Automatically profiling the author of an anonymous text

By Shlomo Engelson Argamon, Moshe Koppel, James W. Pennebaker, Jonathan Schler. CACM, 2009

471 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/691d4bb060a8494d79bab25c5c9af2216390a5df

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Citations in the corpus

On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification (2017, background): “State-of-the-art techniques for author proﬁling / identiﬁcation usually draw upon large quantities of features; e.g., Burger et al. (2011) use more than 15 million features and Argamon et al. (2009) and Mukherjee and Liu (2010) more than 1,000.”
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas (2019, background): “A series of works by (Koppel et al., 2011, 2009; Argamon et al., 2009; Koppel and Winter, 2014) and their shared tasks (Stamatatos et al.”
Computational Sociolinguistics: A Survey (2016, background): “Females tend to use more pronouns [6, 7, 8, 17, 194, 231, 233] (in particular first person singular [186, 194, 233]).”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, methodology): “They examined language use according to the traits of neuroticism and extraversion over approximately 2200 student essays, while focused on using function words for the prediction of gender [62].”
Extracting Human Temporal Orientation from Facebook Language (2015, background): “Others, such as the bottom three examples, might reference multiple temporal classes or not include clear verb tense and thus rely on the raters’ judgements for what is most dominant.”
Author Profiling: Predicting Age and Gender from Blogs Notebook for PAN at CLEF 2013 (2013, unknown): “Style Based Features Style based features includes N-grams of POS tags in documents, punctuation symbols and number of href links[2,9].”
Developing Age and Gender Predictive Lexica over Social Media (2014, background): “People have also used online media to infer other demographic-like attributes such as native language (Argamon et al., 2009), origin (Rao et al., 2010), and location (Jones et al., 2007).”
External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, background): “The list of papers found is here: [12,14–17,26,28,31–35,43–60].”
Computational methods in authorship attribution (2009, unknown): “The discussion in Section 6 is drawn from that in Argamon et al. (2008).”
Overview of the Author Profiling Task at PAN 2013 (2013, background): “Note that most of the existing research in computational linguistics [3] and social psychology [26] focuses on the English language, and the question is whether the observed relations pertain to other languages as well.”
Learning Multimodal Gender Profile using Neural Networks (2017, background): “Also, Argamon et al. (2009) worked with anonymous texts from blogs using two types of basic features: content-based and style-based features.”
Pitfalls in Machine Learning Research: Reexamining the Development Cycle (2020, unknown): “There are hundreds of papers that claim to be able to accurately determine people’s gender based on anything from a writing sample [6, 56, 20], to their gait [57, 16, 18], to the texture of their skin [54, 27, 1].”