Proceedings of the 8th ACM Conference on Web Science 2016
DOI: 10.1145/2908131.2908197
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Wisdom of the local crowd

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Cited by 15 publications
(6 citation statements)
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“…Local events are also the focus in [Walther and Kaisser, 2013], with an approach which constructs clusters of tweets according to their density. In [Ranneries et al, 2016] posts from both Twitter and Instagram are clustered according to their hashtags, and then geotagged to associate a single place to each cluster. Likewise, [Salas et al, 2017] proposes real-time detection of traffic events using tweets and their geolocation information.…”
Section: A Events Detection In Urban Areasmentioning
confidence: 99%
“…Local events are also the focus in [Walther and Kaisser, 2013], with an approach which constructs clusters of tweets according to their density. In [Ranneries et al, 2016] posts from both Twitter and Instagram are clustered according to their hashtags, and then geotagged to associate a single place to each cluster. Likewise, [Salas et al, 2017] proposes real-time detection of traffic events using tweets and their geolocation information.…”
Section: A Events Detection In Urban Areasmentioning
confidence: 99%
“…Then, these clusters are scored according to different criteria: textual content, number of users, number of tweets, etc. In [19] posts from both Twitter and Instagram are clustered according to their hashtags. After that, the density-based clustering algorithm DBSCAN is applied twice to these clusters in order to associate a single place to each cluster.…”
Section: Related Workmentioning
confidence: 99%
“…3) Using social networks activity: This approach uses information published in social networks [9][10][11]. Geo-location data of photos, tweets or other social media contents can be used to infer the number of individuals present in target areas.…”
Section: Related Workmentioning
confidence: 99%
“…One of these kind of studies gathered info from posts in New York city in real-time and tried to aggregate them in clusters to analyze the flow of people in the city [11]. Another study combined, almost in real time, information gathered from Twitter and Instagram, using low-cost processing procedures [10], in order to analyzed it, using several filters, in the generation of several metrics for user distribution. This approach is dependent on data published by social media users.…”
Section: Related Workmentioning
confidence: 99%
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