Distilling Information Reliability and Source Trustworthiness from Digital Traces

Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17

The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale

In this paper, we interpret the community question answering websites on the StackExchange platform as knowledge markets, and analyze how and why these markets can fail at scale. A knowledge market framing allows site operators to reason about market failures, and to design policies to prevent them. Our goal is to provide insights on large-scale knowledge market failures through an interpretable model. We explore a set of interpretable economic production models on a large empirical dataset to analyze the dynamics of content generation in knowledge markets. Amongst these, the Cobb-Douglas model best explains empirical data and provides an intuitive explanation for content generation through concepts of elasticity and diminishing returns. Content generation depends on user participation and also on how specific types of content (e.g. answers) depends on other types (e.g. questions). We show that these factors of content generation have constant elasticity---a percentage increase in any of the inputs leads to a constant percentage increase in the output. Furthermore, markets exhibit diminishing returns---the marginal output decreases as the input is incrementally increased. Knowledge markets also vary on their returns to scale---the increase in output resulting from a proportionate increase in all inputs. Importantly, many knowledge markets exhibit diseconomies of scale---measures of market health (e.g., the percentage of questions with an accepted answer) decrease as a function of number of participants. The implications of our work are two-fold: site operators ought to design incentives as a function of system size (number of participants); the market lens should shed insight into complex dependencies amongst different content types and participant actions in general social networks.Comment: The 27th International Conference on World Wide Web (WWW), 201

Plan de negocio para determinar la viabilidad de una empresa que implemente una plataforma de crowdlearning digital

El plan de negocios busca la creaci?n de una empresa que ofrezca un espacio que facilite el intercambio de conocimientos entre usuarios que tengan la necesidad de adquirir y/o compartir conocimientos, a trav?s de una plataforma digital de crowdlearning. ?CrowdX? es una empresa que ofrece un espacio que facilita el intercambio de conocimientos entre sus usuarios a trav?s de una plataforma digital de crowdlearning. Est? dirigido a personas que tengan la necesidad de adquirir y/o compartir conocimiento. La plataforma permitir? a los usuarios crear una ?b?squeda de asesor?a? sobre alg?n problema puntual de ?ndole profesional o acad?mico, mediante la cual podr?n ponerse en contacto con otros usuarios que se hayan postulado o hayan sido sugeridos por la plataforma a trav?s de recomendaciones inteligentes y valoraciones otorgadas por la comunidad, con el fin de concretar la asesor?a a trav?s de reuniones virtuales s?ncronas

Development of an e-voting model based on cloud computing technology

Предмет истраживања докторске дисертације је дефинисање модела електронског гласања применом cloud computing технологије. Циљ је испитивање могућности примене овог модела у области управе и образовања. Са аспекта електронске управе, суштина примене модела електронског гласања јесте унапређење постојећег, традиционалног система гласања које , поред бројних других предности, са собом носи и смањење трошкова гласања. Када је реч о примени модела електронског гласања у области електронског образовања, потребно је испитати у којој мери технике електронског гласања могу повећати учешће студената у образовном процесу и њихово интересовање за њега. Још један од начина да се ово постигне јесте употреба друштвених мрежа као медијума за crowdvoting, такмичење и сарадњу међу студентима. Модел инфраструктуре електронског гласања представљен у овој дисертацији заснован је на cloud computing технологији. Питање сигурности обезбеђено је применом криптографских (Blockchain) протокола уз ослањање на принципе електронске идентификације као система за аутентификацију гласача. Како је предложена архитектура модела за електронско гласање вишеслојна, постоји могућност интеграције сервиса електронског гласања, ИТ инфраструктуре и регистра учесника у процесу гласања. У дисертацији је описан концепт сервисно-оријентисане архитектуре која ће бити основ за развој система за електронско гласање. У циљу евалуације модела у докторској дисертацији биће реализовано тестирање система за електронско гласање у погледу функционисања и сигурности, с акцентом на унапређењу перформанси и ефикасности предложеног модела.The subject of research in this thesis is defining the electronic voting model using the cloud computing technology. The aim is to examine the possibility to apply this model in the area of government and education. From the e-government aspect, the essence of e-voting model application is to improve existing, traditional voting system which, in addition to many other advantages, brings also a reduction of voting costs. When it comes to the implementation of the e-voting model in the area of electronic education, it is necessary to examine to what extent e-voting techniques can increase students’ participation and their interest in the educational process. Another way to accomplish this is to use social networks as a medium for crowdvoting, competition and collaboration among students. The e-voting infrastructure model presented in this thesis is based on cloud computing technology. The security issue is ensured by using the cryptographic (Blockchain) protocols, relying on the e-identification principles, as a system for voters’ authentication. Since the suggested architecture of e-voting model is multilayered, there is a possibility to integrate e-voting services, IT infrastructure and register of participants in voting process. The concept of service-oriented architecture is described in the thesis and it going to be base for e-voting system development. With the aim to evaluate model in doctoral dissertation, testing of e-voting system’s functionality and security will be carried, with an emphasis on performance and efficiency improvement of suggested model

Crowdsourcing User-Centered Teams

Crowdsourcing has become an increasingly important tool for team formation and collaboration. This thesis investigates how User-Centered Design, an iterative process that prioritizes users and their needs, can be applied to improve the efficiency and effectiveness of crowdsourcing systems for teamwork and team formation. To achieve this, we conducted a series of studies to explore the role of various factors in shaping crowd workers' behaviour and preferences in collaborative contexts. The main findings of our research are as follows. In online team formation settings, crowd workers prefer disclosing overt traits (e.g., age, gender, topical interests) and avoid sharing sensitive information (e.g., ethnicity, depression). However, they are willing to share information regarding their personality and values, typically considered deep-level sensitive traits. Well-defined digital nudging interventions, such as a diversity progress bar, can promote diverse team formation. In contrast, subtler forms of nudging may inadvertently trigger biases working against the intended objectives. Ad-hoc crowd teams working under pressure can benefit from systems that account for differences in personality traits, as these can influence collaboration outcomes and perceptions. Designing crowdsourcing systems for emergency response requires modelling communication tools that aid, assist, and monitor the shared load, considering the strictly cooperative roles and task- and user-dependent communication styles between collaborators. When forming teams, crowd workers tend to balance attributes between and within groups, with a preference for Openness to Experience among the Big-5 personality traits. Based on these findings, we recommend applying a User-Centered approach to design collaborative crowdsourcing systems, considering user needs, behaviour, intents, and perceptions of digital environments. Future research should continue to explore and evaluate innovative strategies for promoting effective collaboration and team formation in crowdsourcing contexts