Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWhat is the people posting about symptoms related to Coronavirus in Bogota, Colombia?
84
0
0.0
(
0
)
Added by
Josimar Chire Saire
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
During the last months, there is an increasing alarm about a new mutation of coronavirus, covid-19 coined by World Health Organization(WHO) with an impact in many areas: economy, health, politics and others. This situation was declared a pandemic by WHO, because of the fast expansion over many countries. At the same time, people is using Social Networks to express what they think, feel or experiment, so this people are Social Sensors and helps to analyze what is happening in their city. The objective of this paper is analyze the publications of Colombian people living in Bogota with a radius of 50 km using Text Mining techniques from symptomatology approach. The results support the understanding of the spread in Colombia related to symptoms of covid19.
rate research
Read More
Recommendation plays a key role in e-commerce and in the entertainment industry. We propose to consider successive recommendations to users under the form of graphs of recommendations. We give models for this representation. Motivated by the growing interest for algorithmic transparency, we then propose a first application for those graphs, that is the potential detection of introduced recommendation bias by the service provider. This application relies on the analysis of the topology of the extracted graph for a given user; we propose a notion of recommendation coherence with regards to the topological proximity of recommended items (under the measure of items k-closest neighbors, reminding the small-world model by Watts & Stroggatz). We finally illustrate this approach on a model and on Youtube crawls, targeting the prediction of Recommended for you links (i.e., biased or not by Youtube).
Epigenetics has captured the attention of scientists in the past decades, yet its scope has been continuously changing. In this paper, we give an overview on how and why its definition has evolved and suggest several clarification on the concepts used in this field, in particular, on the notions of epigenetic information, epigenetic stability and epigenetic templating. Another issue that we address is the role of epigenetic information. Not only it is important in allowing alternative interpretations of genetic information, but it appears to be important in protecting the genetic information, moreover, we suggest that this function appeared first in evolution and only later on the epigenetic mechanisms were recruited to play a role in cell differentiation.
The 2020 coronavirus pandemic was a historic social disruption with significant consequences felt around the globe. Wikipedia is a freely-available, peer-produced encyclopedia with a remarkable ability to create and revise content following current events. Using 973,940 revisions from 134,337 editors to 4,238 articles, this study examines the dynamics of the English Wikipedias response to the coronavirus pandemic through the first five months of 2020 as a quantitative portrait describing the emergent collaborative behavior at three levels of analysis: article revision, editor contributions, and network dynamics. Across multiple data sources, quantitative methods, and levels of analysis, we find four consistent themes characterizing Wikipedias unique large-scale, high-tempo, and temporary online collaborations: external events as drivers of activity, spillovers of activity, complex patterns of editor engagement, and the shadows of the future. In light of increasing concerns about online social platforms abilities to govern the conduct and content of their users, we identify implications from Wikipedias coronavirus collaborations for improving the resilience of socio-technical systems during a crisis.
A large amount of content is generated everyday in social media. One of the main goals of content creators is to spread their information to a large audience. There are many factors that affect information spread, such as posting time, location, type of information, number of social connections, etc. In this paper, we look at the problem of finding the best posting time(s) to get high content visibility. The posting time is derived taking other factors into account, such as location, type of information, etc. In this paper, we do our analysis over Facebook pages. We propose six posting schedules that can be used for individual pages or group of pages with similar audience reaction profile. We perform our experiment on a Facebook pages dataset containing 0.3 million posts, 10 million audience reactions. Our best posting schedule can lead to seven times more number of audience reactions compared to the average number of audience reactions that users would get without following any optimized posting schedule. We also present some interesting audience reaction patterns that we obtained through daily, weekly and monthly audience reaction analysis.
As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two new communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, evidenced by both language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly loyal. To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
