Abstract

Abstract

Abstract

Abstract
Social media platforms are having a profound impact on the so-called information ecosystem, specifically on how information is produced, distributed and consumed. Social media in particular has contributed to the rise of user generated content and consequently to a greater diversity in online content. On the other hand, social media networks, such as Twitter or Facebook, have become information management tools that allow users to setup and configure information sources to their particular interests. A Twitter user can handpick the sources he wishes to follow, thus creating a custom information channel. However, this opportunity to create personalized information channels effectively results in different consumption profiles? Is the information consumed by users through social media networks distinct from the information consumed though traditional mainstream media? In this work, we set out to investigate this question using Twitter as a case study. We prepare two samples of users, one based on a uniform random selection of user IDs, and another one based on a selection of mainstream media followers. We analyze the home timelines of the users in each sample, focusing on characterizing information consumption habits. We find that information consumption volume is higher, while diversity is consistently lower, for mainstream media followers when compared to random users. When analyzing daily behavior, however, the samples slightly approximate, while clearly maintaining a lower diversity for mainstream media followers and a higher diversity for random users. Copyright © 2018 for the individual papers by the papers’ authors.

Abstract

Abstract
The quality of consumer-oriented health information on the Web is usually assessed through the medical certification of websites. These tools are built upon quality indicators but, so far, no standard set of indicators has been defined. The objective of the present study is to explore the popularity of specific document features and their influence on the quality of health web documents, using HON code as ground truth. A set of top-ranked health documents retrieved from a major search engine was characterized in a univariate analysis, and then used in a bivariate analysis to seek features that affect documents' quality. The univariate analysis provides insights into the characteristics of the overall population of the health web documents. The bivariate analysis reveals strong relations between documents' quality and a set of features (namely split content, videos, images, advertisements, English language) that are potential quality indicators. We characterized health web documents and identified specific document features that can be used to assess whether the information in such documents is trustworthy. The main contribution of this work is to provide other features as candidate indicators of quality. Non-health professionals can use these indicators in automatic and manual assessments of health content.