You are where you tweet: a content-based approach to geo-locating twitter users

By Zhiyuan Cheng, James Caverlee, Kyumin Lee. 2010

1,252 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

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Citations in the corpus

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose (2013, background): “Geolocation is an important part of a tweet, and the study of the location of content and users is currently an active area of research (Cheng, Caverlee, and Lee 2010; Wakamiya, Lee, and Sumiya 2011).”
That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets (2015, methodology): “To do this, we exploit a corpus of 9 million tweets (Cheng et al., 2010), and extract the tweets that describe these behaviors using the #petpeeve hashtags.”
Collaborative Filtering beyond the User-Item Matrix (2014, background): “Similarly, the geotags of a user’s tweets may be used to trace the location of the user [Cheng et al. 2010].”
#Earthquake: Twitter as a Distributed Sensor System (2013, background): “Furthermore, recent work by Cheng et al. (2010) showed that even without any IP or geolocation information provided, users' location could often be estimated at the city level based purely on the content of their messages.”
User Classification with Multiple Textual Perspectives (2016, background): “User attribute classification, also namely user classification for short, is a task which aims to leverage user-generated content to automatically predict user’s attributes, such as gender (Wang et al., 2015), age (Rao et al., 2010; Sap et al., 2014) and location (Cheng et al., 2010).”
Geolocation Prediction in Social Media Data by Finding Location Indicative Words (2012, methodology): “As is well established in previous work (Cheng et al., 2010; Wing and Baldridge, 2011; Kinsella et al., 2011), it is reasonable to assume that user posts in social media reflect their geospatial locum, because lexical priors differ from region to region.”
Large Scale Sentiment Analysis on Twitter with Spark (2016, methodology): “Finally, in order to produce no-sentiment datasets we used Sentiment140 API(5) [9] and the dataset used in [4], which is publicly available(6).”
Fine-grained location extraction from tweets with temporal awareness (2014, background): “Recently, geo-locating tweets and inferring users’ locations have become a hot research topic for location-based services, advertisement, personalization and others [4, 7, 9, 15, 19].”
Supervised Text-based Geolocation Using Language Models on an Adaptive Grid (2012, methodology): “UTGEO2011 experiments are substantially lower than those reported by Cheng et al. (2010), who report a best accuracy within 100 miles of 51%.”
DeepCity: A Feature Learning Framework for Mining Location Check-Ins (2016, background): “In the literature, various choices have been made, such as 10 in [3], 2 in [9], 1,000 in [2], and 40 in [25].”
A Machine Learning Approach to Twitter User Classification (2011, background): “Other previously explored attributes include the user’s location (Jones et al. 2007; Fink et al. 2009; Cheng, Caverlee, and Lee 2010), location of origin (Rao et al. 2010), age (Jones et al. 2007; Rao et al. 2010), political orientation (Thomas, Pang, and Lee 2006; Rao et al. 2010).”
Text-Based Twitter User Geolocation Prediction (2014, background): “Alternatively, some tweets sent from mobile devices are geotagged with accurate GPS coordinates, however, the proportion of geotagged tweets is estimated to be a mere 1% (Cheng et al., 2010), and the location of the vast majority of users are not geotagged.”
Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract (2015, background): “While the majority of the existing studies focus on estimating the users’ locations [Cheng et al. , 2010; Li et al. , 2011; Mahmud et al. , 2012], we are interested in deriving a coherent locational focus that is referred to in a tweet if it exists.”
Beyond Binary Labels: Political Ideology Prediction of Twitter Users (2017, background): “…et al., 2010; Flekova et al., 2016b), gender (Burger et al., 2011; Sap et al., 2014), personality (Schwartz et al., 2013; Preoţiuc-Pietro et al., 2016), socioeconomic status (Preoţiuc-Pietro et al., 2015a,b; Liu et al., 2016c), popularity (Lampos et al., 2014) or location (Cheng et al., 2010).”
Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition (2012, background): “Some studies [4, 17] tried to overcome the problem of location information scarcity in Twitter messages, which adds another source of uncertainty.”