The goal of this study is to understand the institutional and cultural relationship between modern American independent film and the Academy Awards by focusing on the rise in success for independent films from 1992-2007. Two are two main approaches implemented throughout the work. The first focuses on the cultural construction of the indie brand on certain films during this time and the second analyzes how a production or distribution company tries to strategize the marketing of their films to boost their Oscar chances. These approaches allow a conversation for the occurrences of when these two meet and provide a coherent film identity I have identified as an “Oscar indie.” Starting with Miramax in the 1990s and ending with the Oscar race of 2007-08 a trend can be found which shows a rise in success of “indie” branded films at the Academy Awards. The implications of this trend are as simple as more “indie” films being released each year and as complicated as changing the face of the American film industry as a whole. / text

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This thesis contributes to the methodology and application of network theory, the study of graphs as a representation of real systems. In particular, we present four essays on problems related to social network analysis, link analysis, and biological network analysis. Chapters 1 and 2 present two pieces of work on social network analysis, where we model and optimize product diffusion through Word-of-Mouth on social networks. Specifically, we use a directed graph and a limiting case of the Erdős–Rényi random graph to respectively model exact and approximate social network structures. We then build mathematical models to describe how information diffuses among connected individuals in these networks. Using our network-based diffusion models, we design algorithms to optimally control product diffusion and maximize revenue from influencer marketing and referral marketing. Chapter 3 explores link analysis of crowd-sourced data on user-item ratings. We represent these ratings with a bipartite network containing user vertices and item vertices. Such a network representation encodes crucial relationship information among users and items that are not apparent from isolated ratings. We propose network-based algorithms to extract useful information from the structure of the bipartite network to predict award outcomes. In using movie ratings data to predict Academy Award nominees and winners, our proposed algorithms significantly outperform other rating-based baselines and state-of-the-art algorithms. Our algorithms can also predict award outcomes and future item popularity in other domains such as books, music, and dramas where user-item ratings are available, without task-specific feature engineering. Chapter 4 is inspired by an application of biological network analysis: learning effective drug combinations, which can be cast as the problem of learning a hidden hypergraph with n vertices and m hyperedges, where a vertex corresponds to a drug and a hyperedge represents a minimal set of drugs that are an effective treatment. We can learn the hidden hyperedges using membership queries: each query corresponds to a test evaluating whether a subset of the drugs is effective. If the query result is positive, then it means that the tested subset contains at least one hyperedge. We propose the first algorithms with poly(n, m) query complexity for learning non-trivial families of hypergraphs that have a super-constant number of edges of super-constant size.

By conducting an explorative study it is tried to determine whether a sample of film enthusiasts can produce a similar result in judging for the 87th Academy Awards for movies in 2014 like the actual Academy members or not. An online survey has been created and the votes cast by the participants have been tabulated. It can be shown that the results of the simulated awards voting in the survey are quite similar to the actual Academy decision. However, additional adjustments and further studies are recommended to ensure the results.

This study examines the affect of winning an Academy Award on the stock price of parent companies. On average, receiving an Oscar has no significant impact on the stock of parent companies during the few days surrounding the broadcast of the Academy Awards. The findings of this study introduce questions of external interference and possible limitations on this type of research. However, my study sheds light on future topics of investigation for analyzing the effects of televised award shows on the stock market.

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By conducting an explorative study it is tried to determine whether a sample of film enthusiasts can produce a similar result in judging for the 87th Academy Awards for movies in 2014 like the actual Academy members or not. An online survey has been created and the votes cast by the participants have been tabulated. It can be shown that the results of the simulated awards voting in the survey are quite similar to the actual Academy decision. However, additional adjustments and further studies are recommended to ensure the results.

No description available.