Using social media to identify events

Li, Xueliang; Troncy, Raphaël; Huet, Benoît

doi:10.1145/2072609.2072613

Cited by 41 publications

(22 citation statements)

References 13 publications

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“…The approach by Liu et al 79) was different from the other works. They selected the venues where the scheduled events were regularly held in advance, and monitored the statistics of the number of photos shared to detect events.…”

Section: )mentioning

confidence: 89%

[Invited Paper] A Review of Web Image Mining

Yanai

2015

MTA

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In this paper, we review works related to big visual data on the Web in the literature of computer vision and multimedia research regarding the following points: (1) Web image acquisition for construction of visual concept database for image/video recognition, (2) Web image application for visual concept analysis and data-driven computer graphics, and (3) real-world sensing through Web images to detect location-dependent and event-related visual information.

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Section: )mentioning

confidence: 89%

[Invited Paper] A Review of Web Image Mining

Yanai

2015

MTA

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“…Two classifier models are built based on text and image features that later decide the class of the geo-tagged tweet. [20] is another event detection work based on geo-tagged data from Flickr network. They focus on nine events using an online event directory to define a bounding box around venue using GPS data from Flickr images.…”

Section: B Event Localization: Using Geo-tagged Data From Social Netmentioning

confidence: 99%

On localizing urban events with Instagram

Giridhar

Wang

Abdelzaher

et al. 2017

IEEE INFOCOM 2017 - IEEE Conference on Computer Communications

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Abstract-This paper develops an algorithm that exploits picture-oriented social networks to localize urban events. We choose picture-oriented networks because taking a picture requires physical proximity, thereby revealing the location of the photographed event. Furthermore, most modern cell phones are equipped with GPS, making picture location, and time metadata commonly available. We consider Instagram as the social network of choice and limit ourselves to urban events (noting that the majority of the world population lives in cities). The paper introduces a new adaptive localization algorithm that does not require the user to specify manually tunable parameters. We evaluate the performance of our algorithm for various real-world datasets, comparing it against a few baseline methods. The results show that our method achieves the best recall, the fewest false positives, and the lowest average error in localizing urban events.

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“…Weng and Lee [14] develop a system called EDCoW that builds signals for individual words, then using wavelet analysis and modularitybased graph partitioning to clustering signals together. Liu et al [8] look at identifying gigs by considering the frequency of images posted to Flickr, within a bounding box around known gig venues.…”

Section: State Of the Artmentioning

confidence: 99%

Identifying Prominent Life Events on Twitter

Dickinson

Fernández

Thomas

et al. 2015

Proceedings of the 8th International Conference on Knowledge Capture

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Social media is a common place for people to post and share digital reflections of their life events, including major events such as getting married, having children, graduating, etc. Although the creation of such posts is straightforward, the identification of events on online media remains a challenge. Much research in recent years focused on extracting major events from Twitter, such as earthquakes, storms, and floods. This paper however, targets the automatic detection of personal life events, focusing on five events that psychologists found to be the most prominent in people lives. We define a variety of features (user, content, semantic and interaction) to capture the characteristics of those life events and present the results of several classification methods to automatically identify these events in Twitter. Our proposed classification methods obtain results between 0.84 and 0.92 F1-measure for the different types of life events. A novel contribution of this work also lies in a new corpus of tweets, which has been annotated by using crowdsourcing and that constitutes, to the best of our knowledge, the first publicly available dataset for the automatic identification of personal life events from Twitter.

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Using social media to identify events

Cited by 41 publications

References 13 publications

[Invited Paper] A Review of Web Image Mining

[Invited Paper] A Review of Web Image Mining

On localizing urban events with Instagram

Identifying Prominent Life Events on Twitter

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