Conspiracy theories have been present within American culture for hundreds of years. In the hyper-visibility provided by online spaces within the past few years, however, it now feels as if conspiracy theories are everywhere. This proliferation means that, more than ever, it’s imperative that we are equipped to evaluate which conspiracy theories are worth believing in. This paper will explore the qualities of conspiracy theories that should affect how we perceive their credibility. I’ll highlight some similarities shared between conspiracy theories and gossip, and then subsequently apply epistemic concerns relating to gossip to our understanding of conspiracy theories. Two of these concerns – irrelevant influences and the composite hypothesis – will be explored in depth. Although the effects of irrelevant influences and the composite hypothesis are not necessarily defeating for belief in all conspiracy theories, I will highlight the specific ways in which these two features may operate to reduce the credibility. Despite conspiracy theories’ tendency to have these epistemically concerning factors, I am not in favor of an approach that assumes all conspiracy theories are bunkum. Instead, I will argue for an approach in favor of analyzing conspiracy theories on an individual basis and following the evidence in order to assess credibility.

Distributed Gossip in networks is a well studied and observed problem which can be accomplished using different gossiping styles. This work focusses on the development, analysis and evaluation of a novel in-situ distributed gossip protocol framework design called (INDIGO). A core aspect of INDIGO is its ability to execute on a simulation setup as well as a system testbed setup in a seamless manner allowing easy portability. The evaluations focus on application of INDIGO to solve problems such as distributed average consensus, distributed seismic event location and lastly distributed seismic tomography. The results obtained herein validate the efficacy and reliability of INDIGO.

Thesis (M.A.)--The Master's College, 2005. / Includes bibliographical references (leaves [125]-128).

Thesis (M.A.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains iii, 29 p. Includes abstract. Includes bibliographical references (p. 22-25).

L’explosion récente des volumes de données disponibles a fait de la complexité algorithmique un élément central des méthodes d’apprentissage automatique. Les algorithmes d’optimisation stochastique ainsi que les méthodes distribuées et décentralisées ont été largement développés durant les dix dernières années. Ces méthodes ont permis de faciliter le passage à l’échelle pour optimiser des risques empiriques dont la formulation est séparable en les observations associées. Pourtant, dans de nombreux problèmes d’apprentissage statistique, l’estimation précise du risque s’effectue à l’aide de U-statistiques, des fonctions des données prenant la forme de moyennes sur des d-uplets. Nous nous intéressons tout d’abord au problème de l’échantillonnage pour la minimisation du risque empirique. Nous montrons que le risque peut être remplacé par un estimateur de Monte-Carlo, intitulé U-statistique incomplète, basé sur seulement O(n) termes et permettant de conserver un taux d’apprentissage du même ordre. Nous établissons des bornes sur l’erreur d’approximation du U-processus et les simulations numériques mettent en évidence l’avantage d’une telle technique d’échantillonnage. Nous portons par la suite notre attention sur l’estimation décentralisée, où les observations sont désormais distribuées sur un réseau connexe. Nous élaborons des algorithmes dits gossip, dans des cadres synchrones et asynchrones, qui diffusent les observations tout en maintenant des estimateurs locaux de la U-statistique à estimer. Nous démontrons la convergence de ces algorithmes avec des dépendances explicites en les données et la topologie du réseau. Enfin, nous traitons de l’optimisation décentralisée de fonctions dépendant de paires d’observations. De même que pour l’estimation, nos méthodes sont basées sur la concomitance de la propagation des observations et l’optimisation local du risque. Notre analyse théorique souligne que ces méthodes conservent une vitesse de convergence du même ordre que dans le cas centralisé. Les expériences numériques confirment l’intérêt pratique de notre approche. / With the increasing availability of large amounts of data, computational complexity has become a keystone of many machine learning algorithms. Stochastic optimization algorithms and distributed/decentralized methods have been widely studied over the last decade and provide increased scalability for optimizing an empirical risk that is separable in the data sample. Yet, in a wide range of statistical learning problems, the risk is accurately estimated by U-statistics, i.e., functionals of the training data with low variance that take the form of averages over d-tuples. We first tackle the problem of sampling for the empirical risk minimization problem. We show that empirical risks can be replaced by drastically computationally simpler Monte-Carlo estimates based on O(n) terms only, usually referred to as incomplete U-statistics, without damaging the learning rate. We establish uniform deviation results and numerical examples show that such approach surpasses more naive subsampling techniques. We then focus on the decentralized estimation topic, where the data sample is distributed over a connected network. We introduce new synchronous and asynchronous randomized gossip algorithms which simultaneously propagate data across the network and maintain local estimates of the U-statistic of interest. We establish convergence rate bounds with explicit data and network dependent terms. Finally, we deal with the decentralized optimization of functions that depend on pairs of observations. Similarly to the estimation case, we introduce a method based on concurrent local updates and data propagation. Our theoretical analysis reveals that the proposed algorithms preserve the convergence rate of centralized dual averaging up to an additive bias term. Our simulations illustrate the practical interest of our approach.

This dissertation addresses the industrial history of American-based celebrity gossip over century, beginning with the first Hollywood stars in the 1910s and reaching into “celebrified” culture of the 2010s. Gossip, broadly defined as discourse about a public figure produced and distributed for profit, can operate within the star’s good graces or completely outside of the Hollywood machine; it can be published in “old media” print and broadcast forms or online and on a phone. Regardless of form, tone, and content, gossip remains a crucial component of the ways in which star images are produced and consumed. The dissertation thus asks: how has the relationship between the gossip industry and Hollywood in general changed over the last century? And what implications do those changes have for stars, those who exploit their images, and media industries at large? / Not available / text

Thesis advisor: Ted Gaiser / This study explores the alteration of gossip as a result of new technology. Specifically, this study examines the social implications of Facebook, a popular social networking website, on college students using the Boston College undergraduate population as a lens to study the college student population in general. Drawing from the theories of Simmel, Mead, and Goffman, and others, I outline how college students present themselves on Facebook’s online environment. I employed a mixed-method research approach, collecting data from a survey of Boston College undergraduates and, subsequently, conducting a series of in-depth face-to-face interviews in order to gain an understanding of how Facebook altered the social scene and, specifically, how Facebook affects gossip. Facebook is a communication tool widely used by college students in order to present themselves online and maintain relationships. I found that due to the pervasive nature of Facebook, in junction with the simplicity of posting information about oneself and others on Facebook, an important shift occurs in which private matters are publicized to a large audience. This shift facilitates the flow of gossip amongst college students. / Thesis (BA) — Boston College, 2010. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Sociology Honors Program. / Discipline: Sociology.

In the digital age where newspapers compete with the Internet, cable TV and other publications for an audience, USA Today strives to stay relevant in the media with a daily dose of celebrity news. Newspapers continue to lose circulation during a time when the fascination with celebrities shows no signs of dwindling. This study explores how much celebrity news coverage USA Today gives readers, how much competition from other outlets plays a factor and whether the nation's largest newspaper is making a sacrifice of traditional forms of newspaper content in favor of celebrity coverage. The methodology for this qualitative case study is a two-fold approach. In-depth interviews with sixteen managers, editors and staffers at USA Today were conducted, using questions that gather an overview of the newspaper's celebrity news approach. The interviews were transcribed and analyzed in the findings. The second part was a one-year analysis of USA Today's main front and Life section front pages, looking for patterns of celebrity news. The study shows clear differences between USA Today and its non-newspaper competitors. USA Today's newsroom has a strong culture of journalism ethics and standards that limits the newspaper from going head to head with tabloids and celebrity magazines for the rumor and gossip stories. Among them is a strict sourcing policy that forbids blind or anonymous sources in celebrity coverage. Nearly all of the interviewees questioned about competition and gossip mentioned the ethical standards at the newspaper. The analysis of news fronts shows that USA Today uses the skybox in the upper right-hand corner as a way to promote its celebrity news. The majority of days, a celebrity photo and teaser were in that space, something a high-ranking editor at the newspaper said is a conscious effort to showcase celebrities. The Life front pages were loaded with celebrity news, including stories one can argue are tabloid-like in nature. Most of those interviewed at USA Today insisted they are not sacrificing other content for celebrity coverage. They say celebrity news is just part of a balance the newspaper gives readers every day. Covering celebrities heavily is a way USA Today keeps relevant in the ever-changing media landscape. USA Today can be used as a celebrity news model for other newspapers looking for techniques to keep circulation numbers from dropping.

Peer-to-peer (p2p) approaches are an increasingly effective way to deploy services. Popular examples include BitTorrent, Skype, and KaZaA. These approaches are attractive because they can be highly fault-tolerant, scalable, adaptive, and less expensive than a more centralized solution. Cooperation lies at the heart of these strengths. Yet, in settings where working together is crucial, a natural question is: "What if users stop cooperating?" After all, cooperative services are typically deployed over multiple administrative domains, and thus vulnerable to Byzantine failures and users who may act selfishly. This dissertation explores how to construct p2p systems to tolerate Byzantine participants while also incentivizing selfish participants to contribute resources. We describe how to balance obedience against choice in building a robust p2p live streaming system. Imposing obedience is desirable as it leaves little room for peers to attack or cheat the system. However, providing choice is also attractive as it allows us to engineer flexible and efficient solutions. We first focus on obedience by using Nash equilibria to drive the design of BAR Gossip, the first gossip protocol that is resilient to Byzantine and selfish nodes. BAR Gossip relies on verifiable pseudo-random partner selection to eliminate non-determinism, which can be used to game the system, while maintaining the robustness and rapid convergence of traditional gossip. A novel fair enough exchange primitive entices cooperation among selfish peers on short timescales, thereby avoiding the need for distributed reputation schemes. We next focus on tempering obedience with choice by using approximate equilibria to guide the construction of a novel p2p live streaming system. These equilibria allow us to design incentives to limit selfish behavior rigorously, yet provide sufficient flexibility to build practical systems. We show the advantages of using an [element of]-Nash equilibrium, instead of an exact Nash, to design and implement FlightPath, our live streaming system that uses bandwidth efficiently, absorbs flash crowds, adapts to sudden peer departures, handles churn, and tolerates malicious activity. / text

Gossip is a little studied topic and even fewer studies have examined gossip from the perspective of social identity and self categorization theories. However, many of the functions of gossip have significant implications for group processes, including bonding, norm transmission and reinforcement, marginalization of deviants, and social influence. Particularly for those on the margins of the group, gossip may be used as a tool to gain acceptance in the group, as gossip is an effective way to express group loyalty and adherence to group norms. Study One investigated the extent to which being a prototypical member of one's group was predictive of likelihood to spread gossip. Using sororities as the group, members were presented with a hypothetical piece of gossip and asked the extent to which the member who gossiped is prototypical, how likely they would be to share the gossip with other group members, and how prototypical they perceive themselves to be of the sorority. It was predicted that peripheral group members would be more likely to spread gossip than central group members, particularly about other peripheral group members, and particularly when the information was not highly negative. Study Two was conducted in parallel, using the same methodology, but with a piece of gossip about a celebrity instead of a fellow sorority member. It was predicted that the results would mirror those of Study One and that peripheral members would be most likely to spread the gossip. While none of the stated hypotheses were supported, there were several unanticipated interactions. In both Study One and Study Two, there was a significant three-way interaction, in that a highly uncertain respondent, a prototypical target, and relatively mildly negative gossip was associated with anticipated transmission to the highest number of sorority members. While the results were unanticipated, they are not inexplicable and the implications for research in the areas of gossip, celebrity, and self categorization theory are discussed.