Matching Items (2)
Filtering by
- Genre: Academic theses
Description
This dissertation examines cultural understandings and lived realities of entrepreneurship across South Africa’s economic landscape, comparing the experiences of Cape Town’s Black entrepreneurs in under-resourced townships to those of White entrepreneurs in the wealthy, high finance business district. Based on 13 months of participant observation and interviews with 60 entrepreneurs, I find major differences between these groups of entrepreneurs, which I explain in three independent analyses that together form this dissertation. The first analysis examines the entrepreneurial motivations of Black entrepreneurs in Khayelitsha, Cape Town’s largest township. This analysis gives insight into expressed cultural values of entrepreneurship beyond a priori neoliberal analytical frameworks. The second analysis compares the material resources that Black entrepreneurs in Khayelitsha and White entrepreneurs in downtown Cape Town require for their businesses, and the mechanisms through which they secure these resources. This analysis demonstrates how historical structures of economic inequality affect entrepreneurial strategies. The third analysis assesses the non-material obstacles and challenges that both Black entrepreneurs in Khayelitsha and White entrepreneurs in wealthy areas of downtown Cape Town face in initiating their business ventures. This analysis highlights the importance of cultural capital to entrepreneurship and explains how non-material obstacles differ for entrepreneurs in different positions of societal power. Taken together, my findings contribute to two long-established lines of anthropological scholarship on entrepreneurship: (1) the moral values and understandings of entrepreneurship, and (2) the strategies and practices of entrepreneurship. I demonstrate the need to expand anthropological understandings of entrepreneurship to better theorize diverse economies, localized understandings and values of entrepreneurship, and the relationship of entrepreneurship to notions of economic justice. Yet, through comparative analysis I also demonstrate that diverse and localized values of entrepreneurship must be considered within the context of societal power structures; such context allows scholars to assess if and how diverse entrepreneurial values have the potential to make broad-scale social and/or cultural change. As such, I argue for the importance of putting these two streams of anthropological research into conversation with one another in order to gain a more holistic understanding of the relationship between the cultural meanings and the practices of entrepreneurship.
ContributorsBeresford, Melissa (Author) / Wutich, Amber (Thesis advisor) / Bernard, H. Russell (Committee member) / Tsuda, Takeyuki (Committee member) / York, Abigail (Committee member) / Arizona State University (Publisher)
Created2018
Description
Social media has become an important means of user-centered information sharing and communications in a gamut of domains, including news consumption, entertainment, marketing, public relations, and many more. The low cost, easy access, and rapid dissemination of information on social media draws a large audience but also exacerbate the wide propagation of disinformation including fake news, i.e., news with intentionally false information. Disinformation on social media is growing fast in volume and can have detrimental societal effects. Despite the importance of this problem, our understanding of disinformation in social media is still limited. Recent advancements of computational approaches on detecting disinformation and fake news have shown some early promising results. Novel challenges are still abundant due to its complexity, diversity, dynamics, multi-modality, and costs of fact-checking or annotation.
Social media data opens the door to interdisciplinary research and allows one to collectively study large-scale human behaviors otherwise impossible. For example, user engagements over information such as news articles, including posting about, commenting on, or recommending the news on social media, contain abundant rich information. Since social media data is big, incomplete, noisy, unstructured, with abundant social relations, solely relying on user engagements can be sensitive to noisy user feedback. To alleviate the problem of limited labeled data, it is important to combine contents and this new (but weak) type of information as supervision signals, i.e., weak social supervision, to advance fake news detection.
The goal of this dissertation is to understand disinformation by proposing and exploiting weak social supervision for learning with little labeled data and effectively detect disinformation via innovative research and novel computational methods. In particular, I investigate learning with weak social supervision for understanding disinformation with the following computational tasks: bringing the heterogeneous social context as auxiliary information for effective fake news detection; discovering explanations of fake news from social media for explainable fake news detection; modeling multi-source of weak social supervision for early fake news detection; and transferring knowledge across domains with adversarial machine learning for cross-domain fake news detection. The findings of the dissertation significantly expand the boundaries of disinformation research and establish a novel paradigm of learning with weak social supervision that has important implications in broad applications in social media.
Social media data opens the door to interdisciplinary research and allows one to collectively study large-scale human behaviors otherwise impossible. For example, user engagements over information such as news articles, including posting about, commenting on, or recommending the news on social media, contain abundant rich information. Since social media data is big, incomplete, noisy, unstructured, with abundant social relations, solely relying on user engagements can be sensitive to noisy user feedback. To alleviate the problem of limited labeled data, it is important to combine contents and this new (but weak) type of information as supervision signals, i.e., weak social supervision, to advance fake news detection.
The goal of this dissertation is to understand disinformation by proposing and exploiting weak social supervision for learning with little labeled data and effectively detect disinformation via innovative research and novel computational methods. In particular, I investigate learning with weak social supervision for understanding disinformation with the following computational tasks: bringing the heterogeneous social context as auxiliary information for effective fake news detection; discovering explanations of fake news from social media for explainable fake news detection; modeling multi-source of weak social supervision for early fake news detection; and transferring knowledge across domains with adversarial machine learning for cross-domain fake news detection. The findings of the dissertation significantly expand the boundaries of disinformation research and establish a novel paradigm of learning with weak social supervision that has important implications in broad applications in social media.
ContributorsShu, Kai (Author) / Liu, Huan (Thesis advisor) / Bernard, H. Russell (Committee member) / Maciejewski, Ross (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2020