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This thesis is an investigation into the aesthetic and political functions of “rumor” in modern Egypt. While previous studies have emphasized the formal or structural features of the genre, I seek to analyze the discursive, political and technological contexts that contribute to its persistence as such a powerful and ambivalent way of imagining speech. The scope of my analysis is a collection of texts culled from the tradition of Arabic letters in Egypt, beginning with early works of historiography (16th century), and into the political journals, newspapers, and novels of the 20th century, as well as the blogs, search engines and internet forums of the 21st century. I argue that specific discourses and imaginings of the rumor – contingent and mutable – emerged as an inseparable feature of the elite author’s textual encounter with the masses. Anxieties over the agency of various mass subjects – the urban crowd, the citizenry, and others – have contributed to the ways in which different writers reify speech. The final chapters of my thesis turn to focus on rumors about the death of President Husni Mubarak, in order to analyze the role the genre plays in contests over national political authority. Here, the rumor is an index of fears, passions, fantasies and other narratives that the writers both draw on and contribute to. Foregrounding these associations becomes a powerful aesthetic and affective process that allows actors to "fix" - solidify and treat - the agency and subjectivity of others. / text

Magíster en Gestión de Operaciones / Ingeniero Civil Industrial / El esparcimiento de rumores es un modelo intuitivo para la difusión de información en una red social. Una entidad que controla la red, por ejemplo el proveedor del servicio, desea acelerar el proceso de esparcimiento del rumor, de forma tal que se maximice la cantidad de información entregada. Este problema, definido a grandes rasgos, ha sido objeto de múltiples investigaciones en el último tiempo, entre otros como marketing viral y maximización de influencia. Un enfoque natural y ausente en los estudios previos es la adaptividad. En este trabajo se abordan las siguientes preguntas: ¿cómo el controlador puede usar la información del estado de la red para acelerar el proceso de rumor? y ¿cuánto beneficio se obtiene de tal conocimiento? Un concepto novedoso es la comunicación oportunista en redes; cada agente de la red social comparte información (noticias, actualización de software, etc.) con otros usuarios al momento de estar momentáneamente en rango (vía wi-fi, bluetooth, etc.), de esta forma se evita la saturación de la infraestructura que soporta la red. Con esta motivación se estudia un modelo a tiempo continuo, donde cada par de nodos se comunica de acuerdo a un proceso de Poisson de cierta tasa y el rumor se transmite siempre que alguno estuviera informado. Las anteriores comunicaciones no tienen costo para el controlador, pero si éste lo desea puede informar a cualquier nodo pagando un costo unitario por ello. En vez de la usual restricción de presupuesto se fija un deadline, en tal tiempo todos los nodos deben estar informados, debiendo pagar el controlador un costo unitario por cada nodo que no haya obtenido el rumor antes del deadline. Una estrategia no-adaptativa puede informar sólo al comienzo del periodo y cuando se cumple el deadline, pagando por todos aquellos nodos que no se comunicaron nunca con otro nodo informado. Una estrategia adaptativa puede intervenir la red en cualquier instante, usando toda la información disponible hasta ese entonces, en particular sabiendo cuales nodos tienen el rumor en cada momento. El resultado principal de este trabajo es que en el caso homogéneo, donde cada par de nodos se encuentra con la misma tasa, el beneficio de la adaptividad está acotado por una constante. La demostración requiere un entendimiento profundo del proceso estocástico que domina el sistema, que se cree ya una contribución interesante. Adicionalmente, se presenta una extensión natural del caso homogéneo, donde el controlador está interesado sólo en un conjunto de nodos y no en toda la red social, se demuestra que en este escenario el beneficio de la adaptividad también está acotado por una constante. Finalmente, se muestra que, sin el supuesto de homogeneidad, el beneficio de la adaptividad puede crecer de forma no acotada.

Thesis (M.A.)--University of Nevada, Reno, 2005. / "December, 2005." Includes bibliographical references (leaves 24-26). Online version available on the World Wide Web.

Rumors always trigger public panic in China, while the advanced Internet technology has tremendously influenced the daily life of the Chinese. Thus, the dynamics of rumor spreading via the social media in China are worthwhile discussing. In order to fulfill the aim of this research, one of the prevalent Chinese social media sites, the Sina microblog, is introduced and analyzed within the context of the salt-buying frenzy incident that occurred in China in 2011. Various theoretical approaches, from the sociological and media studies perspectives, are introduced to form the framework for analysis. The relevant data and materials were collected via questionnaires with Chinese Internet users, while previous academic research and publications provide supportive materials. From the analysis of this social media site and contemporary Chinese circumstances, it was seen that the Guanxi network extended and enlarged the influence of online rumors to offline Chinese. In other words, the Guanxi network, expanded by social media, can be considered as the most important part of the dynamic process of rumor spreading in China. However, the openness and freedom of the Internet atmosphere were found to be the main factors in eliminating online rumors.

This thesis examines the linkages between the verbal response to social stress, the ostracism of individuals from a social group, and the subsequent increased cohesion of the remaining members. To write the thesis, I utilized these printed references in the forms of scholarly research, journals, diaries, and interviews primarily from the Texas A&M Sterling Evans Library and the online journal resource JSTOR as well as a video documentary. Previous research into the genres of rumor, identity, and scapegoat accusations are explicated. Then, these approaches are applied to the rumors told by the Japanese-Americans who were removed from their homes and sent to internment camps in the United States during World War II. The internment camps were rife with scapegoat accusations between the internees whose once unified culture group was fissured along lines of loyalty to the United States or to Japan. These scapegoat accusations against fellow internees were an outlet for the stress exerted upon them by the American government that was not directly combatable. Even processes as complicated as changing social dynamics can be observed through the mechanisms of rumors and scapegoat accusations.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 47-48).

Thesis (M.A. in National Security Affairs)--Naval Postgraduate School, 2004. / Thesis Advisor(s): James Russell, Karen Guttieri. Includes bibliographical references (p. 65-69). Also available online.

Yes / Twitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.

The penetration of social media today enables the rapid spread of breaking news and other developments to millions of people across the globe within hours. However, such pervasive use of social media by the general masses to receive and consume news is not without its attendant negative consequences as it also opens opportunities for nefarious elements to spread rumors or misinformation. A rumor generally refers to an interesting piece of information that is widely disseminated through a social network and whose credibility cannot be easily substantiated. A rumor can later turn out to be true or false or remain unverified. The spread of misinformation and fake news can lead to deleterious effects on users and society. The objective of the proposed research is to develop a range of machine learning methods that will effectively detect and characterize rumor veracity in social media. Since users are the primary protagonists on social media, analyzing the characteristics of information spread w.r.t. users can be effective for our purpose. For our first problem, we propose a method of computing user embeddings from underlying social networks. For our second problem, we propose a long short-term memory (LSTM) based model that can classify whether a story discussed in a thread can be categorized as a false, true, or unverified rumor. We demonstrate the utility of user features computed from the first problem to address the second problem. For our third problem, we propose a method that uses user profile information to detect rumor veracity. This method has the advantage of not requiring the underlying social network, which can be tedious to compute. For the last problem, we investigate a rumor mitigation technique that recommends fact-checking URLs to rumor debunkers, i.e., social network users who are very passionate about disseminating true news. Here, we incorporate the influence of other users on rumor debunkers in addition to their previous URL sharing history to recommend relevant fact-checking URLs. / Doctor of Philosophy / A rumor is generally defined as an interesting piece of a story that cannot be authenticated easily. On social networks, a user can generally find an interesting piece of news or story and may share (retweet) it. A story that initially appears plausible can later turn out to be false or remain unverified. The propagation of false rumors on social networks has a deteriorating effect on user experience. Therefore, rumor veracity detection is important, and drawing interest in social network research. In this thesis, we develop various machine learning models that detect rumor veracity. For this purpose, we exploit different types of information regarding users, such as profile details and connectivity with other users etc. Moreover, we propose a rumor mitigation technique that recommends fact-checking URLs to social network users who are passionate about debunking rumors. Here, we leverage similar techniques used in e-commerce sites for recommending products to solve this problem.