Covid-19: análisis métrico de vídeos y canales de comunicación en YouTube

[ES] El objetivo de este trabajo es determinar el volumen de vídeos sobre Covid-19 publicados y difundidos a través de YouTube y relacionados directa o indirectamente con el territorio nacional español, caracterizar su impacto (en términos de visualizaciones, likes y comentarios recibidos), y finalmente categorizar los canales a través de los cuales se han difundido. Para ello se han analizado 39.531 vídeos publicados entre el 1 enero y el 30 de abril de 2020. Los resultados muestran que el número de vídeos sobre Covid-19 aumentaron a partir del establecimiento del estado de alarma en España y, poco a poco, han ido disminuyendo en el tiempo, con ciertos repuntes vinculados a decisiones políticas. Los vídeos logran en promedio un gran número de visualizaciones, likes/dislikes y comentarios, y han sido publicados principalmente por medios de comunicación. Los vídeos relacionados con blogs y entretenimiento son muy numerosos pero con menor impacto. Los vídeos pertenecientes a las categorías de Educación y Ciencia y Tecnología son menos numerosos pero con un alto impacto, especialmente en visualizaciones. De forma complementaria, se concluye que los criterios de ordenación de YouTube no son lo suficientemente precisos como para ser utilizados en estudios informétricos sin una alta carga de trabajo en limpieza de datos. Así mismo, la existencia de canales que aplican estrategias engañosas de posicionamiento dificulta la realización de este tipo de estudios.[EN] The objective of this work is to determine the volume of videos on Covid-19 published and disseminated through YouTube, and directly or indirectly related to the Spanish national territory, to characterize the impact of those videos (in terms of views, likes and comments received), and finally to categorize the channels through which the videos have been broadcast. For this, 39,531 videos published between January 1 and April 30, 2020 have been analysed. The results show that the number of videos on Covid-19 grew since the establishment of the state of alarm in Spain, and they have been slightly decreasing over time, including certain upswings linked to political decisions. The videos achieve on average high volumes of views, likes/dislikes and comments, and have been published mainly by the media. The videos related both to Blogs and Entertainment are very numerous but with less impact. The videos belonging to the categories of Education and Science and Technology are less numerous, but had a high impact, especially in view counts. In addition, it is concluded that YouTube search filters are not accurate enough to be used in informetric studies without a high data cleansing workload. Likewise, the existence of channels applying defective positioning techniques makes it difficult to carry out this type of study.Orduña Malea, E.; Font-Julian, CI.; Ontalba Ruipérez, JA. (2020). Covid-19: análisis métrico de vídeos y canales de comunicación en YouTube. El Profesional de la información (Online). 29(4):1-14. https://doi.org/10.3145/epi.2020.jul.01S11429

Covid-19: análisis métrico de vídeos y canales de comunicación en YouTube // Covid-19: metric analysis of videos and communication channels on YouTube

The objective of this work is to determine the volume of videos on Covid-19 published and disseminated through YouTube, and directly or indirectly related to the Spanish national territory, to characterize the impact of those videos (in terms of views, likes and comments received), and finally to categorize the channels through which the videos have been broadcast. For this, 39,531 videos published between January 1 and April 30, 2020 have been analysed. The results show that the number of videos on Covid-19 grew since the establishment of the state of alarm in Spain, and they have been slightly decreasing over time, including certain upswings linked to political decisions. The videos achieve on average high volumes of views, likes/dislikes and comments, and have been published mainly by the media. The videos related both to Blogs and Entertainment are very numerous but with less impact. The videos belonging to the categories of Education and Science and Technology are less numerous, but had a high impact, especially in view counts. In addition, it is concluded that YouTube search filters are not accurate enough to be used in informetric studies without a high data cleansing workload. Likewise, the existence of channels applying defective positioning techniques makes it difficult to carry out this type of study

Covid-19: análisis métrico de vídeos y canales de comunicación en YouTube // Covid-19: metric analysis of videos and communication channels on YouTube

The objective of this work is to determine the volume of videos on Covid-19 published and disseminated through YouTube, and directly or indirectly related to the Spanish national territory, to characterize the impact of those videos (in terms of views, likes and comments received), and finally to categorize the channels through which the videos have been broadcast. For this, 39,531 videos published between January 1 and April 30, 2020 have been analysed. The results show that the number of videos on Covid-19 grew since the establishment of the state of alarm in Spain, and they have been slightly decreasing over time, including certain upswings linked to political decisions. The videos achieve on average high volumes of views, likes/dislikes and comments, and have been published mainly by the media. The videos related both to Blogs and Entertainment are very numerous but with less impact. The videos belonging to the categories of Education and Science and Technology are less numerous, but had a high impact, especially in view counts. In addition, it is concluded that YouTube search filters are not accurate enough to be used in informetric studies without a high data cleansing workload. Likewise, the existence of channels applying defective positioning techniques makes it difficult to carry out this type of study

Mining Social Media to Understand Consumers' Health Concerns and the Public's Opinion on Controversial Health Topics.

Social media websites are increasingly used by the general public as a venue to express health concerns and discuss controversial medical and public health issues. This information could be utilized for the purposes of public health surveillance as well as solicitation of public opinions. In this thesis, I developed methods to extract health-related information from multiple sources of social media data, and conducted studies to generate insights from the extracted information using text-mining techniques. To understand the availability and characteristics of health-related information in social media, I first identified the users who seek health information online and participate in online health community, and analyzed their motivations and behavior by two case studies of user-created groups on MedHelp and a diabetes online community on Twitter. Through a review of tweets mentioning eye-related medical concepts identified by MetaMap, I diagnosed the common reasons of tweets mislabeled by natural language processing tools tuned for biomedical texts, and trained a classifier to exclude non medically-relevant tweets to increase the precision of the extracted data. Furthermore, I conducted two studies to evaluate the effectiveness of understanding public opinions on controversial medical and public health issues from social media information using text-mining techniques. The first study applied topic modeling and text summarization to automatically distill users' key concerns about the purported link between autism and vaccines. The outputs of two methods cover most of the public concerns of MMR vaccines reported in previous survey studies. In the second study, I estimated the public's view on the ac{ACA} by applying sentiment analysis to four years of Twitter data, and demonstrated that the the rates of positive/negative responses measured by tweet sentiment are in general agreement with the results of Kaiser Family Foundation Poll. Finally, I designed and implemented a system which can automatically collect and analyze online news comments to help researchers, public health workers, and policy makers to better monitor and understand the public's opinion on issues such as controversial health-related topics.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120714/1/owenliu_1.pd