Navegando por Palavras-chave "Social Network"

Agora exibindo 1 - 3 de 3
  • Imagem de Miniatura
    Item
    Acesso aberto (Open Access)
    Análise do conteúdo textual de mensagens provenientes de redes sociais sobre temas de saúde baseado no inter-relacionamento de doenças, medicamentos e sintomas
    (Universidade Federal de São Paulo (UNIFESP), 2020-05-15) Araujo, Gabriela Denise De [UNIFESP]; Pisa, Ivan Torres [UNIFESP]; Universidade Federal de São Paulo
    Background: Analyzing and interpreting data available on the web, whether on social networks, blogs, or editorial sites, establishing relationships, identifying useful and relevant information is a current and significant computational challenge. The information age has favored the availability of a huge amount of data on the web that has naturally become a rich source of information and evidence on various subjects, including health. Objectives: The purpose of this study is to develop a methodological framework to monitor general public health information from social networks and to contribute to the scientific production of health surveillance studies. Methods: The messages containing at least two medical terms were selected using health terms and phrases related to diseases, symptoms, and medications. Data mining techniques, complex networks, and topic modeling were used to analyze health-related discussions on social networks. Results: About 141 million Twitter messages published in the Brazilian territory in 2017 were collected. Around 95 thousand were classified as health-related. Of these, 27% contained terms related to diseases, 56% related to symptoms and 47% to medications. It was possible to explore the relationship between health terms, the strength of connections and their types, and to observe themes that stood out by measuring their relative importance within the network. With the topic modeling technique, popular subjects were identified, and national health campaign events were highlighted. Unexpected topics were also noted; as symptom treatments and food. Conclusion: Users sharing their opinions and experiences on health topics on social networks can assist in monitoring some aspects of public health and collaborate for participatory surveillance, offering a perception to health managers of how people interact with health topics on the web. The results showed that varied topics related to health are discussed in social networks and the methodologies used in this study are efficient to highlight them and make them useful in terms of information.
  • Nenhuma Miniatura disponível
    Item
    Somente Metadadados
    Avaliação Da Rede Social De Adolescentes Com Excesso De Peso E Sua Relação Com O Desempenho Durante O Tratamento No Centro De Recuperação E Educação Nutricional - Cren
    (Universidade Federal de São Paulo (UNIFESP), 2018-06-28) Feres, Vivian Fortuna [UNIFESP]; Sawaya, Ana Lydia [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    Objective: To Use A Social Network Approach To Promote Food And Nutritional Security Of Overweight Children. Methods: The Present Study Follows A Methodology Of Qualitative Approach And Describes The Design And The Intervention In The Social Network Of Four Children, Two Females And Two Males, With The Construction Of The Detailed And Complete Social Network Map, Representing All The Relationship Content That Makes Up The Child's Network. These Children Participated In A Larger Project With The Objective Of Evaluating The Effectiveness Of The Multidisciplinary Intervention For Overweight Children In Two Public Schools In São Paulo For Sixteen Months. Results: The Design Of The Social Network Map Carried Out With The Children Made It Possible To Identify The Places And Relationships Where Their Feeding Took Place And Its Profile. In Addition, It Assisted The Reflection Of The Child On His Responsibility And Protagonism In Relation To His Health. In Addition, It Helped The Children And Family Members Reflect On
  • Nenhuma Miniatura disponível
    Item
    Somente Metadadados
    A Comparative Study On Regression Approaches For Event Detection In Instagram
    (Universidade Federal de São Paulo (UNIFESP), 2017-11-30) Santos, Elder Donizetti Dos [UNIFESP]; Faria, Fabio Augusto [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    With the advancement of the use of web-based applications and mobile device technologies, in particular, online social networks, many approaches have been proposed in the literature using it as the source of information. Online social networks like Instagram have more than 700 million users who, together, create over 300 million new content every day. All of this data can be used, for instance, to detect real-world events. Such events can be defined as a car accident, a natural disaster, a riot, a political affair, among others. In order to do that, algorithms need to manage massive, rapidly changing and fast arriving data streams made of text, images, and videos. It also involves challenges such as the lack of a labeled database to analyze the effectiveness of applied techniques that can be reused by other researchers and the need for an approach that adapts to the constant changes in the flow of information. However, existing approaches are often either limited or not suitable for new data sources like Instagram. In this sense, this work provides contributions in the area of event detection for online social networks. As a first contribution a review on how the task of event detection has been approached by researchers since its inception in the 1990’s is presented. The second contribution is an introduction to the behavior and volume characteristics of Instagram posts modeled as time series. Then, a comparative study of different regression techniques for time series prediction is conducted by applying a preprocessing step and algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), Autoregressive Integrated Moving Averages (ARIMA), Classification and Regression Trees (CART) and K-Nearest Neighbors (KNN). As a result, it is demonstrated how a simple yet efficient approach can be used to detect events in social networks. Trying to overcome some of the challenges mentioned, as a third contribution, a semi-supervised learning approach is proposed using time series correlations. Experimental studies have shown that time series from different sub-regions with similar characteristics can be used to generalize knowledge and predict the occurrence of an event. Moreover, it is demonstrated that the proposed approach is a good alternative to the Gaussian Process Regression (GPR) used in the literature since the approach based on time series correlations provides good results using much less computing resources than GPR. In addition to the main contributions cited, the entire dataset used in this thesis with more than 180 thousand manually labeled Instagram posts is publicly available.