1. Introduction
1.1. Research Background
Digital transformation is profoundly changing the way people access, select, and form awareness regarding economic, political, cultural, and social issues. This is one of the crucial requirements for the national development process and the construction of a digital society in Vietnam in the current period [1]. If in the traditional media environment, individuals actively search for and select information sources from various channels, in the digital media environment, the process of receiving information is increasingly dominated by content recommendation algorithms on social media platforms [3, p. 167194]. Through collecting and analyzing user behavioral data such as search history, watch time, likes, comments, and shares, algorithms continuously select, arrange, and prioritize content capable of generating high levels of engagement. Therefore, the information users access no longer fully reflects objective reality but is increasingly the result of a process of “constructing cognitive reality” by algorithms [3, p. 167194; 8].
For students – a social group with a high level of Internet and social media usage – algorithms are gradually becoming the new “gatekeepers” in the digital environment [3, p. 167194]. According to Digital 2025 Vietnam, Vietnamese youth belong to the demographic with the highest frequency of Internet and social media usage, wherein social media has become the regular source of information for most young people [9]. What students read, watch, share, or debate on social media is largely no longer actively chosen by them but is shaped by the platforms’ recommendation mechanisms [3, p. 167194]. This process affects not only information access but also how social awareness, attitudes, and behaviors are formed [8]. Under such conditions, tendencies to elevate the ego, assert oneself, and seek recognition in cyberspace tend to be more strongly reinforced. Algorithms prioritize content that sparks debate, stimulates emotions, or attracts interaction, causing extreme viewpoints to easily spread and be repeatedly amplified [7, p. 131-141; 8]. When users frequently encounter content aligning with their pre-existing beliefs, their capacity to accept multidimensional perspectives narrows, increasing the tendency to absolutize personal viewpoints and downplay communal responsibilities [4; 5; 6, p. 175-200].
In this context, extreme individualism is no longer merely a social phenomenon formed by economic, cultural, or psychological conditions, but is also significantly influenced by the operational mechanisms of the digital media environment. Social media algorithms do not directly create extreme individualism; rather, they act as an amplification mechanism, causing existing tendencies to be reinforced, spread, and manifested more distinctly in students’ awareness, attitudes, and behaviors [5; 7, p. 131-141; 8]. Therefore, researching the amplification of extreme individualism under the impact of social media algorithms not only contributes to clarifying changes in the digital media environment but also holds vital significance for political and ideological education, legal education, and the cultivation of a digital culture in contemporary higher education institutions [1; 2; 10, p. 2380-2396].
1.2. Research Gap
In recent years, alongside the development of digital media, numerous studies have analyzed the impact of social networks on social life and student behavior. Works in the fields of sociology and psychology have focused on elucidating the manifestations of individualism, whereas research on digital media has primarily addressed fake news, information polarization, digital culture, social media addiction, and platform governance. In the realms of communication and computer science, various studies have also explicated the operational mechanisms of recommendation algorithms, information personalization, and user engagement optimization [3, p. 167194; 4; 8].
However, these lines of inquiry have largely been pursued in isolation. Studies concerning individualism have yet to clarify the role of algorithms in amplifying ego-centric tendencies; conversely, research on algorithms has predominantly emphasized technical dimensions or media efficacy, paying little attention to their impact on the transformation of value systems and the manifestations of extreme individualism among students [3, p. 167194; 7, p. 131-141; 8]. To date, there remains a lack of a unified analytical framework to elucidate the relationship between social media algorithms and the amplification of extreme individualism. Concepts such as information personalization, filter bubbles, echo chambers, and confirmation bias are typically investigated in isolation, rather than being integrated into a cohesive explanatory mechanism for the transition from algorithmic operations to shifts in students’ awareness, attitudes, and behaviors [4; 5; 6, p. 175-200].
Addressing this gap, the present study approaches social media algorithms as an amplification mechanism rather than the direct cause of extreme individualism. Through theoretical analysis combined with a survey of 508 students, this research elucidates the process by which algorithms personalize the information environment, generate filter bubbles and echo chambers, and reinforce confirmation bias. Consequently, this process amplifies the manifestations of extreme individualism within students’ awareness, attitudes, and behaviors, thereby contributing to bridging the research gap regarding the social impacts of algorithms in the context of digital transformation [3, p. 167194; 8].
2. Research Objectives
The study aims to clarify the mechanism by which social media algorithms impact the amplification of manifestations of extreme individualism among students in the context of digital transformation. By systematizing the theoretical foundation of extreme individualism and the operational mechanisms of social media algorithms, the research analyzes the processes of information personalization, the formation of filter bubbles, echo chambers, and confirmation bias in the digital media environment. Consequently, the study clarifies how these mechanisms contribute to reinforcing and amplifying extreme individualistic cognitive, attitudinal, and behavioral tendencies among students, rather than viewing algorithms as the direct cause of this phenomenon. Simultaneously, through empirical survey results of students, the study proposes orientations to enhance digital capacity, critical thinking, a sense of social responsibility, and the effectiveness of political, ideological, and legal education in cyberspace, contributing to building a healthy digital environment in current higher education institutions [1; 2; 10, p. 2380-2396].
3. Theoretical Foundation
3.1. Extreme Individualism in the Context of Digital Media
Individualism reflects the elevation of the role, rights, and interests of the individual in social relations. In modern society, respecting human rights and promoting individual capacities are objective requirements of the development process [1]. However, when personal interests are absolutized and detached from community interests, individualism devolves into extreme individualism. This is a tendency that elevates the ego, downplaying social responsibility as well as legal and ethical standards. In this study, extreme individualism is approached across three dimensions: awareness, attitude, and behavior, with manifestations such as absolutizing personal perspectives, restricting the reception of differing opinions, and prioritizing personal interests over community values [1, 2].
In the context of digital transformation, social media creates a favorable environment for these manifestations to be exposed and spread more rapidly [3, p. 167194; 8]. The fact that personal value is increasingly measured through engagement metrics such as likes, comments, or shares makes the need for validation easily transition into a tendency to elevate the ego and pursue prominence [7, p. 131-141; 8]. For students – a demographic with high levels of Internet and social media usage that is concurrently in the process of forming its value system [9; 10, p. 2380-2396] – a lack of critical thinking and digital capacity makes them susceptible to manifestations such as absolutizing personal viewpoints, struggling to accept opposing opinions, equating freedom of expression with the right to unrestricted speech, or evaluating self-worth through online validation [7, p. 131-141; 8; 10, p. 2380-2396]. These manifestations are significantly impacted by the operational mechanisms of algorithms on social media platforms [3, p. 167194; 8].
3.2. Operational mechanism of social media algorithms in nnformation distribution
A social media algorithm is a system of computational models functioning to select, arrange, and distribute content for individual users based on the analysis of behavioral data [3, p. 167194]. Unlike traditional media, digital platforms employ artificial intelligence and machine learning to predict preferences, thereby determining the content displayed on each individual’s newsfeed [3, p. 167194; 8].
This mechanism is executed through information personalization. Based on search history, watch duration, interaction levels, and relational networks, algorithms continuously construct digital profiles of users and prioritize displaying content capable of generating high engagement [3, p. 167194; 8]. Consequently, each person gradually accesses a highly personalized information environment rather than a shared informational space [3, p. 167194; 4].
Simultaneously, these platforms operate on an engagement optimization mechanism, prioritizing content that is highly emotional, controversial, or strongly asserts personal viewpoints in order to prolong usage time and increase engagement levels [3, p. 167194; 7, p 131-141; 8]. Given the high frequency of social media usage among students [9], this mechanism not only dictates information access but also impacts how they perceive, evaluate, and react to social issues, thereby laying the foundation for amplifying the cognitive tendencies that already exist within users [3, p. 167194; 7, p 131-141; 8].
3.3. The amplification mechanism of extreme individualism under the impact of social media algorithms
In the digital media environment, social media algorithms do not directly create extreme individualism but exert an indirect impact by shaping the users’ information environment [3, p. 167194; 7, p 131-141; 8]. Through the mechanisms of selecting, arranging, and prioritizing content distribution, algorithms alter how individuals access and evaluate information, thereby reinforcing pre-existing cognitive tendencies [3, p. 167194; 8].
Firstly, the process of information personalization causes users to primarily access content aligned with their own search history and interactive behaviors, while differing perspectives appear at a lower frequency [3, 8]. This process gradually forms the filter bubble phenomenon, narrowing the capacity to access multidimensional viewpoints [4]. Concurrently, users’ tendency to connect with like-minded individuals further creates an echo chamber, where similar viewpoints are repeated and reinforced, making users susceptible to believing that their perceptions are correct or universally accepted [5]. This repetition contributes to reinforcing confirmation bias, increasing the propensity to trust information that aligns with existing awareness while reducing the receptivity to contrary evidence [6, p. 175-220].
For students-a demographic with high social media usage frequency currently forming their value systems [9] – this mechanism easily leads to a tendency to absolutize personal perspectives, elevating freedom of expression while neglecting social responsibility. This is subsequently manifested through behaviors such as confrontational debating, sharing unverified information, or seeking validation via engagement metrics on digital platforms [7, p. 131-141; 8]. Thus, algorithms do not create extreme individualism; rather, they operate as a mechanism to amplify, maintain, and propagate the manifestations of this phenomenon by shaping the users’ information environment.
4. Research Methodology
The study utilizes a combination of primary and secondary data sources to ensure the comprehensiveness and reliability of the research results. For primary data, the authors conducted a questionnaire survey from January to March 2026, encompassing 508 students from various higher education institutions, across different disciplines and academic years. The research sample was selected using a structurally controlled convenience sampling method to ensure relative representation for the surveyed group. The questionnaire was designed using a 5-point Likert scale, focusing on groups of questions regarding awareness of freedom of expression in cyberspace, the level of agreement with manifestations of extreme individualism, interactive behaviors on social media, and awareness of the impacts of these behaviors on the individual and the community. The collected data was processed utilizing descriptive statistics via frequency and percentage analysis.
For secondary data, the study inherits and analyzes scientific works, research reports, and statistical data from reputable domestic and international organizations, selected based on timeliness and reliability. This data source is utilized to cross-reference and supplement theoretical and practical foundations, while also supporting the interpretation of survey results within the analytical framework of the impact of social media algorithms on the amplification of extreme individualism among students. The integration of these two data sources not only enhances the empirical nature of the study but also ensures that the results are analyzed in connection with the operational mechanisms of the digital media environment, rather than stopping at merely describing the observed phenomena.
5. Research Results
5.1. Social Media Algorithms Change Students’ Information Reception Mechanisms
The development of recommendation algorithms has fundamentally changed the information reception mechanisms of students in the digital environment. Whereas users previously engaged in active information-seeking, today, social media platforms primarily distribute content via algorithms based on data concerning search history, watch duration, interaction levels, and relational networks. Consequently, students’ information reception process has increasingly shifted from proactive selection to receiving algorithmically recommended content [11, p. 895-913].
Survey results indicate that 75.8% of students use social media for 3 hours or more per day, of which 17.9% use it for over 6 hours daily. This demonstrates that social media has become the primary source of information for students, concurrently increasing their reliance on the platforms’ content distribution mechanisms.
Prioritizing the maximization of engagement, algorithms frequently recommend content aligning with users’ prior preferences and behaviors. As a result, students primarily access information that reinforces their pre-existing viewpoints, whereas differing perspectives appear at a lower frequency. The survey findings also reveal that many students lack full awareness of algorithmic dominance over their information reception process, leading to a tendency to equate the content on their newsfeeds with a comprehensive picture of reality. This narrows the capacity to access multidimensional information and negatively impacts critical thinking [4, 12].
Simultaneously, algorithms escalate the personalization level of the information environment, as each user accesses a distinct content system tailored to their behavioral data. Given that students spend a substantial portion of their time on social media, this process dictates not only what information they access but also influences how they form perspectives, evaluate social issues, and select behaviors in cyberspace.
Thus, social media algorithms are not merely information distribution tools; they have become a defining factor shaping students’ information reception mechanisms by selecting displayed content, restricting access to multidimensional information, and heightening personalization. This serves as a crucial foundation for explaining the amplification of users’ cognitive tendencies and values in the digital environment.
5.2. Algorithms Amplify Extreme Individualism
The research results indicate that social media algorithms do not directly generate extreme individualism but play a role in amplifying inherent tendencies through mechanisms of content personalization, interaction prioritization, and information repetition. This impact is manifested across three dimensions: awareness, attitude, and behavior [4; 11, p. 895-913; 12].
Regarding awareness, frequent exposure to content aligned with existing views reinforces confirmation bias and the tendency to absolutize personal experiences. Survey results show that 53.4% of students maintained a neutral stance on the statement “when debating online, I often defend my viewpoint to the end ”; 44.3% were neutral toward the assertion “my opinion is usually more correct than others”, and 41.34% were neutral regarding the statement “I find it hard to accept opposing opinions”. Furthermore, 26.97% of students agreed or strongly agreed that they find it difficult to accept opposing opinions, reflecting a declining capacity to receive differing perspectives.
In terms of attitude, 44.5% of students agreed or strongly agreed with the notion “on social media, I can say whatever I want” while approximately 30% believed that online statements bear minimal legal or ethical consequences. This indicates that a segment of students still exhibits a tendency to equate freedom of expression with the right to unrestricted speech. Meanwhile, algorithms often prioritize highly emotional and controversial content due to its high engagement potential, thereby exacerbating adversarial tendencies and eroding the spirit of dialogue.
Behaviorally, 57.7% of students reported acting more aggressively when utilizing anonymous accounts; 17.0% admitted to employing offensive language during online debates, and 46.5% had shared information without adequate verification. The survey also highlighted that posting content to garner likes, shares, or attention has become a prevalent motive among a subset of students. In a context where algorithms prioritize high-engagement content, this mechanism continually incentivizes attention-seeking behaviors, including disseminating unverified information or expressing extreme viewpoints [13, p. 321-326].
Overall, the research results affirm that social media algorithms are not the direct cause of extreme individualism but serve as an amplification mechanism for pre-existing tendencies. By reinforcing cognitive biases, elevating individual rights, and promoting interaction-driven behaviors, algorithms contribute to increasing the tendency to absolutize the ego in the digital environment. The resonance between the platform’s operational mechanisms and the psychological characteristics of students creates conditions for the manifestations of extreme individualism to spread in increasingly sophisticated forms.
6. Discussion
The research results underscore the necessity for an objective approach when examining the relationship between social media algorithms and the rise in manifestations of extreme individualism among students. Attributing extreme individualism solely to algorithms lacks scientific grounding, as it is a social phenomenon influenced simultaneously by economic, cultural, educational, and psychological factors. The algorithms themselves do not generate new values or ideologies; they merely process and distribute information based on user behavioral data. Therefore, absolutizing the role of technology leads to a simplistic interpretation of an inherently social phenomenon.
However, acknowledging that algorithms are not the direct cause does not equate to denying their role in the amplification process of extreme individualism’s manifestations. The research findings indicate that content personalization mechanisms cause users to consistently encounter information aligning with their preferences and existing viewpoints, thereby reinforcing established perceptions. For individuals inherently predisposed to elevating the ego, algorithms do not create new tendencies but rather sustain, legitimize, and distinctly manifest them. This mechanism is evident across all three dimensions: awareness, attitude, and behavior. Survey results show that a segment of students does not clearly distinguish between defending one’s stance and absolutizing personal opinions; simultaneously, incomplete awareness regarding freedom of expression in cyberspace persists. These limitations primarily stem from gaps in knowledge, skills, and social experience, but under algorithmic influence, they are reinforced through the repetition of congruent content, progressively translating into attitudes and behaviors.
Algorithms also alter the mechanisms of social value evaluation in the digital environment. As likes, shares, and follower counts become prevalent indicators of validation, users tend to adjust their behavior to maximize engagement. In the absence of self-regulation capabilities, the need for validation easily morphs into a tendency to elevate the ego and downplay social responsibility. Thus, algorithms do not direct users to become extreme, but they foster a conducive environment where such behaviors are reinforced and repeated.
From a theoretical perspective, algorithms should be viewed as an intermediary factor in the amplification process of extreme individualism, rather than the precipitating cause. Extreme individualism only thrives under the simultaneous impact of individual psychology, the social environment, media culture, and digital platform operational mechanisms; wherein algorithms act as catalysts, while education, family, school, and cultural environments remain the determining factors. Therefore, the issue at hand is not to restrict the development of algorithms, but to enhance students’ technological mastery, critical thinking, digital capacity, and sense of social responsibility to curb the amplification of deviant tendencies in the digital media environment.
7. Conclusion And Recommendations
7.1. Conclusion
This study has elucidated the relationship between social media algorithms and the amplification of extreme individualism among students within the context of digital transformation. Based on theoretical analysis and survey results from 508 students, it affirms that social media algorithms are not the direct cause of extreme individualism. Instead, primarily through mechanisms of personalization and prioritized content distribution, they reinforce and promote the pre-existing cognitive, attitudinal, and behavioral tendencies of users.
The research findings indicate that algorithms not only alter information reception mechanisms but also contribute to exacerbating ego-centric tendencies and irresponsible interactive behaviors in cyberspace. Therefore, mitigating the negative impacts of algorithms should be approached by enhancing students’ capacity for information mastery, critical thinking, and social responsibility, rather than focusing exclusively on technical solutions. Ultimately, this study asserts that algorithms should be conceptualized as an amplification mechanism for existing social tendencies, rather than the direct root cause of extreme individualism.
7.2. Recommendations
From the research results, it is evident that limiting the amplification impact of social media algorithms on manifestations of extreme individualism must be implemented comprehensively, with the focal point being the enhancement of students’ capacity to master the digital environment rather than concentrating exclusively on technical solutions.
For higher education institutions, it is imperative to strengthen digital capacity education aimed at helping students comprehend algorithmic operational mechanisms and the impacts of information personalization, while cultivating critical thinking, information verification skills, and access to multidimensional information sources [1; 10, p. 2380-2396]. Concurrently, cultivating an academic environment that encourages dialogue and evidence-based debate, whilst respecting differences, is essential to help curtail the radicalization of viewpoints.
For families and social organizations, it is necessary to promote the role of value orientation, fostering self-awareness, emotional regulation, and responsible behavior in the digital space. Alongside this, enhancing communication regarding digital culture and elevating community awareness of algorithmic impacts is critical.
For state management agencies, there must be continuous refinement of the legal framework governing digital platforms, increasing transparency in content recommendation mechanisms, controlling false information, and incentivizing technology enterprises to fulfill social responsibilities in algorithm design and operation. Simultaneously, the popularization of digital skills and media education for youth should be vigorously promoted.
For students themselves, who play the decisive role in mitigating algorithmic impacts: Each student must proactively recognize the operational mechanisms of algorithms, diversify information sources, verify content before reception and sharing, maintain a spirit of dialogue, and refrain from evaluating self-worth via online engagement metrics. Equipped with adequate digital capacity, critical thinking, and social responsibility, students will master algorithms instead of allowing algorithms to dictate their awareness and behaviors in cyberspace.
