Xorijiy lingvistika va lingvodidaktika –
Зарубежная лингвистика и
лингводидактика – Foreign
Linguistics and Linguodidactics
Journal home page:
https://inscience.uz/index.php/foreign-linguistics
Age-based lexical and semantic patterns in social media:
a comparative study of generational differences
Durdona ABDURAYIMOVA
1
Tashkent State University of Uzbek Language and Literature
ARTICLE INFO
ABSTRACT
Article history:
Received November 2024
Received in revised form
10 December 2024
Accepted 25 December 2024
Available online
25 January 2025
This study investigates how age influences lexical and
semantic patterns in social media communication. Analyzing
data from Twitter, Facebook, and Instagram, the research
focuses on four age groups: teenagers (13-19), young adults (20-
35), middle-aged adults (36-55), and older adults (56+). Results
reveal that teenagers and young adults exhibit more informal,
innovative language use, including slang and emojis, while older
adults tend to stick to formal and conventional language. This
study provides insights into how language evolves across age
groups in digital spaces.
2181-3701/© 2024 in Science LLC.
DOI:
https://doi.org/10.47689/2181-3701-vol3-iss1
This is an open-access article under the Attribution 4.0 International
(CC BY 4.0) license (
https://creativecommons.org/licenses/by/4.0/deed.ru
Keywords:
lexical variation,
semantic change,
social media language,
age-based linguistic
patterns,
generational differences,
sociolinguistics,
and digital communication.
Ijtimoiy tarmoqlarda yoshga asoslangan leksik va
semantik naqshlar: avlodlar farqlarini qiyosiy o‘rganish
ANNOTATSIYA
Kalit so‘zlar:
leksik variatsiya,
semantik o‘zgarish,
ijtimoiy tarmoq tili,
yoshga asoslangan lingvistik
xususiyatlar,
avlodlararo farqlar,
sotsiolingvistika,
raqamli muloqot.
Ushbu
tadqiqot
yoshning
ijtimoiy
tarmoqlardagi
muloqotining leksik va semantik xususiyatlariga ta’sirini
o‘rganadi. Twitter, Facebook va Instagram ma’lumotlari asosida
to‘rt yosh guruhining yozishmalarini tahlil qilish orqali tadqiqot
o‘smirlar (13-19 yosh), yosh kattalar (20-35 yosh), o‘rta
yoshdagi kattalar (36-55 yosh) va kattalar (56+ yosh) kabi
guruhlarni qamrab oladi. Tadqiqot natijalari shuni ko‘rsatadiki,
o‘smirlar va yosh kattalar ko‘proq norasmiy, yangilangan tilni
ishlatib, slang va emojilarni qo‘llaydi, ayni paytda kattalar rasmiy
va an’anaviy tilga moyil bo‘ladi. Ushbu tadqiqot yosh guruhlari
bo‘yicha
tilning
raqamli
makonlardagi
rivojlanish
tendensiyalarini ochib beradi.
1
Doctoral student, Tashkent State University of Uzbek Language and Literature. E-mail: Abdurayimovadurdona21@gmail.com
Xorijiy lingvistika va lingvodidaktika – Зарубежная лингвистика
и лингводидактика – Foreign Linguistics and Linguodidactics
Special Issue – 1 (2025) / ISSN 2181-3701
42
Возрастные
лексические
и
семантические
закономерности в социальных сетях: сравнительное
исследование различий между поколениями
АННОТАЦИЯ
Ключевые слова:
лексическое разнообразие,
семантические изменения,
язык социальных сетей,
языковые особенности по
возрастным группам,
межпоколенческие
различия,
социолингвистика и
цифровая коммуникация.
Данное исследование рассматривает влияние возраста
на лексические и семантические особенности общения в
социальных сетях. Анализируя данные из Twitter, Facebook
и Instagram, исследование фокусируется на четырех
возрастных группах: подростки (13–19 лет), молодые
взрослые (20–35 лет), люди среднего возраста (36–55 лет) и
пожилые (56+ лет). Результаты показывают, что подростки
и молодые взрослые чаще используют неформальный и
инновационный язык, включая сленг и эмодзи, тогда как
пожилые люди, как правило, придерживаются более
формального и традиционного стиля общения. Это
исследование помогает понять, как язык изменяется в
зависимости от возраста в цифровых пространствах.
INTRODUCTION
Social media has significantly altered how people communicate, making it a vital
area of sociolinguistic research. Various studies suggest that age plays a significant role in
shaping language use, with younger generations often at the forefront of linguistic
innovation (Eckert, 1988). While older adults maintain more traditional language forms,
younger users tend to adopt new slang, abbreviations, and emojis, often reflecting
a desire to create a group identity and distinguish themselves from older generations
(Sharma & Dodsworth, 2020). However, the relationship between age and lexical-semantic
variation on social media has not been fully explored. This study aims to fill this gap by
analysing the linguistic behaviour of four distinct age groups on platforms like Twitter,
Facebook, and Instagram.
Research Objectives
are to examine lexical and semantic differences in social
media communication across age groups; to investigate how age influences linguistic
innovation and semantic shifts; and to explore the practical applications of these findings
for marketing, education, and sociolinguistic research.
METHODS
2.1. Data Collection:
Data was gathered from Twitter, Facebook, and Instagram using API tools.
The dataset included a total of 1.5 million posts, categorized by age group:
Teenagers (13-19)
Young Adults (20-35)
Middle-aged Adults (36-55)
Older Adults (56+)
Preprocessing:
The collected data was cleaned and pre-processed to standardize and focus on
relevant linguistic elements. Steps included:
Xorijiy lingvistika va lingvodidaktika – Зарубежная лингвистика
и лингводидактика – Foreign Linguistics and Linguodidactics
Special Issue – 1 (2025) / ISSN 2181-3701
43
Tokenization and Lemmatization: Words were broken down into base forms to
ensure consistency.
Text Filtering: Non-linguistic elements like URLs, hashtags, and advertisements
were removed.
Language Filtering: Only English-language posts were retained for consistency.
2.2. Analytical Framework:
The analysis utilized several methods:
1. Lexical Analysis: The frequency of words, slang, abbreviations, and emojis were
analyzed across age groups.
2. Semantic Analysis: Word embeddings (e.g., Word2Vec, BERT) were used to track
semantic shifts in word meanings.
3. Sentiment Analysis: Sentiment analysis was performed using LIWC (Linguistic
Inquiry and Word Count) software to evaluate the emotional tones of posts.
4. Statistical Analysis: Chi-square tests and regression models were used to assess
the significance of the observed differences.
RESULTS
3.1. Lexical Variations:
Younger users were more inclined to use slang, abbreviations, and emojis. Terms
like “vibe”, “lit”, and “no cap” were common in the posts of teenagers and young adults. In
contrast, older adults preferred more conventional language, using words like “excellent”
instead of slang terms (Eckert, 1988). This shows a clear lexical divergence between
younger and older age groups.
3.2. Semantic Shifts:
Semantic shifts were particularly noticeable among younger users. For example,
“fire” was used to mean “great” or “cool” instead of its literal meaning related to flames. In
contrast, older adults continued to use words in their traditional sense, showing less
variation in meaning (Sharma & Dodsworth, 2020). This illustrates the age-related
evolution of word usage across platforms.
3.3. Sentiment Patterns:
Teenagers and young adults exhibited more extreme sentiments, often using
emotional language and exclamation points to convey excitement or frustration. Older
adults, however, displayed more neutral sentiments in their posts, avoiding over-
expressive language (Baron, 2010).
DISCUSSION
4.1. Interpretation of Findings:
The findings confirm that age influences language use on social media. Younger
users are more likely to innovate linguistically, a trend that has been observed in previous
research (Eckert, 1988). Their use of slang and emojis serves as a tool for group identity
and differentiation from older generations (Sharma & Dodsworth, 2020). This trend also
supports the notion that younger users are more adaptable to linguistic changes (Sumner
et al., 2012). Older adults, on the other hand, tend to adhere to more formal, traditional
language norms. This aligns with theories suggesting that older generations are more
conservative in their linguistic behaviour (Davies, 2013). The results suggest that social
media platforms, being spaces for social interaction, allow younger generations to
experiment with language, while older individuals maintain a more conservative approach
(Baron, 2010).
Xorijiy lingvistika va lingvodidaktika – Зарубежная лингвистика
и лингводидактика – Foreign Linguistics and Linguodidactics
Special Issue – 1 (2025) / ISSN 2181-3701
44
4.2. Implications for Sociolinguistics:
This study highlights the role of age in sociolinguistic variation. It confirms that
language use on social media can serve as an indicator of social identity, with younger
users embracing linguistic change while older users resist it. The study also underscores
the importance of considering age-based differences in digital communication research
(Milroy & Milroy, 1985).
4.3. Practical Applications:
1. Marketing:
Understanding the linguistic preferences of different age groups can
help marketers develop targeted advertising strategies. For instance, younger audiences
may respond better to informal language, emojis, and pop culture references, while older
audiences might prefer more formal, clear language (Pennebaker & King, 1999).
2. Education:
This study suggests that digital literacy programs should address the
generational differences in language use on social media. Understanding how language
evolves across age groups is crucial for developing effective communication strategies for
both personal and professional contexts (Yarkoni, 2010).
3. Digital Communication:
Social media platforms can be designed to cater to the
diverse linguistic preferences of different age groups, improving user experience and
interaction (Labov, 1972).
CONCLUSION
This study provides valuable insights into how age shapes lexical and semantic
patterns in social media communication. Younger users are more likely to innovate
linguistically and adopt new meanings for words, while older adults maintain traditional
language use. These findings have important implications for marketing, education, and
digital communication, offering practical applications for understanding and leveraging
age-related linguistic differences (Tausczik & Pennebaker, 2010).
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