Bridge over Unstructured Text: A Big Data Approach to Public Diplomacy
Data available in social media, such as Facebook, and Twitter provide a wealth of unstructured data about many topics and interests, with one of the most widely followed being that of public, political figures. This data is important for a number of applications. For example, diplomats desire a good understanding of the people with whom they must negotiate. Collating and summarizing relevant, unstructured information is time-intensive, making it useful to develop methods to semi-automatically mine such information. Institution theory, specifically the concept of isomorphism, predicts that public figures adopt specific ritualistic styles of communication, which cross international boundaries. Therefore, it should be possible to develop rules and heuristics that can be applied consistently across nations to semi-automatically text mine social media postings of public figures to extract useful information. This research develops and applies heuristics for text mining vast amounts of data on public figures from social media. To demonstrate the feasibility of doing so, the Facebook pages of five Foreign Ministers (or equivalents) are mined to make inferences about where they are, and with whom they interact on specific days. An assessment is made of how generalizable the results are and how they contribute to our understanding of domain-specific text mining on large amounts of data.
Chua, C. E., Kaul, M., & Storey, V. C. (2013). Bridge over Unstructured Text: A Big Data Approach to Public Diplomacy. Proceedings of the 23rd Workshop on Information Technology and Systems (2013, Milan, Italy) Social Science Research Network.
23rd Workshop on Information Technology and Systems: Leveraging Big Data Analytics for Societal Benefits, WITS 2013 (2013: Dec. 14-15, Milan, Italy)
Business and Information Technology
Keywords and Phrases
Domain; Information; Institutional theory; Intelligence; Isomorphism; Negotiations; Organizational theory; Text mining; Unstructured big data
Article - Conference proceedings
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01 Dec 2013