The field of intercultural communication emerged from demonstrated need in the public sector and has roots in cultural anthropology. There is continued need in academic and practitioner domains for improved ways to effectively engage across cultures. To do so, it is necessary to develop approaches that enable a person to take the emic perspective of an intercultural Other. Worldview is a promising concept in several fields, such as anthropology and cross-cultural psychology, but remains undeveloped in the field of intercultural competence. In addition, existing conceptualizations and approaches to identify worldviews are too comprehensive or ambiguous to be useful. The purpose of this project was to propose a novel worldview framework synthesizing existing literature. The resulting construct is constituted by the composite universals, morality, agency, and positionality (MAP). Worldview MAP was applied to intercultural interactions between members of two distinct sociocultural groups working together on a two-week global management project in a multinational organization in Japan. Three research questions focused on identifying intercultural difficulties, worldview assumptions of each party, and relationships between the difficulties and worldviews. Inter-rater reliability was calculated for three morality subdimensions most underdeveloped in the literature. Findings include worldview descriptions for both culture groups across MAP and ways in which worldviews are interconnected with and illuminate three complex intercultural difficulties. Further, five meta-level worldview findings show how implicit worldviews were indirectly revealed in narrative data. Limitations of the study and implications for future work are discussed.

Speculation regarding interstate conflict is of great concern to many, if not, all people. As such, forecasting interstate conflict has been an interest to experts, scholars, government officials, and concerned citizens. Presently, there are two approaches to the problem of conflict forecasting with divergent results. The first tends to use a bird’s eye view with big data to forecast actions while missing the intimate details of the groups it is studying. The other opts for more grounded details of cultural meaning and interpretation, yet struggles in the realm of practical application for forecasting. While outlining issues with both approaches, an important question surfaced: are actions causing interpretations and/or are the interpretations driving actions? In response, the Theory of Narrative Conflict (TNC) is proposed to begin answering these questions. To properly address the complexity of forecasting and of culture, TNC draws from a number of different sources, including narrative theory, systems theory, nationalism, and the expression of these in strategic communication.

As a case study, this dissertation examines positions of both the U.S. and China in the South and East China Seas over five years. Methodologically, this dissertation demonstrates the benefit of content analysis to identify local narratives and both stabilizing and destabilizing events contained in thousands of news articles over a five-year period. Additionally, the use of time series and a Markov analysis both demonstrate usefulness in forecasting. Theoretically, TNC displays the usefulness of narrative theory to forecast both actions driven by narrative and common interpretations after events.

Practically, this dissertation demonstrates that current efforts in the U.S. and China have not resulted in an increased understanding of the other country. Neither media giant demonstrates the capacity to be critical of their own national identity and preferred interpretation of world affairs. In short, the battle for the hearts and minds of foreign persons should be challenged.

A story is defined as "an actor(s) taking action(s) that culminates in a resolution(s)''. I present novel sets of features to facilitate story detection among text via supervised classification and further reveal different forms within stories via unsupervised clustering. First, I investigate the utility of a new set of semantic features compared to standard keyword features combined with statistical features, such as density of part-of-speech (POS) tags and named entities, to develop a story classifier. The proposed semantic features are based on triplets that can be extracted using a shallow parser. Experimental results show that a model of memory-based semantic linguistic features alongside statistical features achieves better accuracy. Next, I further improve the performance of story detection with a novel algorithm which aggregates the triplets producing generalized concepts and relations. A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. The algorithm clusters triplets into generalized concepts by utilizing syntactic criteria based on common contexts and semantic corpus-based statistical criteria based on "contextual synonyms''. Generalized concepts representation of text (1) overcomes surface level differences (which arise when different keywords are used for related concepts) without drift, (2) leads to a higher-level semantic network representation of related stories, and (3) when used as features, they yield a significant (36%) boost in performance for the story detection task. Finally, I implement co-clustering based on generalized concepts/relations to automatically detect story forms. Overlapping generalized concepts and relationships correspond to archetypes/targets and actions that characterize story forms. I perform co-clustering of stories using standard unigrams/bigrams and generalized concepts. I show that the residual error of factorization with concept-based features is significantly lower than the error with standard keyword-based features. I also present qualitative evaluations by a subject matter expert, which suggest that concept-based features yield more coherent, distinctive and interesting story forms compared to those produced by using standard keyword-based features.