Predicting engagement in online social networks: Challenges and opportunities
It addresses the challenge of engagement prediction for researchers and practitioners in social media, but it is incremental as it primarily reviews and classifies existing work.
This survey article examines the problem of predicting user engagement in online social networks, finding that no universal machine learning algorithm or feature set works well across all network types and noting a lack of adoption of state-of-the-art techniques like neural networks.
Since the introduction of social media, user participation or engagement has received little research attention. In this survey article, we establish the notion of participation in social media and main challenges that researchers may face while exploring this phenomenon. We surveyed a handful of research articles that had been done in this area, and tried to extract, analyze and summarize the techniques performed by the researchers. We classified these works based on our task definitions, and explored the machine learning models that have been used for any kind of participation prediction. We also explored the vast amount of features that have been proven useful, and classified them into categories for better understanding and ease of re-implementation. We have found that the success of a technique mostly depends on the type of the network that has been researched on, and there is no universal machine learning algorithm or feature sets that works reasonably well in all types of social media. There is a lack of attempts in implementing state-of-the-art machine learning techniques like neural networks, and the possibility of transfer learning and domain adaptation has not been explored.