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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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“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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Items 101–200 of 209
Radford, J. S. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2804265 · Cited by 11
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Profiling the Age of Russian Bloggers
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Automatically Inferring Gender Associations from Language
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“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.”
BackgroundHilte, L., Daelemans, W. M. P., & Vandekerckhove, R. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.738278 · Cited by 12
Online Social Networks and Writing Styles–A Review of the Multidisciplinary Literature
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BackgroundMethodResultInfluentialA Data-driven Approach to Studying Given Names and their Gender and Ethnicity Associations
Malmasi, S. (2014) · Cited by 9
Piergallini, M., Doğruöz, A. S., Gadde, P. H., et al. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1012 · Cited by 9
“Popular prediction problems within this space include gender classification (Argamon et al., 2003), age classification (Argamon et al., 2007), political affiliation classification (Jiang & Argamon, 2008), and sentiment analysis (Wiebe et al., 2004).”
BackgroundGender Classification of Web Authors Using Feature Selection and Language Models
Aravantinou, C., Simaki, V., Mporas, I., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23132-7_28 · Cited by 9
“In a subsequent study [2], Argamon et al.”
BackgroundBlog/shop: it is authentic so don't worryˆˆˆ
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BackgroundThe Rest of the Story: Finding Meaning in Stylistic Variation
Argamon, S., & Koppel, M. (2010) · Springer eBooks · doi:10.1007/978-3-642-12337-5_5 · Cited by 8
Self-citationPredicting audience gender in online content‐sharing social networks
Xiao, C., Zhou, F., & Wu, Y. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22865 · Cited by 8
“, 2002), blogs (Argamon et al., 2007), news and e-mails (Cheng et al.”
BackgroundMethodUsing Syntactic and Semantic Context to Explore Psychodemographic Differences in Self-reference
Rouhizadeh, M., Ungar, L., Buffone, A. E. K., & Schwartz, H. A. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1219 · Cited by 8
Analysing Utterances in Polish Parliament to Predict Speaker’s Background
Przybyła, P., & Teisseyre, P. (2014) · Journal of Quantitative Linguistics · doi:10.1080/09296174.2014.944330 · Cited by 8
“Argamon et al. (2007) investigated if and how age and gender affect the writing style of blog authors.”
BackgroundYou Write like You Eat: Stylistic Variation as a Predictor of Social Stratification
Basile, A., Albert Gatt, A., & Nissim, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1246 · Cited by 8
“…or personal variables, ranging from geographical location and dialect (Zampieri et al., 2014; Han et al., 2014; Eisenstein, 2013) to age and gender (Argamon et al., 2007; Newman et al., 2008; Sarawgi et al., 2011; Johannsen et al., 2015; Hovy and Søgaard, 2015), as well as personality…”
BackgroundA Comprehensive Study of Learning Approaches for Author Gender Identification
Dalyan, T., Ayral, H., & Özdemir, Ö. (2022) · Information Technology And Control · doi:10.5755/j01.itc.51.3.29907 · Cited by 9
A study of age gaps between online friends
Liao, L., Jiang, J., Lim, E., & Huang, H. (2014) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2631775.2631800 · Cited by 6
“These features include function/content words, word classes [13], content word classes [3] and unigrams [20].”
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Simaki, V., Aravantinou, C., Mporas, I., & Megalooikonomou, V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24033-6_66 · Cited by 6
“[5] have applied factor analysis for gender and age classification in texts mined from the blogosphere.”
MethodDwivedi, V. P., Singh, D. K., Jha, S., & Ranvijay (2017) · International Conference on Advanced Computing · doi:10.1109/icoac.2017.8441506 · Cited by 6
“Some more recent attempts at classifying gender have used features such as content words, dictionary based content analysis results, POS (parts-of-speech) tags, feature selection along with a supervised learning algorithm and variable length POS sequence patterns ([4] [5] [6] [7]).”
BackgroundKučera, D., Haviger, J., & Havigerová, J. M. (2020) · Psychological Studies · doi:10.1007/s12646-020-00553-z · Cited by 6
Hilte, L., Vandekerckhove, R., & Daelemans, W. M. P. (2016) · Cited by 7
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Trevisan, B., Digmayer, C., Reimer, E., et al. (2015) · IEEE International Professional Communication Conference (ProComm) · doi:10.1109/ipcc.2015.7235799 · Cited by 5
“Applying these methods, [13] determines style- and content-based features in English blog texts providing evidence on the gender and age of an author.”
MethodClassifying Papers from Different Computer Science Conferences
HaCohen‐Kerner, Y., Rosenfeld, A., Tzidkani, M., & Cohen, D. N. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-53914-5_45 · Cited by 5
“These include use of linguistic features in classifying news stories [2], work on gender identification [15, 2, 13], work on classifying documents according to their author [29, 6, 22, 4, 32, 16, 17], and work on identifying the historical period and ethnic origin of different documents [10, 11].”
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Abdurahman, N. H. (2017) · Indonesian Journal of EFL and Linguistics · doi:10.21462/ijefll.v2i1.18 · Cited by 5
“…another limitation because the linguistic features characterising gender cannot be generalised from only the use of personal pronouns; there are a lot of features needed to take into account (Hosseini & Tammimy, 2016; Bamman, 2014; Newman et al., 2008; Argamon et al., 2007; Argamon et al., 2003).”
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Kernot, D. (2017) · Lecture notes in management and industrial engineering · doi:10.1007/978-3-319-55914-8_29 · Cited by 6
Kaati, L., Lundeqvist, E., Shrestha, A., & Svensson, M. (2017) · European Intelligence and Security Informatics Conference · doi:10.1109/eisic.2017.32 · Cited by 5
“For example, in regards to function words: women tend to more personal pronouns, conjunctions, and auxiliary verbs whereas male uses articles and prepositions more frequently [2].”
BackgroundA Stylistic Features Based Approach for Author Profiling
Kavuri, K., & Kavitha, M. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-0426-6_20 · Cited by 6
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Peersman, C., Daelemans, W. M. P., Vandekerckhove, R., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.02431 · Cited by 5
“…personal, informal writings listed in reverse-chronological order on a blogger’s web site (e.g., Sarawgi, Gajulapalli & Choi, 2011; Mukherjee & Liu, 2010; Zang & Zang, 2010; Goswami, Sarkar & Rustagi, 2009; Argamon, Koppel, Pennebaker & Schler, 2007; Nowson & Oberlander, 2007; Yan & Yan, 2006).”
BackgroundBřezina, V. (2018) · Cambridge University Press eBooks · doi:10.1017/9781316410899.004 · Cited by 4
Wisdom of words? Age differences in language and social media use in job applications
Brandt, P. M., & Herzberg, P. Y. (2022) · Current Psychology · doi:10.1007/s12144-021-02646-y · Cited by 4
A White-Box Sociolinguistic Model for Gender Detection
Sánchez, D. M., Moreno, A., & Jiménez‐López, M. D. (2022) · Applied Sciences · doi:10.3390/app12052676 · Cited by 4
“Argamon [56] also found that females used more conjunctions than males.”
BackgroundEvaluation and Sociolinguistic Analysis of Text Features for Gender and Age Identification
Simaki, V., Mporas, I., & Megalooikonomou, V. (2016) · American Journal of Engineering and Applied Sciences · doi:10.3844/ajeassp.2016.868.876 · Cited by 7
“They observed that specific forms and unigrams are more frequently used by young bloggers and that the writing style differs significantly among the age groups according to their classification in 10’s, 20’s, 30’s. Argamon et al .”
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Pennebaker, J. W. (2013) · Information Design Journal · doi:10.1075/idj.20.3.062pen · Cited by 4
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Simaki, V., Aravantinou, C., Mporas, I., & Megalooikonomou, V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23132-7_14 · Cited by 4
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“Language is often used to infer demographics of users, especially in classification tasks that tend to find clear distinctions between men and women (e.g. Burger et al., 2011; Argamon et al., 2007; Schler, 2006; Rao et al., 2010).”
BackgroundPapacharissi, Z., & Meraz, S. (2012) · The International Encyclopedia of Media Studies · doi:10.1002/9781444361506.wbiems074 · Cited by 4
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Chauhan, A., Toshniwal, D., & Tejwani, R. (2016) · International Conference on Computational Techniques in Information and Communication Technologies · doi:10.1109/icctict.2016.7514619 · Cited by 3
“Similarly [12], [13], [14], [10], [15], [16], [17], [6], [5], [4] have shown lexicons used by people can be used for understanding their personal values and show the different uses of these traits for recommendations.”
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Raess, M. L. (2018) · Cardinal Scholar (Ball State University) · Cited by 3
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Belbachir, F., Henni, K., & Zaoui, L. (2013) · doi:10.1109/aiccsa.2013.6616510 · Cited by 3
Blinka, L., Subrahmanyam, K., Šmahel, D., & Seganti, F. R. (2012) · Young · doi:10.1177/110330881202000304 · Cited by 3
“However, it appears that adult and youth bloggers may write about different things; Argamon et al. (2007) found that youth bloggers wrote more about home, romance and used swear words, whereas adults wrote more about business, politics or religion.”
BackgroundPiloting a theory-based approach to inferring gender in big data
Radford, J. P. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258555 · Cited by 3
“The Blogger corpus was originally created by [5] for demographic inference.”
BackgroundMethodTwitter Can Predict Your Next Place of Visit
Chauhan, A., Tejwani, R., & Toshniwal, D. (2016) · International Journal of Distributed Sensor Networks · doi:10.1155/2016/9274715 · Cited by 3
Recent Trends In Sarcasm Detection on Online Social Networks
K.Ranganath, ., M. S., & Shabana, S. (2017) · International Journal of Computer Sciences and Engineering · doi:10.26438/ijcse/v5i10.235239 · Cited by 3
“In this methodology we used a absolute list of features for the purpose of classification [11, 12].”
MethodIdentity Formation Among Teenaged Members of the Muslim Population of Ballyhaunis, Co. Mayo
McGarry, O. (2012) · Arrow@dit (Dublin Institute of Technology) · doi:10.13025/15992 · Cited by 3
Stolerman, A., Fridman, A., Greenstadt, R., Brennan, P., & Juola, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44952-3_12 · Cited by 2
“This application is quite di ff erent from author recognition because writings are mined for linguistic features to identify characteristics of their authors such as age and gender [5], native language [24] and personality characteristics [13].”
Flesch, M. (2024) · Gender and Language · doi:10.1558/genl.25734 · Cited by 2
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“One reason for our good performance could be because we have used different templates for both gender and age classification along with the generalized categories as defined in [5].”
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Chaput, L. (2013) · Scholarship@Western (Western University) · Cited by 2
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“Studies on private blogging revealed signi fi cant differences in the use of blogs between genders (eg, Argamon et al. , 2007; Herring and Paolillo, 2006; Hesse, 2008; Huffaker, 2004; Huffaker and Calvert, 2005; Nowson et al. , 2005; Pedersen and Macafee, 2006).”
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“Recent studies using the English language show that a number of stylistic and content-based indicators are significantly affected by both age and gender (Argamon et al., 2007).”
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Social-Media Users can be profiled by their Similarity with other Users.
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MethodAgreement mismatches in Dutch relatives
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Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Romine, W. L., Monadjemi, A., Thirunarayan, K., Sheth, A., & Pathak, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06843 · Cited by 2
“…employ more first-person singular pronouns (Chung and Pennebaker, 2007), and deictic language22 (Mukherjee and Liu, 2010), while males tend to use more articles (Argamon et al., 2007) which characterizes concrete thinking, and formal, informational and affirmation words (Newman et al., 2008).”
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“…1997; Labov, 2001). and the distinction between the content-focused language typically preferred by men and the contextual language favored by women (argamon et al., 2003, 2007; tannen, 1990). the research in this area typically focused on establishing statistical correlations between gender and…”
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“These models make use of the nuances of language to predict a certain criterion, and these models are called “LBAMs” (see Argamon et al., 2007; Boyd & Schwartz, 2021; Kjell et al., 2024; Park et al., 2015; Tausczik & Pennebaker, 2010).”
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“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.”
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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 ›
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