Queries for which there are no clicks are known as abandoned queries. Differentiating between good and bad abandonment queries has become an important task in search engine evaluation since it allows for better measurement of search engine features that do not require users to click. Examples of these features include answers on the SERP and detailed Web result snippets. In this paper, we investigate how sequences of user interactions on the SERP differ between good and bad abandonment. To do this, we study the behavior patterns on a labeled dataset of abandoned queries and find that they differ in several ways, such as in the number of user interactions and the nature of those interactions. Based on this insight, we frame good abandonment detection as a sequence classification problem. We use a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to model the sequence of user interactions and show that it performs significantly better than other baselines when detecting good abandonment, achieving 71% accuracy. Our findings have implications for search engine evaluation.