Kuehn, A. (2013). Cookies versus clams: Clashing tracking technologies and online privacy. Retrieved on 22 September from https://doi-org.proxy.uba.uva.nl:2443/10.1108/info-04-2013-0013
This article examines developments in online advertising and how technology monitors private individual subscribers’ online activities. And, how internet providers (ISPs) use deep packet inspection (DPI) whereas operators of big (social media) platforms and ad networks use cookies and web beacons to collect behavioral information. Kuehn investigates the ‘battle of technologies’ between the different ways of online tracking and how it affects Internet governance. He analyzes privacy issues surrounding these technologies and concludes that privacy violations cannot explain policy outcomes.
The article is interesting as regards to privacy issues on networks, but these technologies have immensely developed during the recent years. Thus, the accuracy of named techniques may not be sufficient. Furthermore, the article relies on several sources, but some of them are blog posts and do not seem entirely reliable. Besides, the information given may not be useful for my topic.
Chang, Y., Li, H., Shokouhi, M., Li, R., Wang, H., (2017, August). Search, Mining, and Their Applications on Mobile Devices: Introduction to the Special Issue.
Retrieved on 21th September 2017 from http://delivery.acm.org.proxy.uba.uva.nl:2048/10.1145/3090000/3086665/a29-wang.pdf?ip=146.50.98.28&id=3086665&acc=ACTIVE%20SERVICE&key=0C390721DC3021FF%2E86041C471C98F6DA%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=988909436&CFTOKEN=28049169&__acm__=1506320531_c168ca51aefee785d5de02c6a782db57
In this article the authors examine research problems in search, mining and their applications on mobile devices. It analyzes mobile usage data and the evolution of mobile search research. Search engines have evolved to proactive systems, which precisely show relevant information to users based on the context and user search history. The data of mobile device users records amounts of behavioral data and because this data has become widely accessible, these introduce new data mining problems. By monitoring and identifying repetitive tasks from search logs, the … can predict if and when people will perform those tasks in the future. The information given by these patterns, can be used by agencies to observe certain searches and keep an eye on … The goal of the article was to collect results of different works and studies on mobile data development and to review the processes.
The information which the article is based on is reliable and consists of several (peer-reviewed) studies. It includes a long list of useful references. But, it is only a brief overview of different studies and does not go in depth.
Agarwal, S. (2015, November 21). Applying social media intelligence for predicting and identifying on-line radicalization and civil unrest oriented threats. Retrieved September 21, 2017, from https://arxiv.org/pdf/1511.06858.pdf
van der Beken, T., Verfaillie, K., (2008) Proactive policing and the assessment of organised crime, Policing: An International Journal of Police Strategies & Management, Vol. 31 Issue: 4, pp.534-552. Retrieved on 26 September, 2017 from https://doi.org/10.1108/13639510810910553
Bogomolov, A., Lepri, B., Oliver, N., Pentland, A., Pianesi, F., & Staiano, J. (2014). Once upon a crime: Towards crime prediction from demographics and mobile data. ACM International Conference on Multimodal Interaction. Retrieved 21 September 2017, from https://arxiv.org/abs/1409.2983
Brinson, N. H., Doorey, A., Eastin, M. S., & Wilcox, G. (2016, May). Living in a Big Data world: Predicting mobile commerce activity through privacy concerns. Retrieved 24 September, 2017, from http://www.sciencedirect.com.proxy.uba.uva.nl:2048/science/article/pii/S0747563215303216
Burnap, P., Sloan, L. & Williams, M. L., (2016, March 31). Crime sensing with Big Data: The affordances and limitations of using open-source communications to estimate crime patterns. Retrieved September 25, 2017, from https://academic.oup.com/bjc/article/57/2/320/2623946/Crime-Sensing-With-Big-Data-The-Affordances-and
Chan, J., & Moses, L. B., (2016). Algorithmic prediction in policing: assumptions, evaluation, and accountability. Policing and Society, 1-17. Retrieved September 22 from http://dx.doi.org/10.1080/10439463.2016.1253695
Chang, Y., Li, H., Li, R., Shokouhi, M., Wang, H. (2017, August) Search, mining, and their applications on mobile devices: Introduction to the special issue., ACM Trans. Inf. Syst. 35, 4, Article 29. Retrieved September 20, 2017, from http://dx.doi.org/10.1145/3086665
Chaturvedi, S. K. & Dubey, N., (2014, March). A survey paper on crime prediction technique using data mining. Int. Journal of Engineering Research and Applications, Vol. 4, Issue 3( Version 1), pp.396-400. Retrieved September 27, 2017, from http://ijera.com/papers/Vol4_issue3/Version%201/BS4301396400.pdf
Janssen, M., & Kuk, G. (2016, September 28). The challenges and limits of Big Data algorithms in technocratic governance. Government Information Quarterly 33, 371–377, Retrieved September 26, 2017, from http://www.sciencedirect.com.proxy.uba.uva.nl:2048/science/article/pii/S0740624X16301599
Kallus, N. (2014, February 10). Predicting crowd behavior with Big Public Data. Retrieved September 22, 2017, from https://arxiv.org/pdf/1402.2308.pdf
Kuehn, A. (2013). Cookies versus clams: Clashing tracking technologies and online privacy. Info, Vol. 15 Issue: 6, pp.19-31, Retrieved 22 September, 2017 from https://doi-org.proxy.uba.uva.nl:2443/10.1108/info-04-2013-0013
O’Leary, D. E. (2015, November). Big Data and privacy: Emerging issues. Retrieved September 22, 2017, from https://www-computer-org.proxy.uba.uva.nl:2443/csdl/mags/ex/2015/06/mex2015060092.pdf
Predictive analytics : the power to predict who will click, buy, lie, or die / Eric Siegel. (book, available at UvA)
Burnap, P., Sloan, L. & Williams, M. L., (2016, March 31). Crime Sensing With Big Data: The Affordances and Limitations of Using Open-source Communications to Estimate Crime Patterns. Retrieved September 25, 2017, from https://academic.oup.com/bjc/article/57/2/320/2623946/Crime-Sensing-With-Big-Data-The-Affordances-and
In this study the authors examine to what extent people are able to plan and anticipate organized crime threats using strategic planning tools. The authors argue that predicting crime and forecasts are limited in their ability to support…Prospective statements about organized crime assume that criminal behavior is related to its context. But, in social processes it is very complicated to use predictions and forecasting techniques because human behavior is not self-evident. The accuracy is not profound. But, to explain the processes and dynamics of organized crime, there are no (clear) causal relationships between agency and criminal activity. Therefore, we cannot expect certain analytical models to have the ability to predict future threats and criminal activity.
The authors are aware of the limitations of technological crime prediction, but rather than using this information to prove a statement, they reflect and analyze the other ideas. Also, the article does not go into depth enough about the strategic planning tools or ways to anticipate organized crime. It is more of an analytical than a descriptive article and questions the ways policymakers detect future crime. Nonetheless, parts of the study could easily be used to evaluate my topic and look at it from a different, more critical perspective. Especially when examining the value of detection in its relationship with privacy dilemmas. Furthermore, the study is based on a lot of sources and the authors make a lot of references to other studies. It is well-researched.
Williams, M. L., Burnap, P., & Sloan, L. (2016). Crime Sensing with Big Data: The Affordances and Limitations of Using Open-Source Communications to Estimate Crime Patterns | The British Journal of Criminology | Oxford Academic. Retrieved September 23, 2017, from http://orca.cf.ac.uk/87031/7/azw031.pdf
This study examines the relationship/connection between open-source communications data and police-recorded crime data in London boroughs.
Data obtained on social media (in particular, Twitter data) can provide insight into the behavior of specific groups. In their study, the authors assume every Twitter user is a sensor of offline phenomena who publish information as victims, first-hand witnesses, second-hand observers and perpetrators.
In addition, when using the obtained data in social research, researches should keep the ethical part of the question in mind. Although most people accept Terms of Service and thus give their permission for their information to be accessed by third parties, many are unaware of how and who can access this.
In their study, they used Twitter data as a predictor to conduct an ecological analysis of London police-recorded crime.
They conclude that the effort to predict are often overlooked. Purely-data driven mechanisms tend to lead to results that do not reflect reality. But when using Twitter data, a large scale of data is filled with noise and needs to be filtered using machines. The author brings into attention that to examine and reflect on this information, we cannot solely rely on machines. Wholly-data driven approaches cannot predict crime. He concludes that the key when dealing with big-data in criminological research, that strict checks and balancing still need to be put in place to collect reliable info.
Their findings are based on using (only) one social networking site. Despite Twitter still being a big social network, additional data from other social networking sites such as Facebook would have provided a broader field of results. The authors themselves are very critical of their findings and bring to attention that social media data has a tendency to be biased and that several sources of bias may be present in Twitter obtained data .Nonetheless, they argue that the obtained information can be useful and even though the utility may sometimes be questionable, raw social media data can still be used to study conflict and abuse or predict crime….