scholarly journals #Datasaveslives: mixed methods analysis of a social media campaign to promote the benefits of using health data for research purposes (Preprint)

2019 ◽  
Author(s):  
Lamiece Hassan ◽  
Goran Nenadic ◽  
Mary Patricia Tully
Keyword(s):  
Social Media ◽  
Health Informatics ◽  
Data Science ◽  
Health Data ◽  
Media Campaign ◽  
Wide Audience ◽  
Personal Support ◽  
Twitter Users ◽  
The Uk ◽  
The Impact

BACKGROUND Social media provides the potential to engage a wide audience about scientific research, including the public. However little empirical research exists to guide health scientists regarding what works and how to optimize impact. We examined the social media campaign #datasaveslives, which was established in 2014 to highlight positive examples of the use and reuse of health data in research. OBJECTIVE The study aimed to examine how the #datasaveslives hashtag was used on social media, how often and by whom; thus, the study aimed to provide insights into the impact of a major social media campaign in the UK health informatics research community and further afield. METHODS We analyzed all publicly available posts (tweets) between 1 September 2016 and 31 August 2017 on the microblogging platform Twitter that included the hashtag #datasaveslives (n=13,895). Using a combination of qualitative and quantitative analyses, we determined the frequency and purpose of tweets. Social network analysis was used to analyze and visualize tweet sharing (‘retweet’) networks among hashtag users. RESULTS Overall, we found 4,175 original tweets and 9,720 retweets featuring #datasaveslives by 3,649 unique Twitter users. In total, 2,756 (66.0%) of original posts were retweeted at least once. Higher frequencies of tweets were observed during the weeks of prominent policy publications, popular conferences and public engagement events. Cluster analysis based on retweet relationships revealed an interconnected series of groups of #datasaveslives users in academia, health services and policy, and charities and patient networks. Thematic analysis of tweets showed that #datasaveslives was used for a broader range of purposes than indexing information, including event reporting, encouraging participation and action, and showing personal support for data sharing. CONCLUSIONS This study shows that a hashtag-based social media campaign was effective in encouraging a wide audience of stakeholders to disseminate positive examples of health research. Furthermore, the findings suggest the campaign supported community-building and bridging practices within and between the interdisciplinary sectors related to the field of health data science and encouraged individuals to demonstrate personal support for sharing health data. CLINICALTRIAL

scholarly journals HDR UK supporting mobilising computable biomedical knowledge in the UK

2020 ◽  
Vol 27 (2) ◽  
pp. e100122
Author(s):  
Neil J Sebire ◽  
Caroline Cake ◽  
Andrew D Morris
Keyword(s):  
Health Informatics ◽  
Data Science ◽  
Open Data ◽  
Health Data ◽  
Biomedical Knowledge ◽  
Domain Specific ◽  
Research And Innovation ◽  
Use Of Data ◽  
Main Components ◽  
The Uk

Computable biomedical knowledge (CBK) represents an evolving area of health informatics, with potential for rapid translational patient benefit. Health Data Research UK (HDR UK) is the national Institute for Health Data Science, whose aim is to unite the UK’s health data to enable discoveries that improve people’s lives. The three main components include the UK HDR Alliance of data custodians, committed to making health data available for research and innovation purposes for public benefit while ensuring safe use of data and building public trust, the HDR Hubs, as centres of expertise for curating data and providing expert domain-specific services, and the HDR Innovation Gateway (‘Gateway’), providing discovery, accessibility, security and interoperability services. To support CBK developments, HDR UK is encouraging use of open data standards for research purposes, with guidance around areas in which standards are emerging, aims to work closely with the international CBK community to support initiatives and aid with evaluation and collaboration, and has established a phenomics workstream to create a national platform for dissemination of machine readable and computable phenotypical algorithms to reduce duplication of effort and improve reproducibility in clinical studies.

scholarly journals Evaluation Considerations for Secondary Uses of Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis

2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  
Keyword(s):  
Clinical Data ◽  
Health Informatics ◽  
Best Practice ◽  
Data Science ◽  
Clinical Performance ◽  
Secondary Analysis ◽  
Health Data ◽  
Evidence Based ◽  
Great Expectations ◽  
Scientific Context

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.

scholarly journals Big data analytics of corporate internet disclosures

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamed A.K. Basuony ◽  
Ehab K.A. Mohamed ◽  
Ahmed Elragal ◽  
Khaled Hussainey
Keyword(s):  
Social Media ◽  
Firm Size ◽  
Data Science ◽  
Big Data Analytics ◽  
Corporate Disclosure ◽  
Content Type ◽  
Science Approach ◽  
The Usa ◽  
Corporate Office ◽  
The Uk

Purpose This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA. Design/methodology/approach A disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA. Findings The results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure. Practical implications This paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process. Originality/value To the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.

scholarly journals Evaluation Considerations for Secondary Uses of Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis

2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  
Keyword(s):  
Clinical Data ◽  
Health Informatics ◽  
Best Practice ◽  
Data Science ◽  
Clinical Performance ◽  
Secondary Analysis ◽  
Health Data ◽  
Evidence Based ◽  
Great Expectations ◽  
Scientific Context

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.

scholarly journals Defending the media from itself: Reforming media regulations to minimise the impact of misinformation

European View ◽  
2019 ◽  
Vol 18 (2) ◽  
pp. 210-219
Author(s):  
Juha-Pekka Nurvala ◽  
Amelia Buckell
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Case Studies ◽  
Search Engines ◽  
Behavioural Changes ◽  
Social Media Platforms ◽  
The Media ◽  
The Uk ◽  
The Impact

This article argues that media regulations on correcting incorrect articles are in dire need of reform due to technological and behavioural changes. By using case studies from the UK, the authors demonstrate the huge difference between the number of people who were reached by the original article before the Independent Press Standards Organisation (the regulator in the UK) ruled it incorrect and the number reached by the correction or corrected article. The authors argue that media regulations must be reformed to ensure that corrections reach the same people as the original incorrect article to avoid misinformation impacting peoples’ decision-making, and that reforms must include social media platforms and search engines.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

Conclusion

2021 ◽  
pp. 240-272
Author(s):  
David Denver ◽  
Mark Garnett
Keyword(s):  
Social Media ◽  
Electoral System ◽  
The Internet ◽  
Voting Behaviour ◽  
House Of Commons ◽  
Voting System ◽  
Preceding Discussion ◽  
The 1960S ◽  
The Uk ◽  
The Impact

This chapter sums up the preceding discussion and examines the radical changes in the nature of electoral competition in the UK since 1964. In particular, it assesses the impact on campaigning of social media and the Internet. It also discusses the impact of social change on voting behaviour over the years, as well as the transformation of political parties and the very different composition of the House of Commons. These various changes had occurred while UK-wide elections are still conducted under the Simple Plurality (‘first-past-the-post’) electoral system, although a variety of different systems have been adopted for virtually all other elections. Thus, by 2021, almost the only factor in UK elections which has remained constant since 1964 is the voting system. In other respects, the volatility which has become increasingly marked since the 1960s looks set to continue.

scholarly journals Knowing the Tweeters: Deriving Sociologically Relevant Demographics from Twitter

2013 ◽  
Vol 18 (3) ◽  
pp. 74-84 ◽  
Author(s):  
Luke Sloan ◽  
Jeffrey Morgan ◽  
William Housley ◽  
Matthew Williams ◽  
Adam Edwards ◽  
...  
Keyword(s):  
Social Media ◽  
Spatial Information ◽  
Demographic Data ◽  
Social Science Research ◽  
Science Research ◽  
Geographical Information ◽  
Online Social Media ◽  
Twitter Users ◽  
Use Of Social Media ◽  
The Uk

A perennial criticism regarding the use of social media in social science research is the lack of demographic information associated with naturally occurring mediated data such as that produced by Twitter. However the fact that demographics information is not explicit does not mean that it is not implicitly present. Utilising the Cardiff Online Social Media ObServatory (COSMOS) this paper suggests various techniques for establishing or estimating demographic data from a sample of more than 113 million Twitter users collected during July 2012. We discuss in detail the methods that can be used for identifying gender and language and illustrate that the proportion of males and females using Twitter in the UK reflects the gender balance observed in the 2011 Census. We also expand on the three types of geographical information that can be derived from Tweets either directly or by proxy and how spatial information can be used to link social media with official curated data. Whilst we make no grand claims about the representative nature of Twitter users in relation to the wider UK population, the derivation of demographic data demonstrates the potential of new social media (NSM) for the social sciences. We consider this paper a clarion call and hope that other researchers test the methods we suggest and develop them further.

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