A Genetic Algorithm to Optimize a Tweet for Retweetability
This work addresses the challenge of enhancing message virality on social media platforms like Twitter, though it is incremental as it builds on existing optimization methods in a simulated setting.
The paper tackled the problem of optimizing tweet composition to increase retweetability by using a genetic algorithm on a simulated Twitter-like network, finding that the algorithm consistently and significantly improved message reach.
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.