A Genetic Algorithm to Optimize a Tweet for Retweetability

Twitter is a popular microblogging platform. When users send out messages, other users have the ability to forward these messages to their own subgraph. Most research focuses on increasing retweetability from a node's perspective. Here, we center on improving message style to increase the chance of a message being forwarded. To this end, we simulate an artificial Twitter-like network with nodes deciding deterministically on retweeting a message or not. A genetic algorithm is used to optimize message composition, so that the reach of a message is increased. When analyzing the algorithm's runtime behavior across a set of different node types, we find that the algorithm consistently succeeds in significantly improving the retweetability of a message.

[1]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, WWW '04.

[2]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[3]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[4]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[5]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[6]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[7]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[8]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[9]  Stefan Stieglitz,et al.  Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior , 2012, 2012 45th Hawaii International Conference on System Sciences.

[10]  C. A. Murthy,et al.  Genetic Algorithm with Elitist Model and Its Convergence , 1996, Int. J. Pattern Recognit. Artif. Intell..

[11]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[12]  Brian Mcnair.  An Introduction to Political Communication , 1995 .

[13]  Timothy W. Finin,et al.  Why We Twitter: An Analysis of a Microblogging Community , 2009, WebKDD/SNA-KDD.

[14]  Ralf Herbrich,et al.  Predicting Information Spreading in Twitter , 2010 .