Diffusion of Messages from an Electronic Cigarette Brand to Potential Users through Twitter

Objective

This study explores the presence and actions of an electronic cigarette (e-cigarette) brand, Blu, on Twitter to observe how marketing messages are sent and diffused through the retweet (i.e., message forwarding) functionality. Retweet networks enable messages to reach additional Twitter users beyond the sender’s local network. We follow messages from their origin through multiple retweets to identify which messages have more reach, and the different users who are exposed.

Methods

We collected three months of publicly available data from Twitter. A combination of techniques in social network analysis and content analysis were applied to determine the various networks of users who are exposed to e-cigarette messages and how the retweet network can affect which messages spread.

Results

The Blu retweet network expanded during the study period. Analysis of user profiles combined with network cluster analysis showed that messages of certain topics were only circulated within a community of e-cigarette supporters, while other topics spread further, reaching more general Twitter users who may not support or use e-cigarettes.

Conclusions

Retweet networks can serve as proxy filters for marketing messages, as Twitter users decide which messages they will continue to diffuse among their followers. As certain e-cigarette messages extend beyond their point of origin, the audience being exposed expands beyond the e-cigarette community. Potential implications for health education campaigns include utilizing Twitter and targeting important gatekeepers or hubs that would maximize message diffusion.

Below:  Description of the 3-layer retweet network. (A) Layer 0 (Blu) sends the original tweet. (B) This is followed by a Layer 1 user that retweets the message. (C) Finally, a Layer 2 user retweets the retweet.

Below:  The number of users found in each category in Layer 1 and Layer 2 of the retweet network

Below:  The retweet networks of the data collected February to April of 2014.

In the rewteet network, the size of node corresponds to the number of retweets from this particular user and the width of link corresponds to the number of retweets made by the users of the ending node (y) from the users of the starting node (x). Red = Person-Supporter, Blue = Industry-RetailerManufacturer, Yellow = Person-BasicProfile, Cyan = Nonperson, Green = Industry-Other, White = Unknown, Purple = TobaccoControl-Research. (A) Includes users from Layer 1 (i.e., only those who retweeted messages by Blu) and (B) includes all users (i.e. Layer 1 and Layer 2).

Full article at:   http://goo.gl/EFCgno

By:   

Kar-Hai Chu, Jennifer B. Unger, Jon-Patrick Allem, Monica Pattarroyo, Daniel Soto, Tess Boley Cruz

Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America

Haodong Yang, Ling Jiang, Christopher C. Yang

College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America

 

More at:  https://twitter.com/hiv_insight

And: http://twitter.com/Prison_Health