Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets
This work addresses the need for businesses to optimize social media advertising by predicting tweet influence, though it appears incremental as it builds on existing prediction methods.
The paper tackles the problem of predicting the influence of commercial tweets on social media, using a model that combines decoration and meta features to outperform baseline and tweet embedding models, with a practical application for engineering unsuccessful tweets to increase success.
Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For businesses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.