Mining the Blogosphere: Age, gender and the varieties of self-expression

By Shlomo Engelson Argamon, Moshe Koppel, James W. Pennebaker, Jonathan Schler. 2007

233 citations recorded by the source corpus.

The growth of the blogosphere offers an unprecedented opportunity to study language and how people use it on a large scale. We present an analysis of over 140 million words of English text drawn from the blogosphere, exploring if and how age and gender affect writing style and topic. Our primary result is that a number of stylistic and content-based indicators are significantly affected by both age and gender, and that the main difference between older and younger bloggers, and between male and female bloggers, lies in the extent to which their discourse is outer- or inner-directed. In fact, the linguistic factors that increase in use with age are just those used more by males of any age, and conversely, those that decrease in use with age are those used more by females of any age.

Subfields: Artificial Intelligence, Gender Studies, Human-Computer Interaction, Social Sciences & Humanities

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Citations in the corpus

Women also Snowboard: Overcoming Bias in Captioning Models (2018, background): “concerned about whether contextual clues in complex scenes bias the production of gendered words during sentence generation. Gender classiﬁcation has also been studied in natural language processing ([41,42], [43]). Ethical Considerations. Frequently, gender classiﬁcation is seen as a binary task: d”
"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, background): “Both content features and stylistic features (such as partof-speech and the amount of slang words) have been found to be useful for predicting the age of users (Nguyen, Smith, and Rosé 2011; Argamon et al. 2007; Goswami, Sarkar, and Rustagi 2009).”
Gender as a Variable in Natural-Language Processing: Ethical Considerations (2017, background): “For example, two early studies, Yan and Yan (2006) and Argamon et al. (2007), used machine learning to classify blogs by their authors’ genders.”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, methodology): “…phenomenon, the linguistic features of personality are only indirectly reflected in speaking or writing (a hypothesis underlying much work on detection of personality and other features in text, including Oberlander & Nowson, 2006; Argamon et al., 2007; Schwartz et al., 2013; Youyou et al., 2015).”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “…found that the majority of emotion-bearing features, e.g., emoticons, repeated letters, exasperation, are used more by female than male users, which is consistent with results reported in other recent work (Garera and Yarowsky, 2009; Burger et al., 2011; Goswami et al., 2009; Argamon et al., 2007).”
Gender Attribution: Tracing Stylometric Evidence Beyond Topic and Genre (2011, background): “In-deed, recent research on web blogs reports that there is substantial gender bias in topics (e.g., Janssen and Murachver (2004), Argamon et al. (2007)) as well as in genre (e.g., Herring and Paolillo (2006)).”
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.”
Gender identity and lexical variation in social media (2012, methodology): “ution. “Involved” language is characterized by the engagement between the writer/speaker and the audience—this is why involvement is often measured by first and second person pronoun frequency (e.g., Argamon et al. 2007). Named entities describe concrete referents, and thus may be thought of as info”
Automatically Inferring Gender Associations from Language (2019, background): “Echoing Lakoff’s original claim, a popular strand of computational work focuses on differences in how women and men talk, analyzing key lexical traits (Boulis and Ostendorf, 2005; Argamon et al., 2007; Bamman et al., 2014) and predicting a person’s gender from some text they have written (Rao et al.”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, background): “components of word use to link gender and age, showing male components of language increase with age while female factors decrease [32].”
Emphasizing personal information for Author Profiling: New approaches for term selection and weighting (2018, methodology): “…(IRF); [51] , which explores second order attributes (SOA); [52] , which uses group-level attributes (GLA) by applying topic analysis methods; [53] , which presents an analysis of over 140 million words (MW) of English text drawn from the blogosphere; and [21] , where a set of style and…”
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]).”