Journalism, Algorithms, and the People's Right to Know

The COVID-19 pandemic is the saddest proof of the importance of data. Decisions are made based on information about the spread of the virus and countries that have poor local data are at most risk. This chapter advocates for an approach to journalism that would embrace The Disruption with the ultimate purpose of defending its core mission, safeguarding "the people's right to know.” Several studies have proved how extensively data is now being used in the media. Statistical information has become a "crucial tool to shape public opinion” and has also become widely used by journalists. Data journalism is flourishing and serving the best investigative journalism. Journalists with basic data skills can access open data portals at the local and regional levels and better monitor how the public money is being employed. Mainstream media needs to dedicate many more journalists to investigate algorithms and platforms in general. © 2021 John Wiley and Sons, Inc.

Mining the Health Information Needs of COVID-19 Patients Based on Social Q&A Community

COVID-19 has become a global pandemic, and COVID-19 patients are in a medical dilemma with no effective treatment and no effective drugs. The questions and answers in the social Q&A community can reveal the characteristics and evolution rules of the health information needs of COVID-19 patients. Using the Q&A data in Baidu Zhidao (https://zhidao.baidu.com/ ) as the research object, using the web crawlers to capture the data, automatic topic recognition on the acquired data by constructing an LDA topic model, exploring the content of COVID-19 patients' health information needs, and revealing the change rule of Q&A publication volume and health information need topics from the time dimension. Combining statistical information such as the number of answers, the number of likes, and the level of respondents, cluster analysis is used to reveal the changing rules of social characteristics and health information need topics. By analyzing the Q&A data on COVID-19 patients in Baidu Zhidao, it is found that the topic distribution of health information needs topic is relatively concentrated. Moreover, the number of Q&A and the types of health information needs to be changed in different development periods. There are differences in social characteristics that correspond to different topics of health information needs. Through in-depth analysis of the characteristics of health information needs of COVID-19 patients in the social Q&A community, on the one hand, it is beneficial for COVID-19 patients to obtain the required health information content timely. On the other hand, it is beneficial to optimize the community information display mechanism and improve the organization of information resources. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The challenge of generating knowledge for intervention in contexts of urgency and social isolation. The work experience in the framework of the FaHCE and IdIHCS Commission of Surveying and Monitoring of the Sanitary Emergency in Gran La Plata (2020-2021)

These notes present - with particular emphasis on the methodological and practical challenges associated with the available sources and the context - a thoughtful overview of the compilation work, production, analysis of information and preparation of documents carried out in the framework of the Commission of Surveying and Monitoring of the Sanitary Emergency in Gran La Plata, which was constituted in the context of the Covid-19 pandemic at the initiative of FaHCE-UNLP and IdIHCS with the objective of providing socio-sanitary information to the university actors who were working in the territory. The task involved a series of challenges mainly linked to the possibilities of accessing and/or producing, as quickly as possible, reliable and up-to-date statistical information on the living conditions in the vulnerable neighborhoods of La Plata Town and on the evolution of the epidemiological situation in Gran La Plata region, recovering the social and demographic aspects. Furthermore, the additional challenge of disaggregating information territorially by municipal delegations of La Plata Town or conducting comparative analyses by towns.

RSFAD: A Large-Scale Real Scenario Face Age Dataset in the wild

Age estimation is a hot and challenging research topic in the computer vision community. Several facial datasets annotated with age and gender attributes became available in recent years. However, the statistical information of these datasets reveal the unbalanced label distribution which inevitably introduce bias during model training. In this work, we manually collect and label a large-scale age dataset called Real Scenario Face Age Dataset (RSFAD) which contains 85, 044 facial images captured from surveillance cameras in the wild. Due to the COVID-19, we not only label the apparent age group and gender but also label the breathing mask, and the label distribution of RSFAD dataset is almost uniform which is the first age dataset to the best of our knowledge. In addition, we investigate the impact of age, gender and mask distribution on age group estimation by comparing GDEX CNN model trained on several different datasets. Our experiments show that the RSFAD dataset has good performance for age estimation task and also it is suitable for being an evaluation benchmark. © 2021 IEEE.

COVID-19 health misinformation: using design-based research to develop a theoretical framework for intervention

Purpose Because health misinformation pertaining to COVID-19 is a serious threat to public health, the purpose of this study is to develop a framework to guide an online intervention into some of the drivers of health misinformation online. This framework can be iterated upon through the use of design-based research to continue to develop further interventions as needed. Design/methodology/approach Using design-based research methods, in this paper, the authors develop a theoretical framework for addressing COVID-19 misinformation. Using a heuristic analysis of research on vaccine misinformation and hesitancy, the authors propose a framework for education interventions that use the narrative effect of transportation as a means to increase knowledge of the drivers of misinformation online. Findings This heuristic analysis determined that a key element of narrative transportation includes orientation towards particular audiences. Research indicates that mothers are the most significant household decision-makers with respect to vaccines and family health in general;the authors suggest narrative interventions should be tailored specifically to meet their interests and tastes, and that this may be different for mothers of different backgrounds and cultural communities. Originality/value While there is a significant body of literature on vaccine hesitancy and vaccine misinformation, more research is needed that helps people understand the ways in which misinformation works upon social media users. The framework developed in this research guided the development of an education intervention meant to facilitate this understanding.