Vulnerability in Social Epistemic Networks

Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks

Can Real Social Epistemic Networks Deliver the Wisdom of Crowds?

In this paper, we explain and showcase the promising methodology of testimonial network analysis and visualization for experimental epistemology, arguing that it can be used to gain insights and answer philosophical questions in social epistemology. Our use case is the epistemic community that discusses vaccine safety primarily in English on Twitter. In two studies, we show, using both statistical analysis and exploratory data visualization, that there is almost no neutral or ambivalent discussion of vaccine safety on Twitter. Roughly half the accounts engaging with this topic are pro-vaccine, while the other half are con-vaccine. We also show that these two camps rarely engage with one another, and that the con-vaccine camp has greater epistemic reach and receptivity than the pro-vaccine camp. In light of these findings, we question whether testimonial networks as they are currently constituted on popular fora such as Twitter are living up to their promise of delivering the wisdom of crowds. We conclude by pointing to directions for further research in digital social epistemology

Pandemic Influenza: Ethics, Law, and the Public\u27s Health

Highly pathogenic Influenza (HPAI) has captured the close attention of policy makers who regard pandemic influenza as a national security threat. Although the prevalence is currently very low, recent evidence that the 1918 pandemic was caused by an avian influenza virus lends credence to the theory that current outbreaks could have pandemic potential. If the threat becomes a reality, massive loss of life and economic disruption would ensue. Therapeutic countermeasures (e.g., vaccines and antiviral medications) and public health interventions (e.g., infection control, social separation, and quarantine) form the two principal strategies for prevention and response, both of which present formidable legal and ethical challenges that have yet to receive sufficient attention. In part II, we examine the major medical countermeasures being being considered as an intervention for an influenza pandemic. In this section, we will evaluate the known effectiveness of these interventions and analyze the ethical claims relating to distributive justice in the allocation of scarce resources. In part III, we will discuss public health interventions, exploring the hard tradeoffs between population health on the one hand and personal (e.g., autonomy, privacy, and liberty) and economic (e.g., trade, tourism, and business) interests on the other. This section will focus on the ethical and human rights issues inherent in population-based interventions. Pandemics can be deeply socially divisive, and the political response to these issues not only impacts public health preparedness, but also reflects profoundly on the kind of society we aspire to be

Optimal Resource Allocation Over Time and Degree Classes for Maximizing Information Dissemination in Social Networks

We study the optimal control problem of allocating campaigning resources over the campaign duration and degree classes in a social network. Information diffusion is modeled as a Susceptible-Infected epidemic and direct recruitment of susceptible nodes to the infected (informed) class is used as a strategy to accelerate the spread of information. We formulate an optimal control problem for optimizing a net reward function, a linear combination of the reward due to information spread and cost due to application of controls. The time varying resource allocation and seeds for the epidemic are jointly optimized. A problem variation includes a fixed budget constraint. We prove the existence of a solution for the optimal control problem, provide conditions for uniqueness of the solution, and prove some structural results for the controls (e.g. controls are non-increasing functions of time). The solution technique uses Pontryagin's Maximum Principle and the forward-backward sweep algorithm (and its modifications) for numerical computations. Our formulations lead to large optimality systems with up to about 200 differential equations and allow us to study the effect of network topology (Erdos-Renyi/scale-free) on the controls. Results reveal that the allocation of campaigning resources to various degree classes depends not only on the network topology but also on system parameters such as cost/abundance of resources. The optimal strategies lead to significant gains over heuristic strategies for various model parameters. Our modeling approach assumes uncorrelated network, however, we find the approach useful for real networks as well. This work is useful in product advertising, political and crowdfunding campaigns in social networks.Comment: 14 + 4 pages, 11 figures. Author's version of the article accepted for publication in IEEE/ACM Transactions on Networking. This version includes 4 pages of supplementary material containing proofs of theorems present in the article. Published version can be accessed at http://dx.doi.org/10.1109/TNET.2015.251254

Moving from rabies research to rabies control: lessons from India

BACKGROUND: Despite the availability of effective interventions and public recognition of the severity of the problem, rabies continues to suffer neglect by programme planners in India and other low and middle income countries. We investigate whether this state of 'policy impasse' is due to, at least in part, the research community not catering to the information needs of the policy makers. METHODS & FINDINGS: Our objective was to review the research output on rabies from India and examine its alignment with national policy priorities. A systematic literature review of all rabies research articles published from India between 2001 and 2011 was conducted. The distribution of conducted research was compared to the findings of an earlier research prioritization exercise. It was found that a total of 93 research articles were published from India since 2001, out of which 61% consisted of laboratory based studies focussing on rabies virus. Animals were the least studied group, comprising only 8% of the research output. One third of the articles were published in three journals focussing on vaccines and infectious disease epidemiology and the top 4 institutions (2 each from the animal and human health sectors) collectively produced 49% of the national research output. Biomedical research related to development of new interventions dominated the total output as opposed to the identified priority domains of socio-politic-economic research, basic epidemiological research and research to improve existing interventions. CONCLUSION: The paper highlights the gaps between rabies research and policy needs, and makes the case for developing a strategic research agenda that focusses on rabies control as an expected outcome

Analyzing fluctuation of topics and public sentiment through social media data

Over the past decade years, Internet users were expending rapidly in the world. They form various online social networks through such Internet platforms as Twitter, Facebook and Instagram. These platforms provide a fast way that helps their users receive and disseminate information and express personal opinions in virtual space. When dealing with massive and chaotic social media data, how to accurately determine what events or concepts users are discussing is an interesting and important problem. This dissertation work mainly consists of two parts. First, this research pays attention to mining the hidden topics and user interest trend by analyzing real-world social media activities. Topic modeling and sentiment analysis methods are proposed to classify the social media posts into different sentiment classes and then discover the trend of sentiment based on different topics over time. The presented case study focuses on COVID-19 pandemic that started in 2019. A large amount of Twitter data is collected and used to discover the vaccine-related topics during the pre- and post-vaccine emergency use period. By using the proposed framework, 11 vaccine-related trend topics are discovered. Ultimately the discovered topics can be used to improve the readability of confusing messages about vaccines on social media and provide effective results to support policymakers in making their policy their informed decisions about public health. Second, using conventional topic models cannot deal with the sparsity problem of short text. A novel topic model, named Topic Noise based-Biterm Topic Model with FastText embeddings (TN-BTMF), is proposed to deal with this problem. Word co-occurrence patterns (i.e. biterms) are dirctly generated in BTM. A scoring method based on word co-occurrence and semantic similarity is proposed to detect noise biterms. In th

Covid-19 Vaccines in Italian public opinion: identifying key issues using Twitter and Natural Language Processing

The COVID-19 pandemic has changed society and people’s lives. The vaccination campaign started December 27-th 2020 in Italy, together with most countries in the European Union. Social media platforms can offer relevant information about how citizens have experienced and perceived the availability of vaccines and the start of the vaccination campaign. This study aims to use machine learning methods to extract sentiments and topics relating to COVID-19 vaccination from Twitter. Between February and May 2021, we collected over 71,000 tweets containing vaccines-related keywords from Italian Twitter users. To get the dominant sentiment throughout the Italian population, spatial and temporal sentiment analysis was performed using VADER, highlighting sentiment fluctuations strongly influenced by news of vaccines' side effects. Additionally, we investigated the opinions of Italians with respect to different vaccine brands. As a result, ‘Oxford-AstraZeneca’ vaccine was the least appreciated among people. The application of the Dynamic Latent Dirichlet Allocation (DLDA) model revealed three fundamental topics, which remained stable over time: vaccination plan info, usefulness of vaccinating and concerns about vaccines (risks, side effects and safety). To the best of our current knowledge, this one the first study on Twitter to identify opinions about COVID-19 vaccination in Italy and their progression over the first months of the vaccination campaign. Our results can help policymakers and research communities track public attitudes towards COVID-19 vaccines and help them make decisions to promote the vaccination campaign