Author Age Prediction from Text using Linear Regression

By Dong Nguyen, Noah A. Smith, Carolyn Penstein Rosé. 2011

205 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): “In contrast, Nguyen, Smith, and Ros´e (2011) approached age prediction as a regression problem, eliminating the need to create age categories.”
Story Cloze Task: UW NLP System (2017, methodology): “We adopt style features that have been shown useful in the past in tasks such as detection of age (Schler et al., 2006; Rosenthal and McKeown, 2011; Nguyen et al., 2011), gender 1Note that taking the logarithm of the expression in Equation 1 gives the pointwise mutual information between the story…”
Demographic Factors Improve Classification Performance (2015, methodology): “, 2014a; Eisenstein, to appear), or b) used linguistic features to infer socio-economic attributes (Rosenthal and McKeown, 2011; Nguyen et al., 2011; Alowibdi et al., 2013; Ciot et al., 2013; Liu and Ruths, 2013; Bergsma et al., 2013; Volkova et al., 2015).”
Overview of the 3rd Author Profiling Task at PAN 2015 (2015, methodology): “previously done in [47] and used an approach based on logistic regression.”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, methodology): “Experiments have been conducted with estimating 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…”
Emphasizing personal information for Author Profiling: New approaches for term selection and weighting (2018, methodology): “As previously mentioned, the AP task consists in analyzing texts to predict general or demographic attributes of authors such as their gender [19–22] , age [20–23] , personality [3,24] , native language [20] , political orientation [25] , among others.”
Overview of the Author Profiling Task at PAN 2013 (2013, methodology): “They modelled age as a continuous variable (as they had previously done in [21]), and used an approach based on logistic regression.”
Computational Sociolinguistics: A Survey (2016, methodology): “Unigrams alone already perform well [185, 186, 198].”
The Social Impact of Natural Language Processing (2016, methodology): “This information can be used to predict author characteristics from text (Rosenthal and McKe-own, 2011; Nguyen et al., 2011; Alowibdi et al., 2013; Ciot et al., 2013; Liu and Ruths, 2013; Volkova et al., 2014; Volkova et al., 2015; Plank and Hovy, 2015; Preotiuc-Pietro et al., 2015a;…”
Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment (2014, unknown): “In contrast, recent works also explored predicting age as a continuous variable and predicting lifestages (Nguyen et al., 2013; Nguyen et al., 2011) .”
The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task (2017, background): “Previous work has shown that different authors can be grouped by their writing style, according to factors such as age (Pennebaker and Stone, 2003; Argamon et al., 2003; Schler et al., 2006; Rosenthal and McKeown, 2011; Nguyen et al., 2011), gender (Argamon et al.”
Predictive Analysis on Twitter: Techniques and Applications (2018, methodology): “accuracy in predicting the age of these users. They selected Support Vector Regression (SVR) with K-Fold Cross Validation [109] as their best performing model after evaluating using Linear Regression [110], Least Absolute Shrinkage and Selection Operator (LASSO) [111], and ElasticNet [112]. Zhang et”
Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter (2015, background): “…specific aspects such as political affiliation (Cohen and Ruths 2013), gender (Ciot, Sonderegger, and Ruths 2013; Alowibdi , Buy, and Yu 2013), age (Nguyen, Smith, and Rosé 2011; Nguyen et al. 2013), location (Jurgens 2013), or combinations thereof (Zamal, Liu, and Ruths 2012; Li, Ritter, and Hovy…”
Studying User Income through Language, Behaviour and Affect in Social Media (2015, background): “Examples range from location [5], gender [6], age [7, 8], personality [9] or political preference [10, 11].”
The role of personality, age, and gender in tweeting about mental illness (2015, methodology): “Recently, automatic user profiling methods have used on usergenerated text and complementary features in order to predict different user traits such as: age (Nguyen et al., 2011), gender (Sap et al.”