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Detection of Social Events in Streams of Social Multimedia

Detection of Social Events in Streams of Social Multimedia
Detection of Social Events in Streams of Social Multimedia
Combining items from social media streams, such as Flickr photos and Twitter tweets, into mean- ingful groups can help users contextualise and consume more effectively the torrents of information continu- ously being made available on the social web. This task is made challenging due to the scale of the streams and the inherently multimodal nature of the information being contextualised.

The problem of grouping social media items into meaningful groups can be seen as an ill-posed and application specific unsupervised clustering problem. A fundamental question in multimodal contexts is determining which features best signify that two items should belong to the same grouping.

This paper presents a methodology which approaches social event detection as a streaming multi-modal clus- tering task. The methodology takes advantage of the temporal nature of social events and as a side benefit, allows for scaling to real-world datasets. Specific challenges of the social event detection task are addressed: the engineering and selection of the features used to compare items to one another; a feature fusion strategy that incorporates relative importance of features; the construction of a single sparse affinity matrix; and clustering techniques which produce meaningful item groups whilst scaling to cluster very large numbers of items.

The state-of-the-art approach presented here is evaluated using the ReSEED dataset with standardised evaluation measures. With automatically learned feature weights, we achieve an F1 score of 0.94, showing that a good compromise between precision and recall of clusters can be achieved. In a comparison with other state- of-the-art algorithms our approach is shown to give the best results.
2192-6611
289-302
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Samangooei, Sina
c380fb26-55d4-4b34-94e7-c92bbb26a40d
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Samangooei, Sina
c380fb26-55d4-4b34-94e7-c92bbb26a40d
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac

Hare, Jonathon, Samangooei, Sina, Niranjan, Mahesan and Gibbins, Nicholas (2015) Detection of Social Events in Streams of Social Multimedia International Journal of Multimedia Information Retrieval, 4, (4), pp. 289-302.

Record type: Article

Abstract

Combining items from social media streams, such as Flickr photos and Twitter tweets, into mean- ingful groups can help users contextualise and consume more effectively the torrents of information continu- ously being made available on the social web. This task is made challenging due to the scale of the streams and the inherently multimodal nature of the information being contextualised.

The problem of grouping social media items into meaningful groups can be seen as an ill-posed and application specific unsupervised clustering problem. A fundamental question in multimodal contexts is determining which features best signify that two items should belong to the same grouping.

This paper presents a methodology which approaches social event detection as a streaming multi-modal clus- tering task. The methodology takes advantage of the temporal nature of social events and as a side benefit, allows for scaling to real-world datasets. Specific challenges of the social event detection task are addressed: the engineering and selection of the features used to compare items to one another; a feature fusion strategy that incorporates relative importance of features; the construction of a single sparse affinity matrix; and clustering techniques which produce meaningful item groups whilst scaling to cluster very large numbers of items.

The state-of-the-art approach presented here is evaluated using the ReSEED dataset with standardised evaluation measures. With automatically learned feature weights, we achieve an F1 score of 0.94, showing that a good compromise between precision and recall of clusters can be achieved. In a comparison with other state- of-the-art algorithms our approach is shown to give the best results.

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More information

Accepted/In Press date: 10 August 2015
e-pub ahead of print date: 26 August 2015
Organisations: Web & Internet Science, Vision, Learning and Control

Identifiers

Local EPrints ID: 380227
URI: http://eprints.soton.ac.uk/id/eprint/380227
ISSN: 2192-6611
PURE UUID: 545a0d0f-fc83-4a87-aa40-58702fa3f2e1
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956

Catalogue record

Date deposited: 01 Sep 2015 09:37
Last modified: 14 Oct 2017 12:24

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Contributors

Author: Jonathon Hare ORCID iD
Author: Sina Samangooei
Author: Nicholas Gibbins ORCID iD

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