| Nowadays, an increasing number of people with serious diseases can seek and provide social support. Analyzing such on group discussions boosts our understanding of the impact of OHCs as well as the different characteristics of support-oriented interactions. This thesis describes four computational studies that analyze the relationship between linguistic alignment, a universal communication phenomenon, and social support.;Firstly, we introduce a communication phenomenon, linguistic adaptation. Linguistic adaptation in web-based communication means people tend to adjust their language use to one another both in terms of word choice and sentence structure. So far, our understanding of the relationship between linguistic alignment in social support-oriented conversations and its possible connection to member benefits is limited. We quantify linguistic alignment in an OHC at two language levels: word choices and syntactic rules. Our finding results show alignment at both lexical and syntactic level, while the speed of the decay on linguistic adaptation is much slower than previous corpus studies. These different patterns not only can be potentially revealed through alignment theories, but also help researchers understand the unique function of OHCs, i.e. exchanging social support.;As support seekers play a key role in support-oriented conversations, we focus on how community members provide support to support seekers. We construct a computational model analyzing linguistic alignment between support seekers and support providers in OHCs. Surprisingly, our finding results show that lexical alignment and syntactic alignment have distinct correlations to the support functions. This result makes potential theoretical progress, revealing the relationship between different levels of linguistic adaptation. It also motivates further research regarding potential refinement of computational linguistic theories, such as the Interactive Alignment Model, in other web-based conversations.;We further analyze how support providers adapted to each other in such interactions. Specially, we study whether latter social support providers are influenced by early support providers. From the theoretical perspective, as adaptation can occur at lexical, syntactic and pragmatic levels, relation between alignment across multiple levels is neither theoretically nor empirically understood. Thus, in this study, we develop a computational model predicting the support choice given the message from early responders using linguistic alignment measures. We find that community members align on social support type. Also, lexical adaptation, not syntactic adaptation, reliably indicates emotional support. These findings can help us understand linguistic signature of different types of social support, as well as the interactive alignment theory.;Finally, we introduce linguistic adaptation phenomenon into a causal relationship inference framework, probabilistic Kripke structure, quantifying the effectiveness of emotional support. We further analyze the framework by examining prima facie causes, and their significance. The result presents that replies with positive sentiment and high alignment score are consistently prima facie causes of resulting positive sentiment of the thread initiator, which means that linguistic adaptation is a temporal causal factor for high level communication.;Our research makes a potential theoretical progress of the mechanism of linguistic adaptation, especially in web-based conversations. From the applied perspective, this thesis also contributes to a better understanding of the impact of supportoriented interactions for benefiting members in OHCs. |
