Working Paper Article Version 1 This version is not peer-reviewed

Case Study on Privacy-Aware Social Media Data Processing in Disaster Management

Version 1 : Received: 30 September 2020 / Approved: 1 October 2020 / Online: 1 October 2020 (13:58:16 CEST)

How to cite: Löchner, M.; Fathi, R.; Schmid, D.; Dunkel, A.; Burghardt, D.; Fiedrich, F.; Koch, S. Case Study on Privacy-Aware Social Media Data Processing in Disaster Management. Preprints 2020, 2020100012 Löchner, M.; Fathi, R.; Schmid, D.; Dunkel, A.; Burghardt, D.; Fiedrich, F.; Koch, S. Case Study on Privacy-Aware Social Media Data Processing in Disaster Management. Preprints 2020, 2020100012

Abstract

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. A cruicial part of the analysis is to avoid unnecessary data retention during that process, in order to prevent subsequent abuse, theft or public exposure of collected datasets and thus, protect the privacy of social media users. There are a number of technical approaches out to face the problem. One of them is using a cardinality estimation algorithm called HyperLogLog to store data in a privacy-aware structure, that can not be used for purposes other than the originally intended. In this case study, we developed and conducted a focus group discussion with teams of social media analysts, in which we identified challenges and opportunities of working with such a privacy-enhanced social media data structure in place of conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisiton process will not distract the data analysis process. Instead, it will improve working with huge datasets due to the improved characteristics of the resulting data structure.

Subject Areas

disaster management; virtual operation support teams; privacy; data retention; hyperloglog; focus group discussion

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