Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users’ intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities.