A Latent Variable Model for Geographic Lexical Variation

By Jacob Eisenstein, Brendan T. O'Connor, Noah A. Smith, Eric P. Xing. 2010

725 citations recorded by the source corpus.

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Citations in the corpus

"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, background): “2011), geographical location (Eisenstein et al. 2010) and age (Rao et al.”
Simplifying Graph Convolutional Networks (2019, methodology): “All experiments on the GEOTEXT dataset are conducted on a single Nvidia GTX-1080Ti GPU while the ones on the TWITTER-NA and TWITTER-WORLD datasets are excuded with 10 cores of the Intel(R) Xeon(R) Silver 4114 CPU (2.20GHz).”
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2019, background): “However, this kind of variation is not just restricted to content domains but a fundamental property of human generated language: we talk differently than our parents or people from a different part of our country, etc. Pennebaker and Stone (2003); Eisenstein et al. (2010); Kern et al. (2016).”
A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls (2014, background): “, 2010), understanding the geographic lexical variation (Eisenstein et al., 2010), and predicting food prices from menus (Chahuneau et al.”
Separating Fact from Fear: Tracking Flu Infections on Twitter (2013, background): “Twitter is a fantastic data resource for many tasks: measuring political (O’Connor et al., 2010; Tumas-jan et al., 2010), and general sentiment (Bollen et al., 2011), studying linguistic variation (Eisenstein et al., 2010) and detecting earthquakes (Sakaki et al., 2010).”
User-Level Race and Ethnicity Predictors from Twitter Text (2018, unknown): “The popularity and ubiquity of social media allows access to a wide variety of spontaneous language enabling researchers to study language variation across space and time at large scale (Eisenstein et al., 2010; Eisenstein, 2016).”
Using Hashtags to Capture Fine Emotion Categories from Tweets (2015, background): “Eisenstein et al. (2010) collected about 380,000 tweets 6 from Twitter’s oﬃcial API.”
#Emotional Tweets (2012, background): “Eisenstein et al. (2010) collected about 380,000 tweets 2 from Twitter’s ofﬁcial API.”
Geolocation Prediction in Social Media Data by Finding Location Indicative Words (2012, methodology): “In addition, topic modelling has been applied to the study of geospatially-related tasks including user dialect (Eisenstein et al., 2010), topic discovery (Yin et al., 2011), object matching (Dalvi et al., 2012), factorisation of different geospatial features (Hong et al., 2012), and…”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “…variables such as age (Rao et al., 2010; Nguyen et al., 2011), gender (Burger et al., 2011; Bamman et al., 2014; Sap et al., 2014), ge-olocation (Eisenstein et al., 2010), political preferences (Volkova et al., 2014), socio-economic status (Preot¸iuc-Pietro et al., 2015), impact (Lampos et al.,…”
Everyone's Invited: A New Paradigm for Evaluation on Non-Transferable Datasets (2015, methodology): “Twitter Datsets Given the utility of Twitter data, multiple corpora have been proposed as community resources for tasks such as sentiment analysis (Pak and Paroubek 2010), event detection (McMinn, Moshfeghi, and Jose 2013), and geolocation inference (Eisenstein et al. 2010; Mahmud, Nichols, and Drew”
What to do about bad language on the internet (2013, background): “In some cases, these associations mirror linguistic variables known from speech — such as geographically-associated lexical items like hella, or transcriptions of phonological variables like “g-dropping” (Eisenstein et al., 2010).”
Where in the World Are You? Geolocation and Language Identification in Twitter* (2013, methodology): “Similarly, there is a danger in relying on the device location as the baseline, true location of the user in training new geolocation algorithms based on text content (a practice used in, e.g., Eisenstein et al. 2010; Wing and Baldridge 2011; Mahmud 2012).”
How Twitter is Changing the Nature of Financial News Discovery (2016, methodology): “This decision can be aided by methods that contextualize tweets [35], such as displaying inferred location [16, 20, 47, 9, 7, 28, 39, 14, 13] or topic context [29, 33].”
You Are What You Tweet: Analyzing Twitter for Public Health (2011, methodology): “Eisenstein et al. (2010) studied lexical variations across geographic areas directly from tweets.”
Computational Sociolinguistics: A Survey (2016, methodology): “…to the way labels are obtained, for example by only including people who explicitly list their gender or age in their social media profile (Burger et al. 2011), who have a gender-specific first name (Bamman, Eisenstein, and Schnoebelen 2014), or who have geotagged tweets (Eisenstein et al. 2010).”
Studying User Income through Language, Behaviour and Affect in Social Media (2015, methodology): “This is solved by established regression or classification methods or more sophisticated latent variable models tailored to the task [10, 12].”
A location-based news article recommendation with explicit localized semantic analysis (2013, background): “Eisenstein et al. proposed a generative model to determine the topics of a set of geo-tagged documents and the regions corresponding to the topics simultaneously [6].”