Planned Events Across Social Media Sites using Association Rule Mining Based on Autocorrelation
Prajakta K. Sarolkar1, Meghna Nagori2
1Prajakta K. Sarolkar, Dept. Of Computer Science & Engg. Government College of Engineering. Aurangabad (M.S.) India.
2Meghna Nagori, Dept. Of Computer Science & Engg. Government College of Engineering. Aurangabad (M.S.) India.
Manuscript received on June 11, 2013. | Revised Manuscript received on June 15, 2013. | Manuscript published on June 25, 2013. | PP: 26-28 | Volume-1 Issue-8, June 2013. | Retrieval Number: H0342061813/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g. Event Brite, Face book events) to discussions and reactions related to events shared on different social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we propose the challenge of automatically identifying user-contributed content for events that are planned and, therefore, known in advance, across different social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associated with an event on different social media sites.
Keywords: This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms