have seen real-world applications of data and text mining applied on social media data from Twitter. We went through many examples that look at social media data in different ways:

  • We identified what Twitter users don’t want, grouped their beliefs and also ordered all of this information accordingly.
  • We identified which usage behavior increases our chances of having a large number of followers (if a large number of followers is our goal).

  • We found which words appear to be associated with a large number of followers. (We have seen that negative thinking and words in Tweets possibly drive people away.)

  • We extracted segments of Twitter users with similar characteristics.

The list of possible applications does not end here. Over the next posts we will also discuss about:

  • Predicting whether a Tweet has the potential to become “viral.”
  • Associating specific events and user emotional states.

To recap: A computer program is able to monitor the words – phrases that you say and your emotions, flag them as positive or negative, track the rate with which you increase your follower count, track the number of updates, Re-Tweets, replies, hashtags, smileys and questions that.

Over the past six months we have seen real-world applications of data and text mining applied on social media data from Twitter. We went through many examples that look at social media data in different ways:

  • We identified what Twitter users don’t want, grouped their beliefs and also ordered all of this information accordingly.
  • We identified which usage behavior increases our chances of having a large number of followers (if a large number of followers is our goal).

  • We found which words appear to be associated with a large number of followers. (We have seen that negative thinking and words in Tweets possibly drive people away.)

  • We extracted segments of Twitter users with similar characteristics.

The list of possible applications does not end here. Over the next posts we will also discuss about:

  • Predicting whether a Tweet has the potential to become “viral.”
  • Associating specific events and user emotional states.

To recap: A computer program is able to monitor the words – phrases that you say and your emotions, flag them as positive or negative, track the rate with which you increase your follower count, track the number of updates, Re-Tweets, replies, hashtags, smileys and questions that you make, flags any mentions about products and services and assigns you to a predefined segment of users sharing similar behavior and interests. Then for each segment its “social media fitness value” is identified (by looking at the follower count).

Usage of Google Wave will possibly reveal other insights: Due to the fact that the sequence of posts will be easily extracted then we could also take under consideration the number of consecutive posts who had a positive sentiment and whether these positive posts appeared at the beginning, center or the end of each thread’s sequence. We could also look at the number of posts -that are part of the same thread- having videos or pictures attached and ultimately identify how all of this information may affect one’s point of view. Of course I am not certain whether such a scenario could prove useful. I sure would like to try though.

We are presented with a unique opportunity to understand people much better than before and with the examples shown so far this should be more clear by now. Predictive analytics is about extracting knowledge and identifying what is more likely to work. As Ian Ayres put it in his book Super Crunchers, decisions are beginning to be based even more on facts and less on intuition. It appears that social media analytics will play an important role in making Corporate decisions for PR, branding and marketing and this will happen through better understanding of human behavior.

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