On the Correlation Between Textual Content and Geospatial Locations in Microblogs

Grabovitch-Zuyev, Irena; Kanza, Yaron; Kravi, Elad; Pat, Barak

doi:10.1145/2619112.2619115

Cited by 16 publications

(5 citation statements)

References 33 publications

(4 reference statements)

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“…Previous research [41,42] has shown the correlation between the content of the tweets and their geographical location. This is because high similar tweets are related to the same topic/event, and therefore they are likely to be posted in the same location.…”

Section: Similarity Score and Tweet Geolocationmentioning

confidence: 99%

On fine-grained geolocalisation of tweets and real-time traffic incident detection

Paule

Sun

Moshfeghi

2019

Information Processing & Management

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Recently, geolocalisation of tweets has become important for a wide range of real-time applications, including real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geotagged tweets available to enable such tasks remains insufficient. To overcome this limitation, predicting the location of non-geotagged tweets, while challenging, can increase the sample of geotagged data and has consequences for a wide range of applications. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets, where each vote is weighted based on evidence gathered from the ranking. Using geotagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) significantly outperforms state-ofthe-art baselines in terms of accuracy and error distance, in both cities, with the cost of decreased coverage. Finally, we investigated the applicability of our method in a real-time scenario by means of a traffic incident detection task. Our analysis shows that our fine-grained geolocalisation method can overcome the limitations of geotagged tweets and precisely map incident-related tweets at the real location of the incident.

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Section: Similarity Score and Tweet Geolocationmentioning

confidence: 99%

On fine-grained geolocalisation of tweets and real-time traffic incident detection

Paule

Sun

Moshfeghi

2019

Information Processing & Management

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“…In fact, the data may expose trends that would be non-obvious to a person. For example, the n-gram Nintendo turns out to be location-sensitive in some New York datasets [8], due to the Nintendo store at Rockefeller Plaza.…”

Section: Metricsmentioning

confidence: 99%

“…Indeed, localizing social media items is not an easy task for several reasons [8]. First, it is a priori unknown which areas will be associated with textual terms and which will not.…”

Section: Introductionmentioning

confidence: 99%

On the Accuracy of Hyper-local Geotagging of Social Media Content

Flatow

Нааман

Xie

et al. 2015

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

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Social media users share billions of items per year, only a small fraction of which is geotagged. We present a datadriven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of n-grams that appear in the text. We explore the trade-off between accuracy and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is preferred to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to assigning location data to short social media texts, and offer implications for all applications that use data-driven approaches to locate content.

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“…Note that they find popular terms for given locations while we search for locations for given terms. For a study of the correlation between locations and content in microblogs see [6]. The MapD 2 system and TweetMap 3 (which is built on top of MapD) provide search capabilities over geotagged tweets, however, these systems employ a "brute-force approach" and require a huge computation power while our system can run on an ordinary computer.…”

Section: Related Workmentioning

confidence: 99%

Geosocial Search

Pat

Kanza

Нааман

2015

Proceedings of the 24th International Conference on World Wide Web

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Geographic search-where the user provides keywords and receives relevant locations depicted on a map-is a popular web application. Typically, such search is based on static geographic data. However, the abundant geotagged posts in microblogs such as Twitter and in social networks like Instagram, provide contemporary information that can be used to support geosocial search-geographic search based on user activities in social media. Such search can point out where people talk (or tweet) about different topics. For example, the search results may show where people refer to "jogging", to indicate popular jogging places. The difficulty in implementing such search is that there is no natural partition of the space into "documents" as in ordinary web search. Thus, it is not always clear how to present results and how to rank and filter results effectively. In this paper, we demonstrate a two-step process of first, quickly finding the relevant areas by using an arbitrary indexed partition of the space, and secondly, applying clustering on discovered areas, to present more accurate results. We introduce a system that utilizes geotagged posts in geographic search and illustrate how different ranking methods can be used, based on the proposed two-step search process. The system demonstrates the effectiveness and usefulness of the approach.

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On the Correlation Between Textual Content and Geospatial Locations in Microblogs

Cited by 16 publications

References 33 publications

On fine-grained geolocalisation of tweets and real-time traffic incident detection

On fine-grained geolocalisation of tweets and real-time traffic incident detection

On the Accuracy of Hyper-local Geotagging of Social Media Content

Geosocial Search

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