Abstract
Event analysis and prospect identification in social media is challenging due to endless amount of information generated daily. While current research focuses on detecting events, there is no clear guidance on how those events should be processed such that they are meaningful to a human analyst. There are no clear ways to detect prospects from social media either. In this paper, we present DISTL, an event processing and prospect identifying platform. It accepts as input a set of storylines (a sequence of entities and their relationships) and processes them as follows: (1) uses different algorithms (LDA, SVM, information gain, rule sets) to identify themes from storylines; (2) identifies top locations and times in storylines and combines with themes to generate events that are meaningful in a specific scenario for categorizing storylines; and (3) extracts top prospects as people and organizations from data elements contained in storylines. The output comprises sets of events in different categories and storylines under them along with top prospects identified. DISTL uses in-memory distributed processing that scales to high data volumes and categorizes generated storylines in near real-time.
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Shukla, M., Fong, A., Dos Santos, R., Lu, CT. (2017). Event Categorization and Key Prospect Identification from Storylines. In: Grueau, C., Laurini, R., Rocha, J. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2016. Communications in Computer and Information Science, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-319-62618-5_5
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