This study introduces a Generational Analysis Framework to examine how parent (P), grandparent (GP), and great-grandparent (GGP) comments influence toxicity transmission in hierarchical online conversations. Rather than assigning a single toxicity label to an entire thread, the framework analytically characterizes whether the terminal comment of a conversation tree is toxic or non-toxic, using generational ancestor features as analytical instruments. We analyze 1.2 million Reddit posts from r/NoNewNormal (June 2020–August 2021), structured into 156 847 conversation trees, from which 23 000 trees with at least four hierarchical levels are identified for P-GP-GGP analysis. Toxicity is detected using Detoxify (RoBERTa-based), and nine machine learning algorithms are evaluated as analytical instruments. Statistical validation includes 95% power analysis, bias assessment via chi-square test ( ${\chi }^2=2.14$, $p=0.34$), and cross-validation. Results reveal context-dependent generational effects: Immediate parents dominate in highly toxic environments (69%–74% accuracy), while great-grandparents are most influential in less toxic contexts (75% accuracy), both outperforming baselines (52%–68%, $p<0.001$). Toxic conversations exhibit more branching (mean: 3.2 vs. 2.1, Cohen’s $d$ = 0.74), greater depth (mean: 5.8 levels vs. 4.1 levels, Cohen’s $d$ = 0.89), higher participation (mean: 23.4 vs. 15.7, Cohen’s $d$ = 0.84), and shorter duration (mean: 18.3 h vs. 34.6 h, Cohen’s $d$ = 1.02). Unexpected transition patterns (23.7% toxic $\to$non-toxic; 31.2% non-toxic $\to$toxic) confirm the explanatory power of the multi-generational framework. However, as findings derive from a single Reddit community, cross-platform validation is necessary before deployment in content moderation applications.