A number of algorithms in the field of artificial intelligence offer poorly interpretable decisions. To disclose the reasoning behind such algorithms, their output can be explained by means of so-called evidence-based (or factual) explanations. Alternatively, contrastive and counterfactual explanations justify why the output of the algorithms is not any different and how it could be changed, respectively. It is of crucial importance to bridge the gap between theoretical approaches to contrastive and counterfactual explanation and the corresponding computational frameworks. In this work we conduct a systematic literature review which provides readers with a thorough and reproducible analysis of the interdisciplinary research field under study. We first examine theoretical foundations of contrastive and counterfactual accounts of explanation. Then, we report the state-of-the-art computational frameworks for contrastive and counterfactual explanation generation. In addition, we analyze how grounded such frameworks are on the insights from the inspected theoretical approaches. As a result, we highlight a variety of properties of the approaches under study and reveal a number of shortcomings thereof. Moreover, we define a taxonomy regarding both theoretical and practical approaches to contrastive and counterfactual explanation.

This study presents a novel approach to analyze user interactions on YouTube, addressing the platform's API limitations in capturing comprehensive conversation chains. By combining Large Language Models (LLMs) and rule-based methods, we developed a pipeline to reconstruct comment threads and analyze user stances on controversial topics. We applied this approach to examine immigration debates across 27,000 comments from YouTube channels with varying political leanings. Using the E-I entropy index, we quantified cross-stance interactions. Our findings reveal that “Contra-Immigration” users dominate discussions and exhibit strong in-group communication tendencies, particularly on right-leaning channels. In contrast, “Pro-Immigration” users show a higher propensity to engage with opposing viewpoints. This research provides insights into the complex dynamics of online political discourse and potential polarization on YouTube, while demonstrating the efficacy of our hybrid approach in overcoming technical challenges in social media analysis. The study lays the groundwork for future investigations into the relationships between channel political bias, audience diversity, and user engagement patterns on controversial topics.