Contextualized Conversational Network Dynamics on Social Media
Abstract
Network Science provides a framework to understand the large-scale discussions that happen on social media and their impact on society. However, a standard network model of a conversational network destroys the context that users are interacting within. First, the interactional context is destroyed. The interactional component of context includes the content of the conversation in which the users are interacting. When interactional context is not accounted for, separate discussions are combined into one big network, artificially inflating the number of nodes and edges in the network. This leads to inaccurate information about conversation structure and important actors. Next, the personal context is destroyed. The personal component of context includes the attributes of the users involved, as observed through their self-descriptions. Long-standing social theory of offline social communities such as self-categorization place great importance on personal context. Thus, this context needs to be accounted for to test these theories in the social media setting.
This thesis provides the theory and methodologies needed to account for both interactional and personal contexts which were previously lost in network analysis of social media conversations. Specifically, I study the importance of these contexts as they relate to community dynamics. I find that network structure is indeed dependent on interactional context, indicating that existing non-contextualized analyses could be improved. When investigating personal context, I find that the long-standing theory of self-categorization can be extended from offline social communities to massive online communities, with some important limitations. Taken together, the dynamic contextualized analysis outlined in this thesis furthers our understanding of attribute salience in online interactions. Each of these analyses is performed on multiple case studies, providing both validation and a set of examples used to detail a list of best practices for contextualized network analysis.
Tom Magelinski, PhD
Senior Data Scientist - Information Extraction and Generative AI
I build AI systems that help domain experts understand vast amounts of data through state-of-the-art techniques from natural language processing, generative modeling, graph ML, and network science. I’m particularly interested researching and developing techniques to combine NLP and graph-based approaches to capture complex relationships in unstructured data.