Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries

Emmert‐Streib, Frank; Dehmer, Matthias

doi:10.3389/fdata.2021.591749

Cited by 8 publications

(4 citation statements)

References 88 publications

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“…Urban % share meetings. This seems to be in line with recent results that showed that through the multiplication of exchanges, the distance between individuals taken at random decreases time after time (Emmert-Streib & Dehmer, 2021).…”

Section: Rurale Populationsupporting

confidence: 92%

Sociability Networks in the City of Bujumbura

Toyi,

Sindayigaya

2023

JSS

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Faced with the profound changes predicting transformations in the mindsets, Bujumbura got a new look that created social networks. The target of this article is to analyze the role and importance of social networks in the everyday life of Bujumbura citizens. The researcher used documentary techniques to inquire through some associations' reports and their statutes what their objective is, aiming to survey how useful networking socially is. Results show that people in Bujumbura are gathered in associations especially according to professions such as driving bicycles (taxi-velo), and women who sew clothes. Results confirm that networking is also operational among people according to the generation whether old people or youth helping one atoner. Results finally attest that, in Bujumbura, in compliance with the law on cooperatives, even though membership in associations is free, social networks formed in cooperatives (Sangwe) are viewed as having a political-eyed target spread on every hill or quarter in the country but their members, though thriving to succeed, are facing many problems even though they are founded by the governmental budget. Social networks are of great importance in Bujumbura in as much as Burundi esteems that development is quickly achieved in group than being alone.

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Section: Rurale Populationsupporting

confidence: 92%

Sociability Networks in the City of Bujumbura

Toyi,

Sindayigaya

2023

JSS

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“…Finally, we would like to note that our study has similarities to recent investigations in Explainable Artificial Intelligence (XAI) (Xu et al, 2019;Emmert-Streib et al, 2020). Specifically, XAI explores the dichotomy of predictive and descriptive models (Emmert-Streib and Dehmer, 2021) in AI and aims to establish mechanisms for making predictive models also explainable in a sense that this can enhance our understanding of a system under investigation. On a wider scope, this discussion has a long history in the statistics community and refers to the distinction of black-box models and causal models (Holland, 1986;Breiman, 2001).…”

Section: Discussionmentioning

confidence: 74%

Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer

et al. 2021

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High-throughput technologies do not only provide novel means for basic biological research but also for clinical applications in hospitals. For instance, the usage of gene expression profiles as prognostic biomarkers for predicting cancer progression has found widespread interest. Aside from predicting the progression of patients, it is generally believed that such prognostic biomarkers also provide valuable information about disease mechanisms and the underlying molecular processes that are causal for a disorder. However, the latter assumption has been challenged. In this paper, we study this problem for prostate cancer. Specifically, we investigate a large number of previously published prognostic signatures of prostate cancer based on gene expression profiles and show that none of these can provide unique information about the underlying disease etiology of prostate cancer. Hence, our analysis reveals that none of the studied signatures has a sensible biological meaning. Overall, this shows that all studied prognostic signatures are merely black-box models allowing sensible predictions of prostate cancer outcome but are not capable of providing causal explanations to enhance the understanding of prostate cancer.

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“…Computational discourse analysis uses natural language processing (NLP) methods to automatically detect cohesion and local coherence that can help in making summative inferences about documents [37]. This is a method derived from data science that is increasingly being applied to harvest data and build archives from social media and World Wide Web pages [38,39] and also to do predictive modeling [40]. As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Disparities and resilience: analyzing online Health information provision, behaviors and needs of LBGTQ + elders during COVID-19

Fan

Gilliland

2022

BMC Public Health

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Background Prior studies indicate that older members of LGBTQ+ communities have specific health provision and health information needs related to coping with COVID-19, its long-term effects, and the social and economic impact of the pandemic. This study addresses the issue of a lack of timely, complete, and high-quality data about this population’s healthcare and healthcare information needs and behaviors. Recognizing also that this is a diverse population made up of multiple communities and identities with different concerns and experiences, this research seeks to develop and refine a method that can provide additional nuanced data and insights that can support improved and more closely targeted health interventions and online information provision. Methods We use computational discourse analysis, which is based on NLP algorithms, to build and analyze a digital corpus of online search results containing rich, wide-ranging content such as quotes and anecdotes from older members of LGBTQ+ communities as well as practitioners, advice, and recommendations from policymakers and healthcare experts, and research outcomes. In our analysis, we develop and apply an innovative disparities and resilience (D&R) framework to identify external and internal perspectives and understand better disparities and resilience as they pertain to this population. Results Results of this initial study support previous research that LGBTQ+ elders experience aggravated health and related social-economic disparities in comparison to the general population of older people. We also find that LGBTQ+ elders leverage individual toughness and community closeness, and quickly adapt mentally and technologically, despite inadequate social infrastructure for sharing health information and elders’ often low social economic status. The methods used therefore are able to surface distinctive resilience in the face of distinctive disparities. Conclusions Our study provides evidence that methodological innovation in gathering and analyzing digital data relating to overlooked, disparately affected, and socially and economically marginalized intersectional communities such as LGBTQ+ elders can result in increased external and self-knowledge of these populations. Specifically, it demonstrates the potential of computational discourse analysis to surface hidden and emerging issues and trends relating to a multi-faceted population that has important concerns about public exposure in highly timely and automated ways. It also points to the potential benefits of triangulating data gathered through this approach with data gathered through more traditional mechanisms such as surveys and interviews. Trial registration Not Applicable.

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Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries

Cited by 8 publications

References 88 publications

Sociability Networks in the City of Bujumbura

Sociability Networks in the City of Bujumbura

Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer

Disparities and resilience: analyzing online Health information provision, behaviors and needs of LBGTQ + elders during COVID-19

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