Week 3: CCK11 Blog & Tweet Analysis, Part One

Integration of the Results of Social Network Analysis in Gephi

Completely fascinated by the data sets when I realized the oppositional nature of tweet mentions and blog comments. In a sense a tweet mention is a specific reference connecting peers with a subject, while Blog comments are subjects asking peers to comment. The latter driven by the text’s content. The former driven by the author’s interest to communicate.

See the images from this Reflective Report analysis in “Week 3 : Assignment : CCK11 Blog & Tweet Images, Part Two”

Analyzing Tweets and Blog posts separately.

A quick analysis of ID and degree show both networks (blog & tweet) exhibit one or more of the characteristics of:

  • cliques formation
    • tweet authors mentioning peers
    • blog authors gaining followers
  • augmenting other social interactions
    • matchmaking peers
    • blog text propagation among different peers
  • or something else

Degree metrics by themselves can not indicate the quality of a communication. For example a tweet can be sent after a meeting referencing the meeting’s content. But a tweet can also be sent before simply as a mention or reminder. In the case of blog text, content may be seen as novelty. As peers communicate among themselves, retweets can represent agreement with a message, but also the spreading of novelty.

Tableau Statistical Analysis

In the blog data you can see there is a small subset (< 16) who are increasingly drawing comments. High-comment authors early in the CCK MOOC at week 6 are still high-comment in the latter view, but others have caught up. Overall, activity appears to have increased in total volume.

The same total increase in the volume of connections is apparent with Twitter. Comparing Week 6 to 12 a simple chart shows the top tweeters with 2-4 times increase in their degree for the same period. Many of the same individuals continue with high mentions.

For Further Investigations

Other ideas for comparing blog and tweet activity could include:

  1. Is there more tweeting or more blogging at the end of the class? (more content or more connecting?)
    This might not be indicative of peer communications with only two snapshots. At the end of any lengthy social activity, peers are inclined to connect more. Twitter might spike at the end of the course. More granular data is necessary to determine this quality of peer behavior.
  2. Are the same individuals still communicating together at the end of week 12? Have participants created connections? Only the original data owner can say for certain.

Original assignment description: Week 3, Assignment Bank