Understanding online conversations through fractal dimension: a case study linking semantic and structural analysis of reddit posts on AI

Abstract

Abstract Fractal dimension can measure the complexity of a branching structure. Botanical trees for example have more sparse branching in poor environments, and more complex branching in good environments. We hypothesize that the branching structures of online conversations can also use fractal dimension to measure sparse versus complex branching. To test this we measured the fractal dimension of Reddit posts about AI. Posts about purely technical content (e.g. distinctions between different algorithms) had lower fractal dimension than those about social controversies (job loss, racial bias, etc.), suggesting that the controversial conversations had more complex branching structures. A sentiment analysis revealed that social posts had more negative sentiment, consistent with characterizing them as more controversial. We also found that even within each category (social vs technical), higher fractal dimension was associated with more negative sentiment.The fractal model offers further insights when considering its analogous biological models. While it is common to use the metaphor of “conversation tree” we find that fractal metrics reveal a structure closer to Diffusion Limited Growth, found in bacteria colonies, fungi, and rhizomatic plant spread, where “sub-trees” can vary in fractal dimension from the parent. The fractal dimension of social controversy subtrees have a stronger coupling to that of the parent than do the technical subtrees, which has potential implications for the semantic process differences. Overall the application of fractal models to online conversations shows that it allows correlations between structural and semantic aspects, and offers a new way to illuminate the underlying characteristics.