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Original Articles

A data-driven framework for archiving and exploring social media data

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Pages 265-277 | Received 22 Jan 2014, Accepted 03 Jul 2014, Published online: 21 Oct 2014

Figures & data

Figure 1. General architecture of an archiving and analytical system for social media data set.

Figure 1. General architecture of an archiving and analytical system for social media data set.

Figure 2. Workflow of an archiving and online analytical system for social media data set.

Figure 2. Workflow of an archiving and online analytical system for social media data set.

Figure 3. Performance of different number of threads for spatiotemporal query of different number of tweets on one and two DB servers.

Figure 3. Performance of different number of threads for spatiotemporal query of different number of tweets on one and two DB servers.

Figure 4. Performance improvement compared to the serial computing (one thread) with one DB server.

Figure 4. Performance improvement compared to the serial computing (one thread) with one DB server.

Figure 5. Response performance comparisons by one and two DB servers with different concurrent request numbers.

Figure 5. Response performance comparisons by one and two DB servers with different concurrent request numbers.

Figure 6. Performance of different number of threads for sentimental analysis of different number of tweets on one and two DB servers.

Figure 6. Performance of different number of threads for sentimental analysis of different number of tweets on one and two DB servers.

Figure 7. Configuring and submitting a spatiotemporal query, and visualizing tweets commenting about Hurricane Sandy in 2012.

Figure 7. Configuring and submitting a spatiotemporal query, and visualizing tweets commenting about Hurricane Sandy in 2012.

Figure 8. Sentiment analysis results: (a) negative tweets, (b) positive tweets, (c) negative and positive tweets overlay.

Figure 8. Sentiment analysis results: (a) negative tweets, (b) positive tweets, (c) negative and positive tweets overlay.

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