Browsing by Subject "User-generated content"

Now showing 1 - 5 of 5

Conspicuous participation : what is it & how does it impact communication strategies of nonprofit organizations?
(2011-05) Schlissel, Erin Nicole; Drumwright, Minette E.; Wilcox, Gary
The following report defines the concept of conspicuous participation and it demonstrates how it impacts nonprofit marketing efforts through social media. This is accomplished through providing an in-depth theoretical background, a detailed typography describing how conspicuous participation is currently being used to promote interactions with nonprofit organizations, and through two case studies, which offer real-world examples of how nonprofit organizations are utilizing this concept. Conspicuous participation can be defined as The act of publishing original or existing content in an online space that is visible to others, either all members of the general public or members of a private social network, in order to interact with and/or show support for a defined community or organization.
Essays on certification mechanism design in strategic communications
(2010-08) Xu, Hong, doctor of information, risk, and operations management; Stinchcombe, Maxwell; Whinston, Andrew B.; Mote, John; Wiseman, Thomas; Gu, Bin
Certifiers have a crucial role in facilitating effective communication in the online and the traditional world. As a way of generating statistically meaningful information, certification has been adopted in financial statements evaluation and more recently in various online communities as well. This dissertation examines three related issues along this common theme: online reputation market, moderation in user-generated content, and strategic communications in the market for certifications, and consists of three essays. The first essay analyzes the impact of various dispute mechanisms on online identity trading. Online identities with a good reputation profile is a valuable and tradable asset. However, with free identity creation, there is room for low quality sellers to free-ride high quality sellers. When there is a lack of incentive for sellers to maintain a good reputation, identity trading becomes ineffective. This essay focuses on the role of an auditing system, such as eBay dispute center, and shows that even a small amount of objective information from the auditors can reverse the negative result and sustain reliable reputation and identity trading. The second essay investigates the impact of moderation on the quality of information in an user-generated content (UGC) environment. In most UGC communities, content contributors have incentive to publish biased or false information. For example, companies hire people to write positive reviews about themselves. This essay establishes a framework for the mechanism design of moderation, and provides insight on how to optimally allocate moderation resource. The third essay examines a market for certification and certifiers' strategic reporting behaviors. The central question is how to induce certifiers to provide statistically meaningful information to investors when they are paid by their client firms. We provide insights on how certifier competition plays an role in firms' certifier choice, how certifiers degrade their accuracies to achieve maximum profit, and how the legal environment impacts the information quality.
Online use
(2016-05) Rashidian, Peyman; Eastin, Matthew S.; Cicchirillo, Vincent
Today’s social network sites give consumers control over producing, circulating and consuming content, thus allowing platforms such as Facebook, YouTube, and Wikipedia to compete with bigger media (i.e., television, newspapers, etc.). To better understand this complex and competitive environment, the current study examines user motivations for consuming, creating, and participating on Facebook, YouTube, and Wikipedia. In order to understand why users consume, create and participate, the uses and gratifications framework is applied. Data indicate that while motivations do vary across platforms, entertainment was the most common expectancies across Facebook, YouTube, and Wikipedia for consuming, creating, and participating.
Perceptual quality prediction of social pictures, social videos, and telepresence videos
(2022-07-01) Ying, Zhenqiang; Bovik, Alan C. (Alan Conrad), 1958-; Ghadiyaram, Deepti; De Veciana, Gustavo; Wang, Atlas; Geisler, Wilson S
The unprecedented growth of online social-media venues and rapid advances in technology by camera and mobile device manufacturers have led to the creation and consumption of a limitless supply of images/videos. Given the tremendous prevalence of Internet images/videos, monitoring the perceptual quality of images/videos would be a high-stakes problem. This dissertation focuses on the perceptual quality prediction of social pictures, social videos, and telepresence videos by constructing datasets of images/videos with their perceptual quality labels, as well as on designing algorithms that accurately predict the perceptual quality of images/videos. While considerable efforts have been put into effectively predicting the perceptual quality of synthetically distorted images/videos, real-world images/videos contain complex, composite mixtures of multiple distortions that non-uniformly distribute across space/time. The primary goal of my research is to design automatic image/video quality predictors that can effectively tackle the widely diverse authentic distortions of images/videos. To develop effective quality predictors, we trained deep neural networks on large-scale databases of authentically distorted images/videos. To improve the quality prediction by exploiting the non-uniformity of distortions, we collected quality labels for both the whole images/videos and patches/clips cropped from them. For social images, we built the LIVE-FB Large-Scale Social Picture Quality Database, containing about 40K real-world distorted pictures and 120K patches, on which we collected about 4M human judgments of picture quality. Using these picture and patch quality labels, we built deep region-based models that learn to produce state-of-the-art global picture quality predictions as well as useful local picture quality maps. Our innovations include picture quality prediction architectures that produce global-to-local inferences as well as local-to-global inferences (via feedback). For social videos, we built the Large-Scale Social Video Quality Database, containing 39K real-world distorted videos and 117K space-time localized video patches, and 5.5 M human perceptual quality annotations. Using this, we created two unique blind video quality assessment (VQA) models: (a) a local-to-global region-based blind VQA architecture (called PVQ) that learns to predict global video quality and achieves state-of-the-art performance on 3 video quality datasets, and (b) a first-of-a-kind space-time video quality mapping engine (called PVQ Mapper). For telepresence videos, we mitigated the dearth of subjectively labeled telepresence data by collecting 2k telepresence videos from different countries, on which we crowdsourced 80k subjective quality labels. Using this new resource, we created a first-of-a-kind online video quality prediction framework for live streaming, using a multi-modal learning framework with separate pathways to compute visual and audio quality predictions. Our all-in-one model is able to provide accurate quality predictions at the patch, frame, clip, and audiovisual levels. Our model achieves state-of-the-art performance on both existing quality databases and our new database, at a considerably lower computational expense, making it an attractive solution for mobile and embedded systems.
Throwing keywords at the internet : emerging practices and challenges in human rights open source investigations
(2019-08-13) Banchik, Anna Veronica; Rose, Mary R. (Mary Ruth), 1966-; Auyero, Javier
Human rights researchers are increasingly turning to the internet to discover, collect, and preserve user-generated content (UGC) documenting human rights abuses. The proliferation of UGC and other kinds of “open source” (publicly accessible) information online make available more information than ever before about abuses. Only—UGC may be incapable of verification, buried online, or in peril of deletion by platform content moderation, state coordinated flagging campaigns, or users themselves. Some contexts might produce a scarcity of information while others generate a deluge of data, complicating already lengthy ad hoc verification procedures. Moreover, UGC collection, recirculation, and preservation may endanger or go against the wishes of witnesses and their families. Drawing on interviews with open source practitioners and experts as well as a year of ethnographic fieldwork at the Human Rights Investigations Lab at U.C. Berkeley’s Human Rights Center, this dissertation examines the practices, logics, and narratives by which a growing number of researchers are applying open source investigative techniques to human rights advocacy and fact-finding. First, amidst increased scrutiny of platforms content moderation, this study highlights the more nuanced roles of platform design elements including algorithms and search functionality in shaping both UGC’s discoverability and investigators’ workarounds—underscoring the ephemerality of UGC itself and of the informational infrastructures through which UGC is sought. Second, this dissertation offers a typology to synthesize how an array of broader social and structural factors impinge on the verifiability of UGC from varied conflicts and contexts, building on prior research pointing to factors impacting the volume of content available about a given conflict as well as scholarship suggesting that the inclusion of “verification subsidies” (McPherson 2015a) into photos or video footage heightens content’s verifiability. Third, this study examines the emergence of human rights advocates and researchers as self-ascribed content stewards and safeguards. In addition to describing investigators’ affective and ethical commitments to UGC, I point to how pervasive “consent-cutting” discourses combine with decisions to refrain from contacting uploaders in ways that effectively normalize the sustenance of communication and consent gaps with content uploaders, raising ethical questions about responsible data collection and usage.