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In many social computing systems, users decide sequentially whether to participate or not and, if they participate, whether to create a piece of content directly (i.e. answering) or to rate existing content contributed by previous users (i.e. voting). We present in this chapter a game-theoretic model that formulates the sequential decision-making of strategic users under the presence of this answering–voting externality. We prove theoretically the existence and uniqueness of a pure strategy equilibrium. We show that there exist advantages for users with higher abilities and for answering earlier. Therefore, the equilibrium exhibits a threshold structure and the threshold for answering gradually increases as answers accumulate. To show the validness of the game-theoretic model, we analyze user behavior data collected from a popular question-and-answer site Stack Overflow and show that the main qualitative predictions of the game-theoretic model match up with observations made from the data. Finally, we formulate the system designer’s problem and abstract several design principles that could potentially guide the design of incentive mechanisms for social computing systems in practice.
Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.
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