Capturing a diverse range of opinions, sentiments, and topics is essential when selecting training data for statistical and machine learning models, particularly those that require interpretability. Online comments offer a valuable source of public opinion, but they often present a skewed representation, with opposing viewpoints being overrepresented compared to supportive ones. This imbalance can lead to biased models that reinforce stereotypes and reduce fairness and utility. The goal is to ensure that a broad spectrum of opinions and sentiments is reflected in the data, helping mitigate bias and providing a more comprehensive dataset for training. By doing so, we can develop fairer, more transparent models that are better suited for analysing complex social issues. To achieve this, it is crucial to employ effective sampling techniques, such as space-filling sampling on networks, that ensure thorough coverage of various topics and sentiments in online discussions. We will demonstrate this methodology with a simulated case study, and analysing social media comments focusing on online debate around migration. Considering the limitations of existing Italian lexical resources, we will introduce a novel sampling technique that ensures both topic and sentiment are adequately represented in the corpus, enhancing its overall reliability and breadth.