The DOI matches its Crossref record.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cited by
Sorted by citations
Linguistic Inquiry and Word Count (LIWC)
Chung, C. K., & Pennebaker, J. W. (2012) · IGI Global eBooks · doi:10.4018/978-1-60960-741-8.ch012 · Cited by 2,215
Self-citationPersonality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
“components of word use to link gender and age, showing male components of language increase with age while female factors decrease [32].”
BackgroundGatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776
“…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).”
BackgroundMethodStatistics in Corpus Linguistics
Březina, V. (2018) · Cambridge University Press eBooks · doi:10.1017/9781316410899 · Cited by 796
Discriminating Gender on Twitter
Burger, J. D., Henderson, J. C., Kim, G., & Zarrella, G. (2011) · Cited by 586
Metadata
- 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
- Social Sciences › Linguistics and Language › 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 ›
Are you an author, or do you see a problem with this page? Request a review
