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Large Scale Personality Classification of Bloggers
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“Not limited to categorical language they found open-vocabulary features, such as bigrams, to be better predictors than LIWC features.”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [67]
In the citing paper
“When we do such an approach, our scores are in similar ranges to such literature: to classification accuracy.”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [67]
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Metadata
- Authors
- Iacobelli, F., Gill, A. J., Nowson, S., & Oberlander, J.
- Affiliations
- Northeastern Illinois University (US)
- University of Surrey (GB)
- University of Edinburgh (GB)
- ORCID
- Iacobelli, Francisco
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Pages
- 568-577
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1562816477
- Cited by
- 136
- References
- 26
- Citations by year
- 2025: 3 · 2024: 9 · 2023: 4 · 2022: 9 · 2021: 14 · 2020: 13 · 2019: 16 · 2018: 14 · 2017: 11 · 2016: 12 · 2015: 9 · 2014: 10 · 2013: 8 · 2012: 4
- FWCI
- 15.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Open Research (University of Surrey) (submittedVersion)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- personality classification, bloggers, Big Five personality traits, feature selection, LIWC, language and personality, online text analysis
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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