Spatio-temporal dynamics of online memes

Kamath, Krishna; Caverlee, James; Lee, Kyumin; Cheng, Zhiyuan

doi:10.1145/2488388.2488447

Cited by 78 publications

(55 citation statements)

References 14 publications

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“…Similarly, (Crawford 2010) classifies the tweets into eight categories: a) user's current status; b) private conversations; c) links to web content; d) politics, sports, and current events; e) product recommendations/complaints; f) advertising; g) spam; and h) other (i.e., messages that match none of the above categories). Finally, one can focus on the content of tweets with an analysis based on the hashtags, such as (Kamath et al 2013), to identify the most relevant subjects. However, from our experience and also from a recent study (Potts et al 2011), although the use of hashtags to characterize a tweet is well known, most users tend to abuse (Woollaston 2013) or misuse (Henry 2012) hashtags or even to neglect the adoption of hashtags, leading to an even more complicated and most likely less reliable analysis.…”

Section: Methodology To Analyse Microblogging Content Based On the Exmentioning

confidence: 99%

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Bertone

Burghardt

2017

J geovis spat anal

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User-generated content is a valuable source of information whose production increases year after year. Twitter data is a form of user-generated content that is frequently adopted to comment on life activities in several contexts. Thus, scientific interest in that data has increased in recent years. This paper focusses on visual analytics approaches addressing the microblogging content exchanged through Twitter. In particular, we concentrate our interest on approaches that consider spatial and temporal aspects and provide visual support. Articles from the major conferences, journals, and digital libraries have been collected, organized and compared based on different criteria such as research questions, application focus, analytical and visual methods adopted, and interaction provided. In addition to these comparisons, opportunities and challenges are illustrated to inspire future research.

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Section: Methodology To Analyse Microblogging Content Based On the Exmentioning

confidence: 99%

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Bertone

Burghardt

2017

J geovis spat anal

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“…Twitter is a mixture of a social network and news media [24], where users follow each other [19] and use hashtags [20] to organize their messages. The complexity of Twitter data gives rise to a number of prediction tasks with a wide variety of possible techniques and solutions.…”

Section: Related Workmentioning

confidence: 99%

“…As additional relevant results addressing hashtag use, Spatial statistics of hashtag adoption are analyzed by Kamath et al [20]. Cheng et al [11] give methods to geolocalize tweets based on content.…”

Section: Related Workmentioning

confidence: 99%

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Location-aware online learning for top-k recommendation

Pálovics

Szalai

Pap

et al. 2017

Pervasive and Mobile Computing

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We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain.For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods.Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message.

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“…Structural properties of the users' social networks have been found to have a strong influence on subsequent tie formation [5]. Geographical properties of the social network have also been linked to tie formation [21]. For example, Kleinberg [23,24] modelled the probability of two individuals being friends based on the intuition that friendship probability increases with geographic proximity.…”

Section: Related Workmentioning

confidence: 99%

Modelling growth of urban crowd-sourced information

Quattrone

Mashhadi²,

Quercia

et al. 2014

Proceedings of the 7th ACM International Conference on Web Search and Data Mining

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Urban crowd-sourcing has become a popular paradigm to harvest spatial information about our evolving cities directly from citizens. OpenStreetMap is a successful example of such paradigm, with an accuracy of its user-generated content comparable to that of curated databases (e.g., Ordnance Survey). Coverage is however low and most importantly non-uniformly distributed across the city. Being able to model the spontaneous growth of digital information in these domains is required, so to be able to plan interventions aimed at gathering content about areas that would otherwise be neglected. Inspired by models of physical urban growth developed by urban planners, we build a model of digital growth of crowd-sourced spatial information that is both easy to interpret and dynamic, so to be able to determine what factors impact growth and how these change over time. We build and test the model against five years of OpenStreetMap data for the city of London, UK. We then run the model against two other cities, chosen for their different physical and digital growth's characteristics, so to stress-test the model. We conclude with a discussion of the implications of this work on both developers and users of urban crowd-sourcing applications.

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Spatio-temporal dynamics of online memes

Cited by 78 publications

References 14 publications

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Location-aware online learning for top-k recommendation

Modelling growth of urban crowd-sourced information

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