Abstract
To achieve accurate situation assessments and information dominance the commander needs accurate and rapid insight into the socio-cognitive landscape of his communities of interest. This requires insight into the key actors, groups, and their issues and concerns, and to have early indicators of changes. Social media (which by its nature is noisy and multilingual) is increasing the amount and type of data available for early assessment of rapidly emerging and changing situations such as disasters or crises. In this paper, we present a way of extracting topics from this kind of data in a principled and scalable fashion – regardless of the mix of languages, subject matter, or provenance of data (e.g. Twitter, VKontakte). Using a non-trivial validation task, we demonstrate that the technique is highly accurate (around 92%). We then show the results of applying the technique to a sample of around 100,000 Twitter posts generally relating to the early-2014 conflict in Ukraine, and explain how these results – or comparable results of applying the technique to other datasets – would enable a busy analyst quickly to gain a top-down understanding of a large set of data and help him or her to decide where to focus more detailed attention.
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© 2015 Springer International Publishing Switzerland
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Chew, P.A. (2015). ‘Linguistics-Lite’ Topic Extraction from Multilingual Social Media Data. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_30
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DOI: https://doi.org/10.1007/978-3-319-16268-3_30
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