Leveraging social information systems : using blogs to inform technology strategy decisions

In the knowledge economy there is a need to develop new methods for processing Internet-based information to achieve growth. This is particularly applicable in complex systems where fact and perception of reality influence each other, and social information systems bridge the two. Social information systems are systems which allow participants to produce, share, integrate and comment on content. In making decisions within complex systems, there is often not one correct decision, and statistically oriented tools provide the ability to assess alternatives. This study provides a set of algorithms and associated strategies for doing this, focused on blogs as a representative social information system. The algorithms automate the searching of blogs in a specific domain, and collection of time-series statistical data over a multi-year period around the co-occurrence of various terms within the domain. The process developed in this study uses the system representation phase of the Complex Large-Scale Interconnected Open Socio-technical System (CLIOS) process to interpret the statistical data. Subject matter experts comment on which specific statistical insights shall be used to adjust the system representation and thus lead to new technology strategy decisions being made. Three case studies were conducted with in-depth blog data analysis and expert interviews - the domains were cloud computing in industry, broadband expansion in Kenya, and renewable energy study in Abu Dhabi. Each case study produced a quantitative assessment of the blog analysis, and also a set of technology strategy areas which would benefit from this research. The intellectual contribution of this research includes enhancements to the theory of social information system around statistical analysis methods and extensions to the CLIOS process, enhancements to the study of blogs and the ability to study broader information sources, an implementation pattern for studying other domains, and enhanced technology forecasting. This research set the stage for five areas of broader impact: automatically reviewing information without reading it, gaining early awareness of insights, connecting experts and amateurs in a field, understanding public perception in a field, and building research tools for broader research.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Seshasai, Satwiksai, 1980-

Advisor dc:contributor.advisor

• Stuart Madnick.

• Engineering Systems Division.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/91081

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/91081