@Phillies Tweeting from Philly? Predicting Twitter User Locations with Spatial Word Usage

Chang, Hau-Wen; Lee, Dongwon; Eltaher, Mohammed; Lee, Jeong-Kyu

doi:10.1109/asonam.2012.29

Cited by 73 publications

(30 citation statements)

References 12 publications

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“…This unfortunately requires a large hand-annotated corpus for training. Han14 systematically investigate various feature selection methods for finding geo-indicative words, such as information gain ratio (IGR) (Quinlan, 1993), Ripley's K statistic (O'Sullivan and Unwin, 2010) and geographic density (Chang et al, 2012), showing significant improvements on TWUS and TWWORLD ( §2).…”

Section: Feature Selectionmentioning

confidence: 99%

Hierarchical Discriminative Classification for Text-Based Geolocation

Wing

Baldridge

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Text-based document geolocation is commonly rooted in language-based information retrieval techniques over geodesic grids. These methods ignore the natural hierarchy of cells in such grids and fall afoul of independence assumptions. We demonstrate the effectiveness of using logistic regression models on a hierarchy of nodes in the grid, which improves upon the state of the art accuracy by several percent and reduces mean error distances by hundreds of kilometers on data from Twitter, Wikipedia, and Flickr. We also show that logistic regression performs feature selection effectively, assigning high weights to geocentric terms.

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Section: Feature Selectionmentioning

confidence: 99%

Hierarchical Discriminative Classification for Text-Based Geolocation

Wing

Baldridge

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Feature Selection. Dimensionality reduction methods have been used to improve geolocation inference performance while reducing computational cost (Cheng et al, 2010;Chang et al, 2012;Han et al, 2012). Of existing approaches, the…”

Section: Resultsmentioning

confidence: 99%

Geocoding Without Geotags: A Text-based Approach for reddit

Harrigian¹

2018

Proceedings of the 2018 EMNLP Workshop W-Nut: The 4th Workshop on Noisy User-Generated Text

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In this paper, we introduce the first geolocation inference approach for reddit, a social media platform where user pseudonymity has thus far made supervised demographic inference difficult to implement and validate. In particular, we design a text-based heuristic schema to generate ground truth location labels for reddit users in the absence of explicitly geotagged data. After evaluating the accuracy of our labeling procedure, we train and test several geolocation inference models across our reddit data set and three benchmark Twitter geolocation data sets. Ultimately, we show that geolocation models trained and applied on the same domain substantially outperform models attempting to transfer training data across domains, even more so on reddit where platformspecific interest-group metadata can be used to improve inferences.

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“…Another study [54] estimated a city-level user location based purely on the content of tweets, which might include reply tweet information, without the use of any external information, such as a gazetteer or internet protocol (IP) information. Two unsupervised methods [55] have been proposed based on notions of nonlocalness and geometric localness to prune noisy data from tweets. One report [56] described language models of locations using coordinates extracted from geotagged Twitter data.…”

Section: Discussionmentioning

confidence: 99%

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

Wakamiya¹,

Kawai²,

Aramaki³

2018

JMIR Public Health Surveill

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BackgroundThe recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor–based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results.ObjectiveThis study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts.MethodsWe presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information.ResultsThe estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points).ConclusionsThe proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification.

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@Phillies Tweeting from Philly? Predicting Twitter User Locations with Spatial Word Usage

Cited by 73 publications

References 12 publications

Hierarchical Discriminative Classification for Text-Based Geolocation

Hierarchical Discriminative Classification for Text-Based Geolocation

Geocoding Without Geotags: A Text-based Approach for reddit

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

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