Extracting age-related stereotypes from social media texts

From National Research Council Canada

Download	View accepted manuscript: Extracting age-related stereotypes from social media texts (PDF, 434 KiB)
Link	http://www.lrec-conf.org/proceedings/lrec2022/index.html
Author	Search for: Fraser, Kathleen C.¹; Search for: Kiritchenko, Svetlana¹; Search for: Nejadgholi, Isar¹
Affiliation	National Research Council of Canada. Digital Technologies
Format	Text, Article
Conference	13th Conference on Language Resources and Evaluation (LREC 2022), June 20-25, 2022, Marseille, France
Physical description	12 p.
Subject	stereotypes; bias; ageism; social media; computational social science
Abstract	Age-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community. Here, we present a method for extracting age-related stereotypes from English language Twitter data, generating a corpus of 300,000 over-generalizations about four contemporary generations (baby boomers, generation X, millennials, and generation Z), as well as “old” and “young” people more generally. By employing word-association metrics, semi-supervised topic modelling, and density-based clustering, we uncover many common stereotypes as reported in the media and in the psychological literature, as well as some more novel findings. We also observe trends consistent with the existing literature, namely that definitions of “young” and “old” age appear to be context-dependent, stereotypes for different generations vary across different topics (e.g., work versus family life), and some age-based stereotypes are distinct from generational stereotypes. The method easily extends to other social group labels, and therefore can be used in future work to study stereotypes of different social categories. By better understanding how stereotypes are formed and spread, and by tracking emerging stereotypes, we hope to eventually develop mitigating measures against such biased statements.
Publication date	2022-06-20
Publisher	European Language Resources Association (ELRA) ACL Anthology
Licence	Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/
In	Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022) (20 June 2022). https://lrec2022.lrec-conf.org/en/conference-programme/lrec2022-proceedings/.
Language	English
Peer reviewed	Yes
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Record identifier	2b7c79c7-7fab-492e-873c-00807b91c3ad
Record created	2022-09-20
Record modified	2022-09-21

Date modified:: 2025-11-17