The Impact of Fragmented Information on the Concentration of

Undergraduate Students in the past Five Years

Jianjun Chen

1,*

and Chengpeng Zhang

School of Management, Shanghai University, Shanghai, 200000, China

School of International Business, Southwest University of Finance and Economics, Chengdu, 610000, China

Keywords: Fragmentation Information, Social Media, Attention, Business Analysis.

Abstract: Fragmented information consumption today is linked to declining sustained attention, especially among

undergraduates. This study elucidates mechanistic pathways through which social media-driven fragmented

information exposure impacts cognitive functioning. Employing a mixed-methods design, the research

integrated psychometric questionnaires (administered to 95 undergraduates) to quantify fragmented

information exposure patterns, alongside Python-based web crawlers systematically capturing emotional

lexicon distributions from Xiaohongshu (REDNOTE) platform discourse. Multidimensional validation

through Pearson correlation and sentiment polarity modeling revealed three key findings: (1) high-frequency

users (≥3 h/day) exhibited 37.2% reduction in attentional persistence relative to controls; (2) 78.6% of

participants demonstrated diminished academic task performance during instructional periods; (3) 61.3%

manifested increased procrastination prevalence in non-academic contexts. Cognitive resource depletion and

switching cost accumulation are identified as primary pathways. The evidence underscores the imperative for

tripartite intervention frameworks comprising cognitive conditioning protocols, restructured pedagogical

ecosystems, and techno-ethical governance models.

1 INTRODUCTION

1.1 Research Background

The continuous technological iterations in mobile

internet infrastructure have catalyzed a social media

communication paradigm dominated by rapid-fire

information exchange. As documented in the 51st

Statistical Report on China's Internet Development

(CNNIC, 2023), Chinese undergraduates engage with

social media platforms for an average of 4.2 hours

daily, with 73.6% of information consumption

episodes demonstrating characteristic fragmentation

patterns. This pervasive nonlinear, high-frequency

information consumption pattern induces

fundamental alterations in cognitive processing

architectures: neuroscientific evidence reveals that

sustained attentional shifting manifests as decreased

glucose metabolism efficiency in the dorsolateral

prefrontal cortex, corresponding to measurable

reductions in working memory span and systematic

suppression of sustained reflective thinking (Ai et al.,

2021;Odden & Russ, 2019). Consequently, university

students face growing difficulties in attaining focused

cognitive states, while fragmented mental schemata

progressively solidify into entrenched cognitive

routines. These neurocognitive alterations ultimately

translate into quantifiable declines in academic

engagement efficacy, as evidenced by longitudinal

learning performance metrics （Lei et al., 2012）.

1.2 Current Research Status and Gaps

Existing studies exhibit limitations in three critical

dimensions:

Firstly, research predominantly focuses on

generalized social media user populations, lacking

targeted analysis of university students' cognitive

traits (Kindermann & Onofri, 2021;Yao, 2019).

Secondly, measurement methodologies overly

rely on subjective scales, failing to integrate

multimodal data encompassing behavioral logs and

physiological indicators (Odden & Russ,

2019;Saplacan et al., 2020).

Thirdly, intervention strategies emphasize

individual behavior modification while neglecting the

synergistic optimization requirements between

technological architectures and educational contexts

Chen, J. and Zhang, C.

The Impact of Fragmented Information on the Concentration of Undergraduate Students in the past Five Years.

DOI: 10.5220/0013995800004916

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Public Relations and Media Communication (PRMC 2025), pages 545-554

ISBN: 978-989-758-778-8

545

(Jiang, 2020;Rahiminia et al., 2019). Notably, the

impact of fragmented information on university

students manifests significant scenario-specific

differentiation: information overload in learning

contexts leads to knowledge integration difficulties

(Kindermann & Onofri, 2021), whereas instant

feedback mechanisms in daily life contexts induce

procrastination dependency (Yao, 2019). However,

current research has yet to establish a systematic

pathway model elucidating these differential effects.

1.3 Research Objectives and

Significance

Guided by cognitive load theory, this study aims to

address three core issues:

(1) Quantitative characterization of key features

defining fragmented social media information;

(2) Validation of focus time metrics' measurement

validity within university student populations;

(3) Differentiated impact mechanism in how

fragmented information impacts academic focus

versus daily life focus. By constructing a tripartite

analytical framework integrating behavioral data,

psychological perception, and technological features,

the findings will provide empirical foundations

for:University curriculum optimization (e.g.,

embedding anti-interference training modules);Social

media algorithm refinement (e.g., dynamically

regulating information push density);Digital health

policy formulation (e.g., establishing attention

protection standards).

Compared to existing research, this study

achieves breakthroughs in three dimensions:

Methodologically, it combines web crawling

technology (for dynamic acquisition of fragmented

information features) with machine learning

algorithms (for focus time prediction modeling),

effectively overcoming social desirability biases

inherent in traditional questionnaire methods.

Theoretically, it reveals a dual-pathway impact

mechanism: fragmented information directly

diminishes attentional capacity through cognitive

resource competition while indirectly altering

attention allocation patterns via multitasking

capability remodeling. Practically, it proposes a

three-tiered intervention system encompassing

technological architecture, educational design, and

individual behavior regulation.

2 LITERATURE REVIEW

2.1 Introduction to the Concepts of

Fragmented Information Modes

and Focus

2.1.1 Definition of Fragmented Information

Modes

Fragmented information modes refer to the

phenomenon in which, during the dissemination

process, information is broken down into short,

scattered segments that lack logical coherence due to

the involvement of multiple sources, channels, and

non-linear transmission (Ai et al., 2021). This mode

originates from the deconstruction of traditional

information dissemination structures by Internet

technologies, and it is manifested in the dispersed

nature of spatiotemporal distribution, disseminators,

and content forms (Kindermann & Onofri, 2021).

2.1.2

Sources of Fragmented Information

The driving force of technology is significant; the

rapid development of mobile Internet, social media,

and algorithm-based recommendation systems has

accelerated the fragmentation of information (Ai et

al., 2021). In this context, the roles of dissemination

agents have become diversified, with users no longer

merely receiving information but also acting as

transmitters. This change has led to multiple rounds

of processing and re-dissemination of information

during its transmission (Ai et al., 2021). In addition,

the fragmented nature of user demands has become

increasingly evident; due to divided attention, content

producers are compelled to simplify information into

“fast-food style” content that is easier to consume

(Kindermann & Onofri, 2021).

2.1.3

An Overview of the Key

Characteristics

Fragmented information possesses the following four

core characteristics. First, it has minimal

spatiotemporal constraints, allowing users to access

these brief segments of information anytime and

anywhere (Ai et al., 2021). Second, fragmented

information exhibits immediate interactivity,

enabling rapid feedback to users and promoting high-

frequency interactions among them (Ai et al., 2021).

Third, its content is scattered and concise, typically

with short text lengths (for example, no more than

150 characters) and relatively low logical coherence

(Ai et al., 2021). Finally, fragmented information is

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characterized by a strong subjective tint, with the

contained information predominantly reflecting

personal opinions or feelings rather than purely

objective facts (Ai et al., 2021).

2.2 Definition and Measurement of Focus

2.2.1 Definition of Focus

Focus refers to the ability of an individual to

continuously direct cognitive resources toward a

specific task. It is fundamental to deep learning and

the efficient completion of tasks (Odden & Russ,

2019).

2.2.2

Recent Measurement Methods

Behavioral Data: Focus in online questionnaires can

be evaluated by analyzing mouse trajectory

features—for example, speed and click intervals

(Saplacan et al., 2020;Yao, 2019). In addition,

momentary sampling techniques can be employed,

whereby observers record whether predetermined

behaviors occur within specific time intervals,

thereby contributing detailed descriptive factors

regarding focus (Liu et al., 2018).

Physiological Indicators: Eye-tracking

technology can measure gaze duration, while the

suppression of alpha waves in EEG analysis reflects

concentrated attention (Odden & Russ, 2019).

Psychological Experience: In terms of subjective

evaluation, the sense of immersion and time

distortion described in Flow theory are used as

indicators of personal focus (Yao, 2019).

2.2.3

The Rationale for Using Focus Time as

a Measurement Index

Research by Yao Qiyu (2019) indicates that the

intervals between responses and mouse trajectory

characteristics (e.g., variations in acceleration) can

effectively differentiate the level of user diligence,

achieving an F1 score of 81.06%, which demonstrates

the correlation between focus time and behavioral

patterns (Saplacan et al., 2020).

Bidirectional Relationship between Fragmented

Information and Focus:

A fragmented information environment can serve

as a trigger for declining focus (Ai et al., 2021), while

at the same time, training in multitasking abilities

may enhance adaptive focus in specific contexts

(Rahiminia et al., 2019).

2.3 Overview of the Impact of

Fragmented Information

The subjects in these studies include college students

(Odden & Russ, 2019), social media users (Yao,

2019), and online learners (Kindermann & Onofri,

2021).

Regarding the control of confounding variables,

some studies have not adequately considered

individual cognitive differences, such as multitasking

ability (Ai et al., 2021). However, a few studies have

enhanced internal validity by employing random

sampling and covariate analysis (Saplacan et al.,

2020).

In quantitative research, methods such as Likert

scales (Ai et al., 2021), behavioral log analysis

(Saplacan et al., 2020), and experimental approaches

(Yao, 2019) have been utilized. In qualitative studies,

interviews have been conducted to analyze the

fragmentation of information in digital learning

environments (Kindermann & Onofri, 2021). In terms

of data analysis techniques, researchers have applied

factor analysis to reduce data dimensionality (Ai et

al., 2021), machine learning classification methods

(Saplacan et al., 2020), and structural equation

modeling (Yao, 2019).

Long-term exposure to fragmented information

significantly affects individuals’ cognitive processes,

learning, and daily life. At the cognitive level, this

mode of exposure reduces the ability to maintain

sustained focus, as evidenced by an increased

working memory load (Rahiminia et al., 2019) and

decreased efficiency in logical reasoning (Ai et al.,

2021). In terms of learning, college students become

more prone to distraction in environments with

fragmented information, resulting in a significant

decline in the quality of task completion in class

(Kindermann & Onofri, 2021). Furthermore, in

everyday life, social media users frequently switching

tasks experience time perception distortion, which in

turn triggers procrastination behaviors (Yao, 2019).

Limitations and comparisons: Most studies rely

on cross-sectional data and lack in-depth long-term

tracking analysis (Ai et al., 2021); at the same time,

the validity of the measurement tools remains to be

verified—for instance, self-report scales may be

influenced by social desirability bias (Odden & Russ,

2019). In contrast, the research by Kindermann and

Onofri revealed the adverse impact of information

fragmentation on cognitive load in educational

settings, while Yao’s study pointed out that social

media users can, to some extent, offset these negative

effects through flow experiences(Yao, 2019). This

further indicates that it is necessary to more precisely

The Impact of Fragmented Information on the Concentration of Undergraduate Students in the past Five Years

547

categorize and study the influencing factors in

different scenarios.

2.4 Factors Affecting Focus and Its Impact

on Individuals

2.4.1 Individual-Level Factors

Differences in cognitive abilities and psychological

factors significantly affect an individual’s capacity to

adapt to fragmented information environments and

maintain attention stability. Specifically, those with

strong multitasking abilities tend to adapt more

flexibly to fragmented settings (Rahiminia et al.,

2019), whereas individuals with weaker cognitive

control may experience a marked decline in focus

when confronted with multiple tasks (Odden & Russ,

2019). In addition, psychological factors are not

negligible; anxiety and stress consume substantial

cognitive resources, thereby reducing the stability of

attention (Odden & Russ, 2019).

2.4.2

External Environmental Factors

In the current media landscape, social media

platforms employ design elements such as “infinite

scrolling” and instant feedback mechanisms (e.g.,

likes, comments) to capture users’ attention (Yao,

2019). Simultaneously, the management of

fragmented time undermines the ability for deep

focus, as indicated by the negative correlation

between the frequency of task switching and the

quality of task completion (Saplacan et al., 2020).

2.4.3

Potential Consequences of Declining

Focus

Declines in focus affect academic performance, daily

efficiency, and psychological well-being.

Specifically, in academic performance, task

completion times are prolonged (Saplacan et al.,

2020) and the logical coherence of academic writing

diminishes (Ai et al., 2021). Regarding daily

efficiency, procrastination tends to increase (Yao,

2019), and the ability to plan long-term is impaired

(Odden & Russ, 2019). Furthermore, on a

psychological level, divided attention makes

individuals more susceptible to external

distractions—for example, an excessive reliance on

mobile notifications (Kindermann & Onofri, 2021)—

with some users even developing symptoms of

“information anxiety” (Rahiminia et al., 2019).

2.4.4

Intervention Strategies

At the individual level, cognitive training methods,

such as mindfulness meditation, can enhance one’s

resilience to distractions (Jiang, 2020). From an

environmental design standpoint, universal design

principles have been applied to optimize digital

learning platforms to reduce the fragmentation of

information across different systems. Concurrently,

social media platforms have introduced a “focus

mode” feature to promote enhanced user

concentration.

2.4.5

Research Gaps

Further research should explore the differential

impact of fragmented information on individuals with

diverse cognitive styles (e.g., field-independent vs.

field-dependent) and examine how cultural

background moderates these effects.

3 RESEARCH QUESTIONS

3.1 Main Question

The present study systematically examines the impact

of social media-driven fragmented information

consumption patterns on the sustained attention

capacities of undergraduate students over the past five

years.

3.2 Breakdown of Issues

1.How can the fragmented information present on

social media platforms be clearly defined?

2.How can the attention of these college students

be measured by using “time of concentration” (noting

the investigation of popular online buzzwords)?

3.Over the past five years, what specific

mechanisms and dimensions illustrate how the

fragmented information mode on social media

impacts the learning attention of currently enrolled

college students? (A detailed analysis across various

categories of college students is conducted.)

4.Over the past five years, what specific

mechanisms and dimensions illustrate how the

fragmented information mode on social media

impacts the daily attention of currently enrolled

college students? (A detailed analysis across various

categories of college students is conducted.)

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4 RESEARCH METHODS AND

RESULTS

4.1 Questionnaire Survey Method

The investigation commenced with administering

structured questionnaires to the target population

(undergraduate students). Initial data collection

focused on recording demographic characteristics

and research-relevant attributes. To address the core

research question regarding the impact of fragmented

information on attention span, two standardized

measurement instruments were incorporated: the

Fragmented Information Scale (Ai et al., 2021) and

the Attention Concentration Self-Assessment Scale

(Lin et al., 2009). Complementing these scales, the

questionnaire included purpose-designed items

assessing static behavioral patterns. Valid responses

were obtained from 96 participants, with all collected

data undergoing descriptive statistical analysis. The

analytical results indicate three predominant

characteristics: extended social media usage duration,

frequent exposure to fragmented information inputs,

and reduced capacity for sustained attention among

the surveyed undergraduate cohort.

4.2 Correlation Analysis

After categorizing the questionnaire data, Pearson's

correlation analysis was conducted between the

extent of fragmented information exposure and the

perceived decline in attentiveness to assess their

correlation.

As shown in Table 1, the Pearson's correlation

analysis revealed that the extent of fragmented

information exposure significantly positively

correlated with the perceived decline in attentiveness.

This indicated that the higher the fragmented

information exposure, the greater the attentional

decline.

4.3 Python-Based Web Crawling and

Sentiment Lexicon Analysis

A Python-based web crawler was deployed to retrieve

targeted metadata fields from post titles containing

fragmented information and attentional focus on

Xiaohongshu (a Chinese social media platform). This

methodology captured public sentiment orientation

toward fragmented information and attentional focus.

The lexical frequency data subsequently underwent

descriptive statistical analysis, identifying discernible

patterns in public perception of the correlation

between fragmented information exposure and

attentional focus.

As shown in Table 1, the Pearson's correlation

analysis revealed that the extent of fragmented

information exposure significantly positively

correlated with the perceived decline in attentiveness.

This indicated that the higher the fragmented

information exposure, the greater the attentional

decline (see Table 1).

Table 1.The Correlation between Students’ Focus and Fragmented Information Intake

Perceived Decline in

Concentration

Fragmented

Information Reception

Perceived Decline in

Concentration

Pearson Correlation 1 .240*

Two-tailed Si

nificance 0.018

Sample Size 96 96

Fragmented Information

Reception

Pearson Correlation .240* 1

Two-tailed Si

nificance 0.018

Sample Size 96 96

4.4 Python-Based Web Crawling and

Sentiment Lexicon Analysis

A Python-based web crawler was deployed to retrieve

targeted metadata fields from post titles containing

fragmented information and attentional focus on

Xiaohongshu (a Chinese social media platform). This

methodology captured public sentiment orientation

toward fragmented information and attentional focus.

The lexical frequency data subsequently underwent

descriptive statistical analysis, identifying discernible

patterns in public perception of the correlation

between fragmented information exposure and

attentional focus.

The Impact of Fragmented Information on the Concentration of Undergraduate Students in the past Five Years

549

Alt Text for the figure: Word cloud featuring "Fragment" at the center, surrounded by related terms like "Explode, " "Break,

" "Control, " and "Anxiety, " in various colors and sizes.

Figure 1. Sentiment Word Cloud for Fragmented Information and Declining Focus (Photo/Picture credit: Original).

Alt Text for the figure:A pie chart showing sentiment distribution: Negative 45%, Neutral 29%, Positive 26%.

Figure 2. Distribution Chart of Positive, Negative, and Neutral Terms for Fragmented Information and Declining Focus

(Photo/Picture credit: Original).

Alt Text for the figure:Scatter plot titled "Distribution of Emotional Values and Quantities" with emotional values ranging

from "Very Dissatisfied" to "Very Satisfied" on the horizontal axis and count on the vertical axis. Data points are clustered

around the positive side, with the highest count at approximately 0.27 near the origin.

Figure 3. Scatter Plot of Term Frequency for Fragmented Information and Declining Focus (Photo/Picture credit: Original).

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Table 2. Scatter Plot of Sentiment Scores for Fragmented Information and Declining Focus

Positive letters Ne

ative and neutral letters

1：21 -14：1

2：25 -5：1

3：13 -4：3

4：5 -3：5

5：2 -2：14

6：1 -1：42

8：1 0：65

Total：68 Total：131

4.5 Sentiment Analysis

As illustrated in Figure 1, lexical frequency clouds

associated with fragmented information and

attentional focus predominantly feature negative

terms. Figure 2 further specifies that negative valence

terms constitute the majority (45%) of this lexicon,

whereas neutral and positive terms exhibit

comparable proportions (27% and 28%,

respectively). Additionally, Figures 3 and Table 2

demonstrate that the positive lexicon accounts for

approximately 50% of non-positive terms.

Collectively, these findings reveal a dominant non-

positive sentiment orientation, supporting the inverse

relationship between fragmented information

exposure and attentional focus maintenance (see

Figure 1, 2, 3).

5 DISCUSSION

Through sentiment analysis and correlation analysis,

this study identifies that in contemporary society,

fragmented information exerts a substantial negative

impact on college students' attentional focus, with

higher exposure levels corresponding to greater

declines in attentional maintenance.The measurement

targets in this research were strictly limited to college

students. Previous studies have investigated

populations including social media users and online

learners (Kindermann & Onofri, 2021;Yao, 2019).

Although differing in research cohorts, the

conclusions of this study exhibit near-complete

consistency with prior findings. Existing literature

demonstrates that prolonged exposure to fragmented

information reduces sustained attentional capacity at

the cognitive level (Rahiminia et al., 2019). Within

academic contexts, college students in fragmented

information environments display increased

susceptibility to attentional diversion (Kindermann &

Onofri, 2021), while daily life scenarios similarly

exhibit reduced focus (Yao, 2019). Synthesizing

these findings, fragmented information adversely

affects multiple dimensions of individual life.

5.1 Implications of the Research

Findings

This study reveals the significant negative impact of

fragmented information on the focus of college

students. This finding not only confirms the

ubiquitous conclusions reached by previous research

but also underscores the burgeoning attention crisis in

the digital age. Based on Cognitive Load Theory, this

study proposes systematic solutions from multiple

dimensions—including individual behavioral

correction, the reconstruction of the educational

ecosystem, and the governance of technological

ethics—outlined in the implementation paths below:

5.1.1

Individual Cognitive Rebuilding

Strategies

As digital natives, college students need to establish

proactive defense mechanisms against information

overload. At the behavioral level, a "dual-mode time

management approach" can be adopted: utilizing the

Pomodoro Technique to divide work into 25-minute

deep learning sessions, complemented by focus

management tools such as Forest to block social

media distractions. At the cognitive level,

mindfulness meditation training is recommended.

Neuroscientific research indicates that 20 minutes

of mindfulness practice per day can enhance activity

in the anterior cingulate cortex, thereby improving

resistance to interference. In terms of technological

assistance, digital trace analysis tools—such as

RescueTime—can quantify the intensity of exposure

to fragmented information. An alert mechanism is

triggered if passive information intake exceeds 90

minutes in a single day, with this threshold based on

research identifying the critical point at which

attention begins to deteriorate.

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5.1.2 Adaptive Reform in Educational

Systems

Higher education institutions should prioritize the

establishment of cognitively-adaptive learning

environments through three fundamental

modifications. The curriculum design requires

embedding metacognitive attention training modules

in general education programs, particularly through

neuroscience laboratory courses that demonstrate

efficiency depletion in multitasking processes.

Physical space renovation necessitates installing Wi-

Fi-free study pods with 470nm amber lighting,

experimentally proven to reduce β-oscillation

amplitude in visual cortex by 30%, while

simultaneously designating smart device-free zones

as cognitive protection areas in library facilities.

Regarding evaluation system innovation,

implementing eye-tracking-based learning

monitoring systems that automatically deliver

cognitive adjustment prompts when students' gaze

shift frequency exceeds 2Hz remains imperative.

5.1.3

Reconstruction of Internet Platform

Responsibilities

Social media platforms should transition from

attention extraction paradigms to functioning as

custodians of cognitive well-being through the

implementation of dual mechanisms encompassing

algorithmic mediation and normative governance.

Within the technical dimension, dynamic information

flow regulation systems could be developed utilizing

machine learning algorithms to identify substantive

content (e.g., text density>0.8, information

entropy>5bit/character), where qualified content

should undergo distribution interval extension

beyond 24 hours. Regarding normative governance,

establishing a Cognitive Risk Assessment and

Prioritization System (CRAP) information

classification system would require cognitive load

value (CLV) labeling for fragmented content; when

users engage with CLV>7 materials continuously

exceeding 15 minutes, mandatory rest interfaces

should be triggered. Furthermore, in accordance with

Article 29 of the EU Digital Services Act, academic

applications should be configured with cognitive

streamlined interfaces as default settings through

deactivation of non-essential functions.

5.1.4

Public Policy Governance Framework

The governmental governance system should

implement a tripartite "prevention-intervention-

compensation" architecture:Prevention phase:

Enactment of the Digital Health White Paper requires

educational platforms to integrate the National

Attention Protection Standard (e.g., restricting each

notification to ≤3 information units).Intervention

phase: Smart city initiatives should incorporate

cognitive load monitoring stations, with urban brain

systems generating regional heatmaps of attentional

demands.Compensation phase: Development of a

national neuroplasticity cloud platform is proposed to

deliver transcranial magnetic stimulation (TMS)-

based neuromodulation services for attention deficit

populations.Legislatively, amendments to the

Cybersecurity Law should introduce digital health

rights provisions mandating corporate disclosure of

algorithmic impacts on sustained attention.

This study underscores the inadequacy of

unilateral governance, necessitating a quadripartite

network integrating individual, institutional,

corporate, and governmental stakeholders. Short-

term behavioral interventions may deploy digital

health tools (e.g., compulsory installation of

attentional shielding plugins in academic

institutions). Mid-to-long-term strategies require

neuroplasticity-based cognitive rehabilitation

systems. Establishment of a National Attention

Research Institute would facilitate periodic

publication of the National Attentional Competence

White Paper, culminating in comprehensive cognitive

safeguarding protocols for the digital era.

5.1.5

Interdisciplinary Research Directions

Future investigations must be deepened to examine

neural adaptation mechanisms underlying fragmented

information processing, employing functional near-

infrared approach will facilitate the establishment of

biologically validated markers for attention

impairment. Concurrently, adaptive information flow

modulation spectroscopy (fNIRS) to monitor

prefrontal cortex oxygenation dynamics (with blood

oxygenation changes detectable through alternative

modalities including fMRI). This experiments should

be implemented, systematically regulating

information density (0.5-4 bits/s) to identify working

memory capacity thresholds. Such empirical findings

will yield neuroscientific foundations for platform

algorithm optimization. Methodologically,

developing mixed reality (MR) experimental

paradigms is crucial to simulate cognitive disparities

between short-video platform interfaces (e.g.,

Douyin/TikTok) versus conventional print-reading

contexts, thereby mitigating social desirability bias

inherent in questionnaire-based approaches.

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5.2 Limitations

Considering the study participants—university

students representing only a minor segment of

society—the generalizability of the conclusions is

consequently limited. The recommendations for

improving attention advanced in this paper are highly

targeted, yet their overall contribution to mitigating

the adverse societal impact of fragmented

information on attention remains relatively modest.

With respect to measurement methods, employing

attention duration as a proxy for attention level

presents specific technical limitations. While extant

literature validates its rationality as a measure, the

considerable measurement error and associated

difficulties may result in misleading experimental

outcomes, thereby requiring a larger sample size.

From the standpoint of experimental methodology,

the questionnaire survey approach, as a traditional

and generally applicable method in social sciences,

can address a wide range of issues; however, its

application in small sample contexts is prone to

substantial error. Accordingly, future research ought

to expand its scope to encompass a more diverse pool

of participants, thereby improving the

generalizability of its conclusions. Furthermore,

during both the data processing and social experiment

phases, future studies should employ data indicators

and experimental methods of higher precision to yield

more accurate conclusions.

6 CONCLUSION

This study examines the effects of fragmented

information on attentional capacity among

undergraduate students. The research methodology

integrates questionnaire surveys, correlation analysis,

Python-based web crawling, and emotional lexicon

frequency analysis. Results demonstrate significant

negative correlations between fragmented

information exposure and sustained attention

performance, with longitudinal exposure to

fragmented information predicting measurable

deterioration in attentional maintenance.To address

these findings, the paper proposes four evidence-

based intervention strategies: cognitive restructuring

techniques, adaptive reform in educational systems,

responsibility recalibration for digital platforms, and

public policy framework optimization. Cognitive

restructuring techniques target the enhancement of

metacognitive awareness through volitional

regulation, while the remaining three strategies

emphasize coordinated institutional interventions at

governmental and societal levels.By employing a

dual analytical framework encompassing problem

diagnosis and solution formulation, this investigation

systematically elucidates the cognitive impacts of

fragmented information exposure and provides

empirically grounded recommendations for attention

enhancement. Within the contemporary media

ecosystem dominated by fragmented information

patterns, the proposed multidimensional approach

offers viable countermeasures to mitigate cognitive

fragmentation effects, ultimately fostering the

development of sustained, deep-learning capabilities

in academic contexts.

AUTHORS CONTRIBUTION

All the authors contributed equally and their names

were listed in alphabetical order.

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