A comparative study of spatial, temporal and content-based patterns emerging in YouTube and Flickr

Mirković, Milan; Ćulibrk, Dubravko; Papadopoulos, Symeon; Zigkolis, Christos; Kompatsiaris, Ioannis; McArdle, Gavin; Crnojević, Vladimir

doi:10.1109/cason.2011.6085942

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…More robust solutions would be provided by integrating and combining event information from multiple social sources, such as Facebook, Flicker, and Youtube (Chen and Roy 2009;Kennedy et al 2007;Kinsella et al 2011;Mirkovic et al 2011;Rattenbury et al 2007;Stefanidis et al 2013;Tang et al 2012). More robust solutions would be provided by integrating and combining event information from multiple social sources, such as Facebook, Flicker, and Youtube (Chen and Roy 2009;Kennedy et al 2007;Kinsella et al 2011;Mirkovic et al 2011;Rattenbury et al 2007;Stefanidis et al 2013;Tang et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…This survey focused on event detection from a single source of social media information. More robust solutions would be provided by integrating and combining event information from multiple social sources, such as Facebook, Flicker, and Youtube (Chen and Roy 2009;Kennedy et al 2007;Kinsella et al 2011;Mirkovic et al 2011;Rattenbury et al 2007;Stefanidis et al 2013;Tang et al 2012). Missing information from one source may be available in the other.…”

Section: Discussionmentioning

confidence: 99%

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A Survey of Techniques for Event Detection in Twitter

Farzindar¹,

Khreich²

2013

Computational Intelligence

597

381

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Twitter is among the fastest-growing microblogging and online social networking services. Messages posted on Twitter (tweets) have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real-world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Survey of Techniques for Event Detection in Twitter

Farzindar¹,

Khreich²

2013

Computational Intelligence

597

381

View full text Add to dashboard Cite

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“…In contrast, several studies have focused on extracting useful information from Web images shared on social media sites [13], [14], [15], [16], [17], [18], [19], [20], [21]. Rabbath et al [13] proposed a clustering method for event detection in Web images.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, several studies on detecting geographical events and landmarks [16], [17], [18], [19] have been conducted on the basis of images posted onto Flickr, which is a popular Website providing a social forum for posting and sharing Web images freely. The Web images posted onto Flickr are usually annotated with not only text information (e.g., comments and tags) but also geotags.…”

Section: Related Workmentioning

confidence: 99%

Clustering-based burst-detection algorithm for web-image document stream on social media

Tamura

Kitakami

et al. 2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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With an increasing interest in social media, a large number of Web images have been created on the Internet. Therefore, extracting useful knowledge from a large-scale set of Web images has become a new type of challenge. In this paper, we focus on Web images that have been posted onto the Internet through social media sites. The main objective of this study is to extract the events and track the topics of a document stream that includes Web images. To address this challenge, this paper proposes a novel method for burst detection in a Web-image document stream. The proposed method integrates a clustering technique with Kleinberg's burst detection. To evaluate the proposed method, we used actual tweets from Twitter users. The experimental results show that the proposed method can extract the events and track the topics related to Web images posted on social media sites.

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“…Geo-referenced data that can be obtained from photo and video publishing web-sites on the Internet has been used to by various authors to analyze certain aspects of human mobility. Most of the work has been focusing on determining attractive locations where tourists congregate, using clustering and data visualization techniques (Mirkovic et al, 2011); (Andrienko et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Deriving Basic Law of Human Mobility using Community - Contributed Multimedia Data

Gavric

Ćulibrk

Crnojević

2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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In recent years, geo-referenced community-contributed multimedia data that is available from services such as Flickr/YouTube, has been used to help understand patterns of human mobility, behavior and habits. While this data is freely available for much larger regions of the world, it is understood that the quality of such data is lower than that of data that can be obtained from mobile phone operators. This is probably the reason why public data has not been considered for studies attempting to identify basic laws that govern human mobility. In this study we explore the possibility of using Flickr data as an alternative to mobilephone-generated data when it comes to analyzing human mobility. To do this, we apply a recently published approach to analysis of mobile phone data to the trajectories of 6404 Fickr users, derived from a dataset of 1 million images pertinent to the San Francisco/San Diego area. Our goal is to show that regularities that can be observed using mobile phone data are present in the Flickr data and that the publicly available data has the potential to enable researchers to conduct similar analysis at larger (continent/world wide) scales, with possible applications to urban planning, traffic forecasting and the spread of biological and mobile-phone viruses. The results presented show that Flickr data is suitable for such studies, and can be used as an alternative to proprietary mobile-phone-use related data.

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A comparative study of spatial, temporal and content-based patterns emerging in YouTube and Flickr

Cited by 6 publications

References 13 publications

A Survey of Techniques for Event Detection in Twitter

A Survey of Techniques for Event Detection in Twitter

Clustering-based burst-detection algorithm for web-image document stream on social media

Deriving Basic Law of Human Mobility using Community - Contributed Multimedia Data

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