Effects of Age and Gender on Blogging

By Jonathan Schler, Moshe Koppel, Shlomo Engelson Argamon, James W. Pennebaker. AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, 2006

853 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8ad544669304c8b3ceb49ad9ff56bb84427662a0

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Citations in the corpus

Deep Learning for Text Style Transfer: A Survey (2020, background): “Many concerns have been raised about the discriminative task of author profiling, which can mine the demographic identities of the author of a writing, even including privacy-invading properties such as gender and age (Schler et al. 2006).”
Demographic prediction based on user's browsing behavior (2007, methodology): “After analysis of a corpus of tens of thousands of blogs, Koppel [11] found that there are significant differences in both writing style and content between male and female bloggers as well as among authors of different ages.”
How to Use less Features and Reach Better Performance in Author Gender Identification (2014, methodology): “This dataset was compiled and described by Schler et al. (2006). by Argamon et al. (2009).”
Computational Sociolinguistics: A Survey (2016, methodology): “Females tend to use more pronouns [6, 7, 8, 17, 194, 231, 233] (in particular first person singular [186, 194, 233]).”
Developing Age and Gender Predictive Lexica over Social Media (2014, methodology): “Seeking out-of-domain data, we downloaded age and gender annotated blogs from 2004 (Schler et al., 2006) (also used in Goswami et al., 2009) and gender labeled tweets (Volkova et al., 2013).”
Darling or Babygirl ? Investigating Stylistic Bias in Sentiment Analysis (2018, background): “In sociolinguistics, the lexical, phonological, syntactic patterns have been shown to vary across social class, ethnicity, gender, and age (Lawton, 1963; Jørgensen et al., 2016; Schler et al., 2006; Johannsen et al., 2015).”
Language-independent Gender Prediction on Twitter (2017, background): “Gender prediction is a well-established task in author proﬁling, useful for a series of downstream analyses (Schler et al., 2006; Schwartz et al., 2013; Bamman et al., 2014) as well as predictive model improvements (Hovy, 2015).”
S3: Social-network Simulation System with Large Language Model-Empowered Agents (2023, unknown): “To predict age using users’ posts, we use Blog Authorship Corpus Dataset [33] dataset to establish the expression-to-age relationship.”
Generation Y's dining information seeking and sharing behavior on social networking sites: An exploratory study (2014, background): “Previous studies reveal that gender differences exist in the use of SNS (Lu and Hsiao, 2009; Schler et al., 2005; Trammel and Keshelashvili, 2005).”
Automatic Compilation of Travel Information from Automatically Identified Travel Blogs (2009, methodology): “In the extraction of travel information from travel blogs, we obtained 74.0% for Precision at the top 100 extracted local products, thereby confirming that travel blogs are a useful source of travel information.”
Personalized Machine Translation: Preserving Original Author Traits (2016, background): “Several studies demonstrated that literary texts and blog posts produced by male and female writers can be distinguished by means of automatic classiﬁcation, us-ing (content-independent) function words and n-grams of POS tags (Koppel et al., 2002; Schler et al., 2006; Burger et al., 2011).”
Author Age Prediction from Text using Linear Regression (2011, methodology): “Another line of work has looked at modeling age estimation as a three-class classification problem (Schler et al., 2006; Goswami et al., 2009), with age groups of 13-17, 23-27 and 33-42.”
Privacy-preserving Neural Representations of Text (2018, methodology): “Blog posts We used the blog authorship corpus presented by Schler et al. (2006), a collection of blog posts associated with the age and gender of the authors, as provided by the authors themselves.”
Social and Emotional Correlates of Capitalization on Twitter (2018, background): “Gender lines divide language use in speech (Eckert and McConnell-Ginet, 2003); in writing (Koppel et al., 2002); and on social media (Koppel et al., 2006; Bamman et al., 2014).”
Overview of the Author Profiling Task at PAN 2013 (2013, methodology): “[30] studied the effect of age and gender in the style of writing in blogs; they gathered over 71,000 blogs and obtained a set of stylistic features like non-dictionary words, parts-of-speech, function words and hyperlinks, combined with content features, such as word unigrams with the highest infor”