Consumer-brand engagement on Instagram

Social media has been subject to an increasing number of studies. Although there is empirical research on consumer–brand interaction on social media, such as on Facebook, there is still a lack of studies on other SNS. As a marketing tool that allows brands to connect and engage with their consumers, Instagram is pointed out as a critical social network. However, there is still a lack of marketing studies on consumer-brand engagement on Instagram. Therefore, the purpose of this research is to understand the motivations for consumer to engage with a brand through Instagram. The findings should provide brand managers guidelines to develop a more effective strategy to approach this SNS. In order to fulfill the research purpose, we used an online survey with 177 valid responses. To understand the relationship between consumer motivations and consumer-brand engagement on Instagram, a multiple linear regression was performed. The results indicate that the main motivation to consume brand content on Instagram is search for information. The main motivations to actively interact with brand related content are social influence and personal identity. Brands that seek to attract their consumers to their Instagram page should offer convenient access to information, by creating interesting content through short stories, videos or photos, they should also stimulate users to share their opinion and offer appealing prizes and discounts. Further research could apply this study to a specific brand or product category

Association of inflammation, dyslipidemia, obesity and physical activity status in children

Abstract The aim of this study was to verify the association between inflammatory biomarkers, dyslipidemia, obesity and physical activity status in 10-years old children. Ninety-four children participated in this study and were classified into eutrophic (n=36), overweight (n=34) or obese (n=24) according to their body mass index (BMI). The genic expression of interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α) and chemokine C-C motif ligand 2 (CCL-2) mRNA; the serum concentration of high-density lipoprotein cholesterol (HDL-c) and triglycerides; BMI, percentage of body fat (% BF) and waist circumference; and the number of steps per day were determined. The expression of IL-6, TNF-α and CCL-2 were associated (p 0.05) between pro-inflammatory biomarkers and number of steps per day was found

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press