Engaging with Digital Research - Further Reading
Here is a joint resource on digital research - please feel free to edit and develop
Indicators, metrics, rating, ranking
Table of contents
1. Social media and the measurement of audiences in arts and media [edit]
2. Indicators and Public Policy [edit]
3. Social media indicators as predictors of future in markets etc [edit]
4. Google Flu and Digital Disease Monitoring [edit]
Social media and the measurement of audiences in arts and media [edit]
Franklin, Michael (2013) 'What metrics really mean, a question of causality and construction in leveraging social media audiences into business results: Cases from the UK film industry', Participations Volume 10, Issue 2
Social Media Buzz: Young Audiences Focused on ‘Divergent’ at Weekend Box Office, Variety MARCH 21, 2014 |
National Benchmarking Key Performance Indicators, Indigo, funded by Arts Council England, 2011
Indicators and Public Policy [edit]
Judith Innes de Neufville 1990 Knowledge and Public Policy: The Search for Meaningful Indicators, Transaction
10 GUIDING PRINCIPLES AND METHODOLOGY FOR ASSESSING AND REPORTING PROGRESS ON SCOTLAND PERFORMS, Scottish Government
Handbook on constructing composite indicators: methodology and user guide, JRC and OECD - JCR Composite Indicators Group
GARRY JACOBS, IVO ŠLAUS Indicators of Economic Progress: The Power of Measurement and Human Welfare, Cadmus OCTOBER 3, 2010
László Pintér and Darren Swanson with Jane E. Barr Use of Indicators in Policy Analysis, Annotated Training Module prepared for the World Bank Institute March 2004
Indicators of Innovation
Measuring innovation: the use of indicators in developing policy – video about the Handbook of Innovation indicators by Fred Gault
Identifying European Poles of Excellence: The Methodology. Giuditta De Prato, Daniel Nepelski. JRC85356 - a complex 'big data' approach to innovation indicators
Social media indicators as predictors of future in markets etc [edit]
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. doi:10.1016/j.jocs.2010.12.007
Asur and Guberman 2010, Predicting the Future with Social Media http://arxiv.org/pdf/1003.5699.pdf
- comparing predictions from twitter with crowdsourced 'market' predictions
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59. http://doi.org/10.1016/j.inffus.2015.08.005 -summary of latest approaches and uses.
Google Flu and Digital Disease Monitoring [edit]
For a really interesting and broader range of projects got to the Computational Epidemiology Lab website at the Boston Children's Hospital
Ginsberg et al (2009) Detecting influenza epidemics using search engine query data, Nature 457, 1012-1014 (19 February 2009) | doi:10.1038/nature07634; http://www.nature.com/nature/journal/v457/n7232/full/nature07634.html
When Google got flu wrong, US outbreak foxes a leading web-based method for tracking seasonal flu, Declan Butler, 2013 Nature.com http://www.nature.com/news/when-google-got-flu-wrong-1.12413
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Science, 343(6176), 1203–1205. http://doi.org/10.1126/science.1248506
Flunearyou.org : Crowdsourcing Flu data https://flunearyou.org
Choi, J., Cho, Y., Shim, E., & Woo, H. (2016). Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health, 16(1), 1238. http://doi.org/10.1186/s12889-016-3893-0
Huang, D.-C., Wang, J.-F., Huang, J.-X., Sui, D. Z., Zhang, H.-Y., Hu, M.-G., & Xu, C.-D. (2016). Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data. PLOS Computational Biology, 12(6), e1004876. http://doi.org/10.1371/journal.pcbi.1004876
Davidson, M. W., Haim, D. A., & Radin, J. M. (2015). Using Networks to Combine “Big Data” and Traditional Surveillance to Improve Influenza Predictions. Scientific Reports, 5, 8154. http://doi.org/10.1038/srep08154
Olson, D. R., Konty, K. J., Paladini, M., Viboud, C., & Simonsen, L. (2013). Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales. PLoS Computational Biology, 9(10), e1003256. http://doi.org/10.1371/journal.pcbi.1003256
Lyon, A., Nunn, M., Grossel, G., & Burgman, M. (2012). Comparison of Web-Based Biosecurity Intelligence Systems: BioCaster, EpiSPIDER and HealthMap. Transboundary and Emerging Diseases, 59(3), 223–232. http://doi.org/10.1111/j.1865-1682.2011.01258.x
Ortiz, J. R., Zhou, H., Shay, D. K., Neuzil, K. M., Fowlkes, A. L., & Goss, C. H. (2011). Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends. PLoS ONE, 6(4), e18687. http://doi.org/10.1371/journal.pone.0018687
Brownstein, J. S., Freifeld, C. C., Chan, E. H., Keller, M., Sonricker, A. L., Mekaru, S. R., & Buckeridge, D. L. (2010). Information Technology and Global Surveillance of Cases of 2009 H1N1 Influenza. New England Journal of Medicine, 362(18), 1731–1735. http://doi.org/10.1056/NEJMsr1002707
Brownstein, J. S., Freifeld, C. C., & Madoff, L. C. (2009). Digital Disease Detection — Harnessing the Web for Public Health Surveillance. New England Journal of Medicine, 360(21), 2153–2157. http://doi.org/10.1056/NEJMp0900702
Wilson, K., & Brownstein, J. S. (2009). Early detection of disease outbreaks using the Internet. Canadian Medical Association Journal, 180(8), 829–831. http://doi.org/10.1503/cmaj.090215
Keller, M., Blench, M., Tolentino, H., Freifeld, C. C., Mandl, K. D., Mawudeku, A., … Brownstein, J. S. (2009). Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance. Emerging Infectious Diseases, 15(5), 689–695. http://doi.org/10.3201/eid1505.081114
Mondor, L., Brownstein, J. S., Chan, E., Madoff, L. C., Pollack, M. P., Buckeridge, D. L., & Brewer, T. F. (2012). Timeliness of Nongovernmental versus Governmental Global Outbreak Communications. Emerging Infectious Diseases, 18(7), 1184–1187. http://doi.org/10.3201/eid1807.120249
Discuss the validity of the existing Baseline data compared with Digital research methods.
Altmetrics and the tourism industry
This is the business sector that has been most impacted by online user-created metrics - TripAdvisor, AirBnB, etc - and where conventional measures of quality - newspaper reviews, RAC, Michelin Star ratings etc - are no very much on the back foot. There are lots of newspaper articles on topics such as 'how to spot a false review. How have these metrics become accepted or opposed by the industry and consumers; how have they shaped business practices and consumer practices? How are intermediaries like TripAdvisor working to develop their legitimacy and richness of service?
Orlikowski, W.J. and Scott S.V., 2014. What Happens When Evaluation Goes Online? Exploring Apparatuses of Valuation in the Travel Sector. Organization Science, May/June, 25 (3), 868-891.
A presentation on online rating prepared for VisitScotland
The Impact of Social Media on Lodging Performance by Chris Anderson Ph.D.
The Influence of Trip advisor reviews on Hotel performance Pasi Tuominen
Altmetric Business
Blog from Echelon Insights on the "new science of measuring politics, sports, and entertainment."
Political polling and prediction [edit]
A variety of academics and practitioners have attempted to use online activity to predict voting:
McDonald and Mao (2014) An alternative Way of predicting the outcome of the Scottish Referendum (Working Paper, Adam Smith Business School, Uni of Glasgow)
(Guardian report :Google search data 'could predict election results http://www.theguardian.com/technology/2015/feb/11/google-search-data-could-be-used-to-predict-election-results )
- Google Trends - based on search behaviour as an indicator of voting behaviour. Compare with the failure of twitter analysis (and Facebook was correct!)
Work quality ratings in online work exchanges [edit]