Toward Data Sense-Making in Digital Health Communication Research: Why Theory Matters in the Age of Big Data

Lee, Edmund Wei Jian; Yee, Andrew

doi:10.3389/fcomm.2020.00011

Cited by 24 publications

(25 citation statements)

References 78 publications

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“…As with all studies, ours had some limitations. First, at the fundamental level, our Twitter sample was not nationally representative and for that reason, scholars should be cautious in interpreting the results of our study and should avoid generalizing our findings to the entire US population; we remain mindful of the problem of data hubris and avoid making any causal claims (Lee and Yee, 2020). Second, we accounted for only three types of cancer and did not examine tweets pertaining to other types (e.g.…”

Section: Discussionmentioning

confidence: 99%

Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach

Wang

Lee

2020

INTR

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PurposeDrawing on the cognitive-functional model of emotions and emotional contagion, the authors aim to examine the role of negative emotions in the diffusion of cancer tweets.Design/methodology/approachUsing an integrated approach of social network and text analytics, the authors analyzed 142,883 cancer tweets from February to March 2018. The roles of negative emotions, emotional contagion, cancer themes and user influence on the diffusion of cancer tweets were examined.FindingsResults indicated that cancer tweets expressing negativity and anger diffused more widely, while those expressing sadness or fear were less likely to diffuse. However, contrary to the authors’ expectation, cancer tweets expressing negative emotions (i.e. negativity, anger and fear) were less likely to arouse similar emotions among retweets, thus suggesting that emotions in cancer tweets were not as contagious as they seemed. Finally, user influence was the most important factor explaining the diffusion of cancer tweets, although cancer-related themes (i.e. affective, informative and social) had marginal effects on likelihood of diffusion.Originality/valueUsing a novel integrated social network–text analytics approach, the authors found that to understand cancer tweets' diffusion, it is critical to go beyond examining the content of tweets about cancer and the influence of messengers – the virality of cancer tweets is inextricable from the negative emotions.

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Section: Discussionmentioning

confidence: 99%

Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach

Wang

Lee

2020

INTR

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“…At the policy and system architecture level, Ethica was built to be compliant with the General Data Protection Regulation requirements, which extended data protection for different types of health data collected from individuals [ 18 , 40 ]. In other words, our participants had the right to access and delete their own data.…”

Section: Discussionmentioning

confidence: 99%

“…After all, smartphone penetration in the United States is high, with about 81% of the population owning a smartphone; the smartphone ownership figures are also high for underserved communities, such as people living in rural areas (71%), those making less than US $30,000 annually (71%), and in minority communities (approximately 79% to 80%) [ 17 ]. Health promotion organizations and policy makers could take advantage of the contextual information provided by smartphones to identify strategic areas to help ensure adequate exposure to antitobacco messages [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study

et al. 2020

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Background People from underserved communities such as those from lower socioeconomic positions or racial and ethnic minority groups are often disproportionately targeted by the tobacco industry, through the relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or protobacco marketing and promotional strategies. It is difficult to capture the smoking behaviors of individuals in actual locations as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the United States—when used alongside data sources on TRO locations—apps could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing, particularly in relation to the locations of TROs as well as locations where they were most likely to smoke. To date, it remains unclear how health apps could be used practically by health promotion organizations to better reach underserved communities in their tobacco control efforts. Objective This study aimed to demonstrate how smartphone apps could augment existing data on locations of TROs within underserved communities in Massachusetts and Texas to help inform tobacco control efforts. Methods Data for this study were collected from 2 sources: (1) geolocations of TROs from the North American Industry Classification System 2016 and (2) 95 participants (aged 18 to 34 years) from underserved communities who resided in Massachusetts and Texas and took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hot spot analysis, and fitted power-law distribution to identify the TROs that attracted the most human traffic using mobility data. Results Participants reported encountering protobacco messages mostly from store signs and displays and antitobacco messages predominantly through television. In Massachusetts, clusters of TROs (Dorchester Center and Jamaica Plain) and reported smoking behaviors (Dorchester Center, Roxbury Crossing, Lawrence) were found in economically disadvantaged neighborhoods. Despite the widespread distribution of TROs throughout the communities, participants overwhelmingly visited a relatively small number of TROs in Roxbury and Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land, and Missouri City) were found primarily in Houston, whereas clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown. Conclusions Smartphone apps could be used to pair geolocation data with self-reported smoking behavior in order to gain a better understanding of how tobacco product marketing and promotion influence smoking behavior within vulnerable communities. Public health officials could take advantage of smartphone data collection capabilities to implement targeted tobacco control efforts in these strategic locations to reach underserved communities in their built environment.

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“…Another issue is that candidate quality cannot be truly known until after the student matriculates. This case is a good example of data hubris, or "overstated claims that arise from big data analysis" [84]. This is particularly problematic when using data to "make causal claims from an inherently inductive method of pattern recognition" [19,84,89].…”

Section: Real-world Examplementioning

confidence: 99%

“…This case is a good example of data hubris, or "overstated claims that arise from big data analysis" [84]. This is particularly problematic when using data to "make causal claims from an inherently inductive method of pattern recognition" [19,84,89]. DEPLOYMENT STAGE This stage is where users start to interact with the developed technology, and sometimes create unintended uses for it.…”

Section: Real-world Examplementioning

confidence: 99%

A Proposal for Identifying and Managing Bias in Artificial Intelligence

Schwartz¹,

Down²,

Jonas³

et al. 2021

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NIST contributes to the research, standards, evaluation, and data required to advance the development and use of trustworthy artificial intelligence (AI) to address economic, social, and national security challenges and opportunities. Working with the AI community, NIST has identified the following technical characteristics needed to cultivate trust in AI systems: accuracy, explainability and interpretability, privacy, reliability, robustness, safety, and security (resilience) -and that harmful biases are mitigated. Mitigation of risk derived from bias in AIbased products and systems is a critical but still insufficiently defined building block of trustworthiness. This report proposes a strategy for managing AI bias, and describes types of bias that may be found in AI technologies and systems. The proposal is intended as a step towards consensus standards and a risk-based framework for trustworthy and responsible AI. The document, which also contains an alphabetical glossary that defines commonly occurring biases in AI, contributes to a fuller description and understanding of the challenge of harmful bias and ways to manage its presence in AI systems. Key wordsbias, trustworthiness, AI safety, AI lifecycle, AI development provide their valuable feedback. AudienceThe main audience for this document is researchers and practitioners in the field of trustworthy and responsible artificial intelligence. Researchers will find this document useful for understanding a view of the challenge of bias in AI, and as an initial step toward the development of standards and a risk framework for building and using trustworthy AI systems. Practitioners will benefit by gaining an understanding about bias in the use of AI systems. Trademark InformationAll trademarks and registered trademarks belong to their respective organizations. Note to ReviewersAs described throughout this report, one goal for NIST's work in trustworthy AI is the development of a risk management framework and accompanying standards. To make the necessary progress towards that goal, NIST intends to carry out a variety of activities in 2021 and 2022 in each area of the core building blocks of trustworthy AI (accuracy, explainability and interpretability, privacy, reliability, robustness, safety, and security (resilience), and mitigation of harmful bias). This will require a concerted effort, drawing upon experts from within NIST and external stakeholders. NIST seeks additional collaborative feedback from members of the research, industry, and practitioner community throughout this process. All interested parties are encouraged to please submit comments about this draft report, and the types of activities and events which would be helpful, via the public comment process described on page 3 of this document. There will also be opportunities for engaging in discussions about and contributing to development of key practices and tools to manage Bias in AI. Please look for announcements for webinars, call for position papers, and request for comment on NIST document(s).

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Toward Data Sense-Making in Digital Health Communication Research: Why Theory Matters in the Age of Big Data

Cited by 24 publications

References 78 publications

Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach

Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach

The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study

A Proposal for Identifying and Managing Bias in Artificial Intelligence

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