Alsaedi, Nasser, Burnap, Peter  ORCID: https://orcid.org/0000-0003-0396-633X and Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646
2017.
Sensing real-world events using social media data and a classification-clustering framework.
Presented at: IEEE/WIC/ACM International Conference on Web Intelligence,
Omaha, Nebraska, USA,
13-16 October 2016.
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).
IEEE,
pp. 216-223.
10.1109/WI.2016.0039
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Abstract
In recent years, there has been increased interest in real-world event identification using data collected from social media, where the Web enables the general public to post real-time reactions to terrestrial events - thereby acting as social sensors of terrestrial activity. Automatically extracting and categorizing activity from streamed data is a non-trivial task. To address this task, we present a novel event detection framework which comprises five main components: data collection, pre-processing, classification, online clustering and summarization. The integration between classification and clustering allows events to be detected - including “disruptive” events - incidents that threaten social safety and security, or could disrupt the social order. We evaluate our framework on a large-scale, real-world dataset from Twitter. We also compare our results to other leading approaches using Flickr MediaEval Event Detection Benchmark
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Publisher: | IEEE |
| ISBN: | 978-1-5090-4470-2 |
| Last Modified: | 10 Sep 2025 21:56 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/97625 |
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