Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations

By Sara Rosenthal, K. McKeown. 2011

194 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “For example, how are social groups delineated when defining demographic attribute prediction tasks (e.g., Koppel et al., 2002; Rosenthal and McKeown, 2011; Nguyen et al., 2013)?”
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.”
The Social Impact of Natural Language Processing (2016, methodology): “This information can be used to predict author characteristics from text (Rosenthal and McKeown, 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; Preoţiuc-Pietro ”
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 (Argamon et al., 2003; Schler et al., 2006; Bam-man et al., 2014), and native language (Koppel et al., 2005; Tsur and…”
Demographic Factors Improve Classification Performance (2015, methodology): “Rosenthal and McKeown (2011) have shown that these attributes are correlated.”
Stylistic Variation in Social Media Part-of-Speech Tagging (2018, background): “Second, social networks are strongly correlated with “demographic” author-level variables such as age (Rosenthal and McKeown, 2011), gender (Eckert and McConnellGinet, 2003), race (Green, 2002), and geography (Trudgill, 1974), thanks to the phenomenon of homophily, also known as assortative mixing (”
Confounds and Consequences in Geotagged Twitter Data (2015, background): “Binning is often employed in work on text-based age prediction (Garera and Yarowsky, 2009; Rao et al., 2010; Rosenthal and McKeown, 2011); it enables word and name counts to be shared over multiple ages, and avoids the complexity inherent in regressing a high-dimensional textual predictors against a”
What to do about bad language on the internet (2013, background): “A series of papers has documented the interactions between social media text and social variables such as age (Burger and Henderson, 2006; Argamon et al., 2007; Rosenthal and McKeown, 2011), gender (Burger et al.”
Privacy-preserving Neural Representations of Text (2018, background): “For example, demographic information about the author of a text can be predicted with above chance accuracy from linguistic cues in the text itself (Rosenthal and McKeown, 2011; Preoţiuc-Pietro et al., 2015).”
Semi-supervised Gender Classification with Joint Textual and Social Modeling (2016, methodology): “Furthermore, we will apply our TSFG approach to some other NLP tasks where both textual and social features are available, such as user age prediction (Rosenthal and McKeown, 2011) and user occupation classification (Preotiuc-Pietro et al. , 2015) .”
Predicting Authorship and Author Traits from Keystroke Dynamics (2018, background): “…so far include gender, age, personality or income, to name but a few (Mairesse and Walker, 2006; Luyckx and Daelemans, 2008; Rao et al., 2010; Rosenthal and McKeown, 2011; Nguyen et al., 2011; Volkova et al., 2013; Flekova et al., 2016b; Verhoeven et al., 2016; van Dalen et al., 2017;…”
Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment (2014, background): “, 2013), blogs (Rosenthal and McKeown, 2011; Goswami et al., 2009), chats in social networks (Peersman et al.”
Multitask Learning for Mental Health Conditions with Limited Social Media Data (2017, methodology): “ural language processing (NLP) to predict author characteristics such as age (Goswami et al., 2009; Rosenthal and McKeown, 2011; Nguyen et al., 2014), gender (Sarawgi et al.”
User Classification with Multiple Textual Perspectives (2016, background): “Other studies, such as Rosenthal and McKeown (2011) and Goswami et al. (2009) explore both the textual and social features in automatic age classification.”