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Text Mining of Personal Communication Understanding the Technical and Privacy Related Challenges

By Håkan Jonsson, Pierre Nugues, Christofer Bach and Johan Gunnarsson

Abstract

Abstract — This paper reports on the work on a new service using text mining on SMS data: SMSTrends. The service extracts trends in the form of keywords from SMS messages sent and received by ad hoc location-based communities of users. Trends are then presented to the user using a phone widget, which is regularly updated to show the latest trends. This allows the user to see what the user community is texting about, and makes her aware of what is going on in this community. Privacy considerations of the service are governed by user expectations and regulations. Brenner and Wang [1] discussed mining of personal communication in operator bit pipes. We expand on this by looking deeper into privacy and regulatory aspects through the specific example of SMSTrends. Especially, the use of adaptive location granularity selection is introduced. possible to mark messages as secret, to make sure they were not used by the service. After this feature was introduced, a small group continued to use the service. However, the user group is yet too small to make any conclusions regarding the end user value of the service compared to the cost of the user information, and further studies with larger groups are needed. B. The Service The service extracts trends in the form of keywords from SMS messages sent and received by the users of the application. Trends are then presented to a user using a phone widget, which is regularly updated to show the latest trends. This allows her to see what a user community is texting about, and makes her aware of what is going on in this community. Keywords-text mining; messaging; location; context awareness; collective awareness; privacy I

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.190.6063
Provided by: CiteSeerX
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