Mining the Blogosphere: Age, gender and the varieties of self-expression
Open accessAbstract
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.
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Argamon et al. used factor analysis and identified 20 coherent components of word use to link gender and age, showing male components of language increase with age while female factors decrease.
“Similar to our finding of 2000 topics (clusters of semantically-related words), Argamon et al. used factor analysis and identified 20 coherent components of word use to link gender and age, showing male components of language increase with age while female factors decrease [32].”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [32]
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“components of word use to link gender and age, showing male components of language increase with age while female factors decrease [32].”
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“Similar results emerged from research on other media, such as online blogs (Herring et al., 2004; Argamon et al., 2007).”
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BackgroundResultSharing of Sponsored Advertisements on Social Media: A Uses and Gratifications Perspective
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A Log-Linear Model for Unsupervised Text Normalization
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“…2010), and while the use of AI in psychology is not yet widespread, it has been used to successfully gain insights into, e.g., who we are (Argamon et al., 2007; Berger and Packard, 2021; Kwantes et al., 2016; Schwartz et al., 2013), how we feel (De Bruyne et al., 2022; Eichstaedt et al.,…”
Using Twitter Content to Predict Psychopathy
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Latent human traits in the language of social media: An open-vocabulary approach
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“style [28], but other findings also indicated that most of the differences between female and male bloggers are related to the social goals of the blog [29] and the genre of the texts published [30].”
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GENDER IN TWITTER: STYLES, STANCES, AND SOCIAL NETWORKS
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Adjectives, communities, and taxonomies of evaluative meaning
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“This approach has subsequently been applied to identify themes in a range of text types including blog posts (Argamon et al. 2007), postings on Internet message boards for depression (Ramirez-Esparza et al. 2008), emails as part of a patient after-care programme (Wolf et al. 2010), and Facebook…”
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Gender classification of microblog text based on authorial style
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“The Naı¨ve Bayes classifier is a popular classification algorithm used extensively in document classification (Yan and Yan 2006; Argamon et al. 2007; Mukherjee and Liu 2010).”
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Proceedings of the 3rd Workshop on Noisy User-generated Text
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Proceedings of the Workshop on Language Analysis in Social Media
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Twitter n-gram corpus with demographic metadata
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DemographicVis: Analyzing demographic information based on user generated content
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“Researchers from Social Sciences gained psychological insights through studies that link language use with age and gender, while researchers from Computer Science have focused on introducing and improving algorithms to predict demographic information.”
BackgroundAuthorship Attribution in Greek Tweets Using Author's Multilevel N-Gram Profiles
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Survey on profiling age and gender of text authors
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“The varied databases for such study include: theater plays [20]; essays [21]; biblical books [22]; book chapters [23], [24]); emails, chats and SMS messages [25]–[28], web forum messages [29], [30]; blogs [31], [32].”
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“(2002), and initially applied to blogs (Argamon et al., 2007; Rosenthal and McKeown, 2011; Nguyen et al., 2011).”
BackgroundCarpenter, J. M., Preoțiuc-Pietro, D., Flek, L., Giorgi, S., Hagan, C. A., Kern, M. L., Buffone, A. E. K., Ungar, L., & Seligman, M. E. P. (2016) · Social Psychological and Personality Science · doi:10.1177/1948550616671998 · Cited by 21 · see citing works
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‘Bad language’ in the Nordics: profanity and gender in a social media corpus
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“Argamon et al. (2007) compiled 140 million words of text from English-language blogs hosted at blogger.com in 2004. A factor analysis of the 1,000 most frequent words resulted in 20 factor groups, many of which showed differences in frequency of use by males and females. The factor “swearing” was slightly more strongl…”
BackgroundInfluentialAn Interactive Method for Inferring Demographic Attributes in Twitter
Beretta, V., Maccagnola, D., Cribbin, T. F., & Messina, E. (2015) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2700171.2791031 · Cited by 21 · see citing works
“Argamon et al. used factor analysis for identifying 20 coherent factors of words linking gender and age [2].”
MethodPrediction of places of visit using tweets
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A Multidimensional Lexicon for Interpersonal Stancetaking
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“…sociolinguistics (Nguyen et al., 2016), language variation has been studied primarily in connection with macro-scale social variables, such as age (Argamon et al., 2007; Nguyen et al., 2013), gender (Burger et al., 2011; Bam-man et al., 2014), race (Eisenstein et al., 2011; Blodgett et al.,…”
BackgroundGendered language differences in public communication? The case of municipal tweets
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Stereotypical gender actions can be extracted from web text
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“It is possible to collect metadata like the gender of the posters, create two sub-corpora based on the text created by the two genders respectively, and then compare the two sub-corpora to reveal statistical differences between the linguistic patterns or content (Argamon et al., 2007).”
Investigating the Role of Emotion-Based Features in Author Gender Classification of Text
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Blogging Around the Globe: Motivations, Privacy Concerns, and Social Networking
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“…al. 2007; Herring et al. 2004; Pedersen and Macafee 2007; Schler et al. 2006; Yan and Yan 2006), teenage vs. adult bloggers (Huffaker and Calvert 2005; Lenhart and Madden 2005; Lenhart et al. 2007,2010; Strandberg 2006), and differences with respect to age (Argamon et al. 2007; Schler et al. 2006).”
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“these features have been used in combination –as a complement– of content features [1, 10].”
MethodOn the Use of URLs and Hashtags in Age Prediction of Twitter Users
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BackgroundIdentifying Audience Attributes
Tareaf, R. B., Berger, P., Hennig, P., Jung, J., & Meinel, C. (2017) · International Conference on Cloud and Big Data Computing (ICCBDC) · doi:10.1145/3141128.3141129 · Cited by 15 · see citing works
“[21] used factor analysis which creates twenty coherent factors that depict clear and distinct themes, mostly topic-related.”
MethodA visual approach for age and gender identification on Twitter
Álvarez‐Carmona, M. Á., Pellegrin, L., Montes-y-Gómez, M., Sánchez-Vega, F., Escalante, H. J., López-Monroy, A. P., Villaseñor-Pineda, L., & Villatoro-Tello, E. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169497 · Cited by 15 · see citing works
“According to the literature, AP in social media has two main subtasks: age and gender detection (see [2, 3, 4, 7, 9, 13, 14, 17, 23, 27, 31, 32, 33, 36, 39, 40, 46] for gender, and [2, 3, 4, 13, 23, 28, 29, 31, 33, 36, 40] for age).”
BackgroundFlek, L., & Gurevych, I. (2013) · TUbilio (Technical University of Darmstadt) · Cited by 15 · see citing works
Lifetime Lexical Variation in Social Media
Liao, L., Jiang, J., Ding, Y., Huang, H., & Lim, E. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8942 · Cited by 12 · see citing works
“With the availability of the large amount of user-generated content, recently there has been much work trying to associate the content and writing styles with user’s ages (Schler et al. 2006; Argamon et al. 2007; Nguyen et al. 2013).”
BackgroundAnalysis of Linguistic Style Accommodation in Online Debates
Mukherjee, A., & Liu, B. (2012) · International Conference on Computational Linguistics · Cited by 11 · see citing works
Gender Identification via Reposting Behaviors in Social Media
Li, D., Li, Y., & Ji, W. (2017) · IEEE Access · doi:10.1109/access.2017.2785813 · Cited by 13 · see citing works
“Other similar works use function or content words based on largest information gain and word frequency [3], Part-Of-Speech (POS) unigram [4], and content word classes [6], to discriminate genders in the use of stylistic features.”
BackgroundPolitical Leaning Categorization by Exploring Subjectivities in Political Blogs.
Jiang, M., & Argamon, S. (2008) · Cited by 12 · see citing works
Self-citationTwitmographics: Learning the Emergent Properties of the Twitter Community
Cheong, M., & Lee, V. C. C. (2010) · Springer eBooks · doi:10.1007/978-3-7091-0294-7_17 · Cited by 12 · see citing works
Predicting users’ demographic characteristics in a Chinese social media network
Wang, Q., Ma, S., & Zhang, C. (2017) · The Electronic Library · doi:10.1108/el-09-2016-0203 · Cited by 12 · see citing works
Identifying the Authors’ National Variety of English in Social Media Texts
Simaki, V., Simakis, P., XPLAIN, A., Paradis, C., & Kerren, A. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_086 · Cited by 12 · see citing works
“The detection of gender, age, and other clues of the author’s personality and language has also attracted a great deal of attention (Argamon et al., 2007a; Cheng et al., 2011; Schler et al., 2006; Arg-amon et al., 2007b; Peersman et al., 2011; Rangel and Rosso, 2013; Simaki et al., 2015a,b, 2016,…”
BackgroundStylistic variation over 200 years of court proceedings according to gender and social class
Degaetano‐Ortlieb, S. (2018) · doi:10.18653/v1/w18-1601 · Cited by 11 · see citing works
“…social variables is considered (Prabhakaran and Rambow, 2017), but is mostly confined to age and gender (see e.g. Ardehaly and Culotta (2015); Argamon et al. (2007); Barbieri (2008); Burger et al. (2011); Eckert and McConnell-Ginet (2013); Holmes and Meyerhoff (2003); Hovy and Søgaard…”
BackgroundPlease help!: patterns of personalization in an online tech support board
Yardi, S., & Poole, E. (2009) · International Conference on Communities and Technologies · doi:10.1145/1556460.1556501 · Cited by 1 · see citing works
“Pennebaker, Kramer, and Joyce have studied large quantities of text using linguistic approaches like word count and clustering [ 4 , 19, 30].”
BackgroundDiscriminating Non-Native English with 350 Words
Henderson, J. C., Zarrella, G., Pfeifer, C., et al. (2013) · Cited by 11 · see citing works
An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection
Alsallal, M., Iqbal, R., Amin, S. A., et al. (2016) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2016.1 · Cited by 11 · see citing works
Age Identification of Twitter Users: Classification Methods and Sociolinguistic Analysis
Simaki, V., Mporas, I., & Megalooikonomou, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75487-1_30 · Cited by 11 · see citing works
“Content-based and style-based characteristics were explored in [2], [3], [20].”
BackgroundKimball, S. H., Hamilton, T. B., Benear, E., & Baldwin, J. (2019) · American Journal of Audiology · doi:10.1044/2019_aja-18-0136 · Cited by 11 · see citing works
- Authors
- Argamon, S., Koppel, M., Pennebaker, J. W., & Schler, J.
- Venue
- First Monday
- Publisher
- University of Illinois Libraries
- Published
- 2007-09-03
- ISSN
- 1396-0458 (ISSN-L)
- 1396-0466
- OpenAlex
- W2033444463
- Cited by
- 210
- References
- 0
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 10 · 2021: 7 · 2020: 8 · 2019: 13 · 2018: 35 · 2017: 26 · 2016: 25 · 2015: 13 · 2014: 15 · 2013: 19 · 2012: 12
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- First Monday (publishedVersion)
- First Monday (acceptedVersion)
- Touro Scholar (Touro College) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Human-Computer Interaction › Digital Communication and Language
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- blogosphere, blogging, age differences, gender differences, writing style, self-expression, blog content, corpus linguistics, English usage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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