Original Article

BEYOND THE TREND: UNDERSTANDING MIMICRY CULTURE AMONG YOUNG SOCIAL MEDIA USERS IN KERALA

 

 

INTRODUCTION

Instagram and YouTube are much more than platforms where one makes new friends and entertains themselves on such platforms, rather, it is about performing one’s identity, producing culture, and validating social relations Pérez-Torres (2024). In the case of Kerala, which has very literate people, widespread smartphone ownership, and a highly connected youth, social media involvement in Kerala is linked to personal aspirations of getting educated and upwardly mobile. Young users in Kerala do not passively consume digital content; they actively engage with it, commenting, sharing, and creating content shaped by what is trending. Mimicry involves the replication or imitation of popular content formats, discourse patterns, body language, and aesthetic forms that are shared by influencers and feed algorithms, is one of the key characteristics of such interaction. Mimicry culture is defined as the phenomenon of socially reinforced trend mimicry arising from the relationship between algorithms, peers, and the need for digital visibility Jenkins (2006). The history of mimicry as a mode of performance within Kearl’s rich culture from stage entertainment to television mimicry provides an interesting local setting that explains how imitation became digitalized in this community Schechner (2003). In today’s world, mimicry takes the form of Malayalam reels performing dialogues of films, foreign fashion imitated with local humour, and lifestyle based on the influence of celebrities. What makes mimicry culture worthy from an academic perspective is not just the fun factor. Algorithms on social media platforms favour imitative content and thus create a feedback loop where imitation is rewarded over originality Gillespie (2014), Cotter (2024). Youth may learn to see imitation as a need when they find out that trending content is more successful than creativity, affecting not just what they produce but their own psyche Twenge (2017), Vijayalakshmi (2025). The study is aimed at analysing the association between the effect of social media platforms on the behaviour of youths in Kerala and the adoption of mimicry by them. For this purpose, Uses and Gratification Theory by Katz et al. (1973), Cialdini (2009) and the concept of Social Currency by Berger has been applied to understand why the youths employ mimicry.

 

REVIEW OF LITERATURE

 Social media as a platform of communication

The evolution of social media has changed how we communicate. It has moved from passive sharing to a process where audiences take part in creating messages Fuchs (2014), Jenkins (2006). This is where the youth functions both as an audience, producer and distributor, interacting using likes, shares, and comments. Short-form video formats Reels, Shorts, TikTok have emerged as the dominant mode of youth digital engagement, prioritizing visual, emotionally resonant, and easily replicable content Yin (2024). This environment structurally encourages mimicry: platform affordances such as remix tools, trending audio libraries, and viral challenges make imitation technically seamless and socially rewarded Abidin (2018).

Mimicry as a Digital Cultural Practice

Contrary to mere copying, mimicry in cyberspace acts as a communicative or cultural practice whereby one imitates or emulates existing trends, norms, and practices. In case of young users, it acts both as self-expression and means of integration into society. Platforms encourage copying through trending hashtags, music clips, filters, and challenges, rendering imitation easier than innovation. This phenomenon has been well documented in literature: original material is algorithmically penalized compared to recognizable, trending formats Van Dijck (2013), Nieborg and Poell (2018).

Algorithmic Visibility and Participatory Cultures

Platform algorithms shape content visibility and, by extension, user behaviour. Recommendation systems on Instagram and YouTube priorities engagement signals watch time, likes, shares creating self-reinforcing cycles that amplify already-popular content Kaye et al. (2024), Cotter (2024). With experience, people will know which format is successful and create content accordingly, resulting in the ‘phantomization’ of creativity as described by Poell et al. (2019), whereby creative practice is subordinated to platform logic. Features like gamification also promote user engagement and encourage imitation Butler (2024), Wallace (2025).

Socio-Cultural Impact on Indian Youth

Studies into the impact of digital technology on Indian adolescents indicate considerable changes in their identities, behaviour, and cultural values Vijayalakshmi (2025). In the case of Kerala, there is a localization of international tendencies, where Malayalam language mixes with internet English while Korean popular culture influences local trends Pennycook (2007). Appadurai (1996). This hybridization reflects active cultural negotiation but also introduces pressures related to social comparison and self-evaluation Featherstone (2007).

Influencer Culture and Research Gap

Mimicry culture operates as a major node for influencers, being the point from which the styles, behaviours, and formats of the content are replicated Freberg et al. (2011). Social Comparison Theory Festinger (1954) highlights the process that takes place when one encounters an idealised version of reality on social media: upward comparison drives the individual to emulate those who are perceived to be better off in order to overcome this deficiency. Many studies exist in the realm of social media and adolescents’ well-being; however, they do not consider mimicry as a focal element of social media usage. Literature pertaining to India and the state of Kerala specifically is limited.

 

 

 

 

REVIEW OF LITERATURE

 Research Design

A mixed method approach to research is adopted by this study in the sense of quantitative information which is used together with qualitative information obtained through interviews. The former examines the relationship between the level of influence of the social media platforms under discussion and mimicry. The latter uses interview data from three clinical psychologists.

Participants and Sampling

Purposive sampling employed for selection of active social media users between the ages of 18-28 years from Kerala state. These individuals represent the key demographic group of digital natives who are in a stage where identity is still evolving and the major consumers of trend-setting media content. Minimum sample size was calculated using Cochran’s formula: n₀ = Z²p(1-p)/e² = (1.96) ² × 0.50 × 0.50/(0.05)²≈ 384. The data from 384 respondents obtained by distributing an online survey questionnaire in Ernakulum, Thiruvananthapuram, and Kozhikode regions. In this, Gender distribution was 30 percent males and 70 percent females.

Instruments and Theoretical Framework

The study uses two distinct constructs, which each include five Likert scale items; answer choices range from 1(Strongly Disagree) to 5 (Strongly Agree). Social Media Platform Influence (SM) – gauges how platform influences engagement with its functionalities. Mimicry Practices (MP) – gauges how often individuals engage in imitative behaviour and adopt trends. Five themes in the expert interview are: platform influence, influencer influence, social validation, trend pressure, and mimicry’s psychological implications. Three theoretical perspectives guide the study. Uses and Gratifications Theory Katz et al. (1973) views mimicry as an intentional process for satisfying social needs through expression. Social Currency (Berger) shows how sharing of trending information can increase one’s digital value. Social Validation Theory Cialdin (2009) helps understand why positive feedback from the audience ensures that imitative actions become ingrained. Technological Determinism Van Dijck (2013) gives the structural underpinning, since platform algorithms create and encourage mimicry.

Data Analysis

Descriptive statistics and Pearson correlation coefficient analyses were used to analyse quantitative data (N=384) to evaluate the proposed link between SM Mean and MP mean. Themes for analysing qualitative data obtained through interviews were generated from both the interview structure and emergent responses.

 

RESULTS AND DISCUSSION

 Descriptive Statistics

For both variables, the average was greater than 3.6 out of 5, implying that the survey participants agreed with the statements depicting social media-induced imitation. The small standard deviation values for both variables (SM = 0.354; MP = 0.384) indicate that all responses were tightly clustered around the mean.

 

Table 1 Descriptive Statistics for Social Media Influence and Mimicry Practices (N = 384)
Variable	Mean	Std. Deviation
Social Media Influence (SM)	3.742	0.354
Mimicry Practices (MP)	3.661	0.384

Platform Usage and Trend Exposure

Instagram is the leading platform used by 52.5% solely and 38.4% combined with YouTube, a fact which is understandable given its focus on the visual nature of Instagram Reels. The data about the immediate appearance of viral content among the surveyed individuals (68.1%) shows the efficiency of the algorithm which recommends such popular content Cotter (2024). The view that following trends increases visibility (87.8%) is widespread since it relies on the common belief that being algorithmically visible is contingent upon one’s willingness to engage with current trends.

 

Table 2 Platform Usage and Trend Exposure Patterns Among Kerala Youth (N = 384)
Measure	Category	Percentage
Primary Platform	Instagram only	52.5%
Primary Platform	Instagram + YouTube	38.4%
Viral content speed	Appears almost immediately	68.1%
Trend-following aids reach	Agree / Strongly Agree	87.8%
Original < trend engagement	Agree / Strongly Agree	~80%+

Correlation Analysis

Pearson correlation analysis exists a strong positive correlation between Social Media Influence and Mimicry Practices, r = 0.571, p < .001. Thus, it is evident that as one’s influence on social media increases, mimicry practices tend to increase accordingly. Null hypothesis H0 has been rejected, whereas alternative hypothesis H1 has been accepted. As per R² results of approximately 0.326, social media influence explains almost 32.6% of the variations in mimicry practices, while the rest is explained by other individual and situational factors.

 

Table 3 Pearson Correlation— SM and MP. **p< .01 (2-tailed)
SM	MP
PEARSON R	1.000	0.571**
SIG. (2-TAILED)	—	.000
N	384	384

 

 

 

 

 

 

 

The results from the correlation analysis at an item level show very high correlations between items in different constructs; the item testing the influence of influencers on fashion choices (SM4) has particularly high correlation with feeling influenced by others to dress like them, often witnessed on social media (MP5: r = 0.87).

 Experts Insights

The qualitative interview results conducted with the help of the psychologists residing in Kerala were quite useful in elaborating on the quantitative findings. Motivations of belongingness and FOMO seemed to be instrumental for engaging with trends. According to one of the counsellors in Kozhikode, trend setting was a way for young people to gain acceptance and become part of the society in the light of Uses and Gratifications Theory. According to a clinical psychologist from Thiruvananthapuram, trend following was possible to help achieve digital literacy and a sense of voice in social environments, but excessive mimicry could result in loss of personal identity and external validation. Thirdly, the opinion of a citizen from Kollam regarding conformity through the prism of Asch theory revealed that trend setting among youth was dictated by group pressure rather than the desire to follow the fashion trends themselves and required substantial psychological effort.

 

CONCLUSION

 Summary of Findings

In this the culture of imitation was analysed among 384 individuals between the ages of 18-28 years using social media platforms in Kerala. It has been observed that there is a highly significant positive correlation between social media platform influence and the practice of imitation, r = 0.571, p < .001, explaining around 32.6% of variance. As evident from high means for both concepts (SM: 3.742; MP: 3.661), the practice of imitation via social media platforms is a widespread phenomenon as opposed to an isolated one. Instagram has 90.9% usage rate while viral content is encountered by 68.1% of users instantly. The idea that imitation increases exposure rate (87.8%) explains the structural motivation behind the act.

Implications

The educational system must incorporate media literacy programs that teach students about the impact of algorithms on fostering imitation rather than creativity. The content creator must think about how he is contributing to cycle formation. Psychologists must incorporate social media comparison and validation into their psychological assessment process. The platform creator must ensure that not only engagement but also innovation is rewarded by the algorithm.

 

 

 

Limitations and Future Research

The cross-sectional design limits causal inference; longitudinal research is needed. Purposive sampling from Kerala’s urban areas limits generalisability. Future research should conduct comparative analyses across Indian regional cultures, explore creator motivations, examine specific content genres in relation to mimicry intensity, and test media literacy intervention models.

Final Conclusion

Mimicry culture is now an integral part of the social media engagement of youths in Kerala. The trend of imitation based on algorithms and peer pressure and the need for validation and affiliation is now prevalent among youth and has replaced their ability to create content on their own online. This trend ensures immediate affiliation and visibility, it has detrimental effects on their creativity and psychological well-being. Combating mimicry culture requires concerted action on the part of educators, social media platforms, parents, and mental health professionals to ensure that youth become creators and not consumers of digital culture.

ACKNOWLEDGMENT

The author wishes to thank Supervisor Dr. Lakshmi Ravindran, Department of Visual Media and Communication, Amrita Vishwa Vidyapeetham, Kochi Campus, for guidance throughout this research. Gratitude is also extended to the clinical psychologists who participated as expert informants and to all survey respondents.

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