The purpose of this study was to determine if metropolitan newspapers from the Southwest, Midwest and Northeast--the Los Angeles Times, the New York Times, and the Indianapolis Star and News--have made an attempt to predict future developments in computer technology and warn their readers about what changes in society, work and the home might occur. The study also used the four newspapers to see if any regional biases existed that may have influenced the reporting from 1950 through 1980. In both cases, the author used a content analysis technique to examine the article content. In all, 331 articles were analyzed and a Chi-square test was applied to the results to determine if a significant difference existed in "favorable" or "unfavorable" treatment of high-tech news by each of the newspapers. Little research exists in this area, although some studies have been made in the treatment of general science, political and business news. The author found that the Los Angeles Times printed a significantly higher number of "future-oriented" articles, but none of the papers treated computer technology and automation more favorably, in regard to bias, than the others.

Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1983. / Bibliography: leaves 628-705. / Microfiche. / lMaster negative: Microfiche MS33210. / viii, 705 leaves, bound ill. 29 cm

M.A. / The subject of this mini-dissertation is the social assessment of the proposed development of a campsite project by the Amangwane community. This project is proposed for the Cathedral Peak area of the Kwazulu Natal northern Drakensberg in partnership with the local conservation authority KZN Wildlife. As a result of the Business Plan process it was decided that participation of the wider community was necessary to obtain their input in designing the requirements for a successful campsite development. In this way the community needs will be met as far as possible. The purpose of undertaking the social assessment was twofold. Firstly, it was undertaken to establish what social structures exist within the community. This was done in order to identify and analyse the characteristics of the social structures in relation to the identified characteristics of the proposed campsite project and the possible impacts the project might have on the community. Secondly, it undertook to make suggestions in mitigation of potentially negative factors. A qualitative style of research was adopted due to the high level of involvement of the researcher in the community being studied. This close association helped in obtaining first-hand information and a practical, participatory approach, described as participatory action research, was followed. It is notable that no significant negative social impacts were recorded. The most noteworthy finding of the research was that the expectations of the community, in terms of positive impacts, were way beyond what the project could deliver. This is particularly true in terms of job creation. The main mitigation measure suggested in this respect relates to education and information dissemination. A number of other suggestions are also put forward that could positively influence the implementation of the project and the subsequent benefits to the community. / Prof. Tina Uys Prof. Anton Senekal

The rapid rise of digital platforms has transformed communication and information sharing. As social networks become increasingly integral to modern society, social media platforms are motivated to implement algorithms that both enhance user experience and bolster advertising. Yet, the intricate nature of social networks poses significant algorithmic design challenges: How can network data be used to predict node attributes? Which graph representations contain the best prediction power? Of paramount concern is the potential for these algorithms to reinforce biases against marginalized groups. Social networks often mirror societal biases tied to gender, race, socioeconomic status, and other factors. Algorithms that unintentionally enhance these biases can detrimentally affect individuals and broader communities. Recognizing these implications, this dissertation delves into four projects, each addressing distinct aspects of these challenges. Through our investigations, we propose innovative solutions aimed at bolstering the fairness, accuracy, and predictive prowess of social network algorithms.

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.