3,550 research outputs found
Learning Representations of Social Media Users
User representations are routinely used in recommendation systems by platform
developers, targeted advertisements by marketers, and by public policy
researchers to gauge public opinion across demographic groups. Computer
scientists consider the problem of inferring user representations more
abstractly; how does one extract a stable user representation - effective for
many downstream tasks - from a medium as noisy and complicated as social media?
The quality of a user representation is ultimately task-dependent (e.g. does
it improve classifier performance, make more accurate recommendations in a
recommendation system) but there are proxies that are less sensitive to the
specific task. Is the representation predictive of latent properties such as a
person's demographic features, socioeconomic class, or mental health state? Is
it predictive of the user's future behavior?
In this thesis, we begin by showing how user representations can be learned
from multiple types of user behavior on social media. We apply several
extensions of generalized canonical correlation analysis to learn these
representations and evaluate them at three tasks: predicting future hashtag
mentions, friending behavior, and demographic features. We then show how user
features can be employed as distant supervision to improve topic model fit.
Finally, we show how user features can be integrated into and improve existing
classifiers in the multitask learning framework. We treat user representations
- ground truth gender and mental health features - as auxiliary tasks to
improve mental health state prediction. We also use distributed user
representations learned in the first chapter to improve tweet-level stance
classifiers, showing that distant user information can inform classification
tasks at the granularity of a single message.Comment: PhD thesi
Learning Representations of Social Media Users
User representations are routinely used in recommendation systems by platform
developers, targeted advertisements by marketers, and by public policy
researchers to gauge public opinion across demographic groups. Computer
scientists consider the problem of inferring user representations more
abstractly; how does one extract a stable user representation - effective for
many downstream tasks - from a medium as noisy and complicated as social media?
The quality of a user representation is ultimately task-dependent (e.g. does
it improve classifier performance, make more accurate recommendations in a
recommendation system) but there are proxies that are less sensitive to the
specific task. Is the representation predictive of latent properties such as a
person's demographic features, socioeconomic class, or mental health state? Is
it predictive of the user's future behavior?
In this thesis, we begin by showing how user representations can be learned
from multiple types of user behavior on social media. We apply several
extensions of generalized canonical correlation analysis to learn these
representations and evaluate them at three tasks: predicting future hashtag
mentions, friending behavior, and demographic features. We then show how user
features can be employed as distant supervision to improve topic model fit.
Finally, we show how user features can be integrated into and improve existing
classifiers in the multitask learning framework. We treat user representations
- ground truth gender and mental health features - as auxiliary tasks to
improve mental health state prediction. We also use distributed user
representations learned in the first chapter to improve tweet-level stance
classifiers, showing that distant user information can inform classification
tasks at the granularity of a single message.Comment: PhD thesi
Enhancing E-learning platforms with social networks mining
Social Networks appeared as an Internet application that offers several tools to create a personal virtual profile, add other users as friends, and interact with them through messages. These networks quickly evolved and won particular importance in people lives. Now, everyday, people use social networks to share news, interests, and discuss topics that in some way are
important to them.
Together with social networks, e-learning platforms and related technologies have evolved in the recent years. Both platforms and technologies (social networks and e-learning) enable access to specific
information and are able to redirect specific content to an individual person.
This dissertation is motivated on social networks data mining over e-learning
platforms. It considers the following four social networks: Facebook, Twitter, Google Plus, and Delicious. In order to acquire, analyze, and make a correct and precise implementation of data, two different approaches were followed: enhancement of a current e-learning platform and improvement of search engines. The first approach proposes and elaborates a recommendation tool for Web documents using, as main criterion, social information to support a custom Learning Management System (LMS). In order to create the proposed system, three distinct applications (the Crawler, the SocialRank, and the Recommender) were proposed. Such data
will be then incorporated into an LMS system, such as the Personal Learning Environment Box (PLEBOX). PLEBOX is a custom platform based on operating systems layout, and also, provides a software development kit (SDK), a
group of tools, to create and manage modules. The results of recommendation tool about ten course units are presented.
The second part presents an approach to improve a search engine based on social networks content. Subsequently, a depth analysis to justify the abovementioned procedures in order to create the SocialRank is presented.
Finally, the results are presented and validated together with a custom search engine. Then, a solution to integrate and offer an order improvement of Web contents in a search engine was proposed, created, demonstrated, and validated, and it is ready for use.As redes sociais surgiram como um serviço Web com funcionalidades de
criação de perfil, criação e interação de amigos. Estas redes evoluíram
rapidamente e ganharam uma determinada importância na vida das pessoas.
Agora, todos os dias, as pessoas usam as redes sociais para partilhar
notícias, interesses e discutir temas que de alguma forma são importantes
para elas.
Juntamente com as redes sociais, as plataformas de aprendizagem baseadas
em tecnologias, conhecidas como plataformas E-learning têm evoluído
muito nos últimos anos. Ambas as plataformas e tecnologias (redes sociais e
E-learning) fornecem acesso a informações específicas e são capazes de
redirecionar determinado conteúdo para um ou vários indivíduos
(personalização).
O tema desta dissertação é motivado pela mineração do conteúdo das redes
sociais em plataformas E-learning. Neste sentido, foram selecionadas quatro
redes sociais, Facebook, Twitter, Google Plus, e Delicious para servir de
estudo de caso à solução proposta. A fim de adquirir, analisar e concretizar
uma aplicação correta e precisa dos dados, duas abordagens diferentes
foram seguidas: enriquecimento de uma plataforma E-learning atual e
melhoria dos motores de busca. A primeira abordagem propõe e elaboração
de uma ferramenta de recomendação de documentos Web usando, como
principal critério, a informação social para apoiar um sistema de gestão de
aprendizagem (LMS). Desta forma, foram construídas três aplicações
distintas, designadas por Crawler, SocialRank e Recommender. As
informações extraídas serão incorporadas num sistema E-learning, tendo
sido escolhida a PLEBOX (Personal Learning Environment Box). A PLEBOX é
uma plataforma personalizada baseada numa interface inspirada nos
sistemas operativos, fornecendo um conjunto de ferramentas (os conhecidos
SDK - software development kit), para a criação e gestão de módulos. Dez
unidades curriculares foram avaliadas e os resultados do sistema de
recomendação são apresentados.
A segunda abordagem apresenta uma proposta para melhorar um motor de
busca com base no conteúdo das redes sociais. Subsequentemente, uma
análise profunda é apresentada, justificando os procedimentos de avaliação,
afim de criar o ranking de resultados (o SocialRank). Por último, os
resultados são apresentados e validados em conjunto com um motor de
busca. Assim, foi proposta, construída, demonstrada e avaliada uma solução
para integrar e oferecer uma melhoria na ordenação de conteúdos Web
dentro de um motor de busca. A solução está pronta para ser utilizad
- …