MOTHER'S DAY SPECIALS! SHOW ME MORE

Close Notification

Your cart does not contain any items

Authorship Analysis in Chinese Social Media Texts

Shaomin Zhang (Guangdong University of Foreign Studies)

$32.95

Paperback

Not in-store but you can order this
How long will it take?

QTY:

English
Cambridge University Press
04 April 2024
This Element explores the sentiment and keyword features in both authorship profiling and authorship attribution in social media texts in the Chinese cultural context. The key findings can be summarised as follows: firstly, sentiment scores and keyword features are distinctive in delineating authors' gender and age. Specifically, female and younger authors tend to be less optimistic and use more personal pronouns and graduations than male and older authors, respectively. Secondly, these distinctive profiling features are also distinctive and significant in authorship attribution. Thirdly, our mindset, shaped by our inherent hormonal influences and external social experiences, plays a critical role in authorship. Theoretically, the findings expand authorship features into underexplored domains and substantiate the theory of mindset. Practically, the findings offer some broad quantitative benchmarks for authorship profiling cases in the Chinese cultural context, and perhaps other contexts where authorship profiling analyses have been used.

This title is also available as Open Access on Cambridge Core.
By:  
Imprint:   Cambridge University Press
Country of Publication:   United Kingdom
Dimensions:   Height: 229mm,  Width: 152mm,  Spine: 6mm
Weight:   177g
ISBN:   9781009324250
ISBN 10:   100932425X
Series:   Elements in Forensic Linguistics
Pages:   114
Publication Date:  
Audience:   General/trade ,  ELT Advanced
Format:   Paperback
Publisher's Status:   Active
Series Preface; Prologue: The Neglected Language Clue and Recondite Forensic Linguistic Evidence in a Real 'Suicide Notes' Case; 1. Introduction; 2. Research Design; 3. Results and Discussion; 4. Conclusion; References.

See Also