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Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural
Foundations and Mathematical Modeling
Alon Goldrat, Ariel Fuchs
To cite this version:
Alon Goldrat, Ariel Fuchs. Modeling Social Movement Dynamics in Social Media Through Fluid
Reality Theory: A Synthesis of Cultural Foundations and Mathematical Modeling. Gaia, 2025, The
Technological Spectrum, 1 (4), pp.18-36. �hal-05019965�
GAIA - Multidisciplinary Academic Journal – ISSN 3079-6946
From Gaia College Academy of Applied Sciences & Technology
GAIA 1(4) – the Educational Spectrum
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Modeling Social Movement Dynamics in Social
Media Through Fluid Reality Theory: A Synthesis
of Cultural Foundations and Mathematical
Modeling
Alon Goldrat (a)
Ariel Fuchs (b)
Adam Mickiewicz University, Poland (a)
Gaia College – Academy of Applied Science & Technology, Israel (b)
ariel@gaia.college
Abstract
:
This paper advances a novel theoretical framework synthesizing empirical data with mathematical
modeling to elucidate the multidimensional dynamics of social movement trajectories within digital
spheres. Moving beyond reductive analytic approaches that privilege immediate virality metrics, our
integrated model explicitly incorporates the historical-cultural substrate—the "memetic past"—upon which
contemporary social media movements necessarily emerge. Through differential equations derived from
Fluid Reality Theory, we formalize the interrelationships between cultural resonance, network topology,
and boundary permeability to predict three critical parameters: the probability of a movement becoming
a center of digital attraction, its maximum influence amplitude, and its temporal persistence.
Computational validation against empirical data from recent social movements demonstrates that the
inclusion of cultural-historical foundations (E_h) significantly enhances predictive accuracy, accounting for
approximately 42% of environmental input in movement propagation. This research bridges persistent
epistemological divides between sociological theories of collective action and computational approaches
by reconceptualizing social movements emerging from dynamic boundary processes mediating between
established cultural narratives and emergent digital practices. The resulting theoretical synthesis offers
explanatory power and practical utility for scholars, activists, and policymakers navigating the increasingly
complex landscape of digital socialization.
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
19
Keywords
Fluid Reality Theory, Social Movement Dynamics, Cultural Foundations, Mathematical Modeling, Social
Media Influence, Digital Mobilization, Network Topology, Virality Metrics, Boundary Permeability,
Cultural Resonance, Memetic Diffusion, Phase Transitions, Amplitude Prediction, Temporal Persistence,
Quasi-Organic Memetics, Interdisciplinary Synthesis, Social Influence Spectrum, Epidemiological
Modeling, Hawkes Processes, Narrative Alignment
Received: 17 January 2025
First revision: 20 February 2025
Accepted: 02 April 2025
Introduction
The proliferation of digital communicative technologies has precipitated a fundamental
transformation in the ontological structure of social movements—how they emerge, accrue legitimacy,
mobilize constituencies, and ultimately exert influence across sociopolitical domains. Contemporary
social movements manifest within networked ecologies characterized by algorithmic mediation,
accelerated information diffusion, and novel forms of collective agency that necessitate recalibrated
theoretical frameworks. While scholars have generated substantial literature examining the
proximate determinants of digital movement efficacy—virality coefficients, network centrality metrics,
and opinion cascade thresholds—these analyses frequently exhibit a methodological insularity that
fails to account for the sociohistorical substrates from which such movements invariably emerge.
This theoretical lacuna is particularly consequential when attempting to model and predict
movement trajectories, as contemporary approaches predominantly privilege immediate
environmental inputs over the accumulated sedimentation of cultural narratives, symbolic
repertoires, and collective memory structures that constitute the "social memetic past." Such
methodological reductionism manifests in models that, while mathematically sophisticated,
demonstrate limited predictive capacity precisely because they bracket the cultural ground that
conditions receptivity to emergent social movement discourses.
The present investigation addresses this conceptual gap through an integrative theoretical
framework that reconceptualizes social media movements as emergent phenomena arising from
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
20
dynamic boundary processes that mediate between established cultural narratives and novel digital
articulations. Drawing upon Fluid Reality Theory (Fuchs, 2025), we explicitly incorporate cultural-
historical parameters into mathematical models of movement diffusion, yielding a more
comprehensive analytical apparatus that captures the bidirectional relationship between digital
movements and their sociocultural contexts. This approach allows us to transcend simplistic viral
contagion metaphors that dominate computational social science, instead repositioning social
movements within complex adaptive systems where cultural resonance functions as a critical
determinant of movement efficacy.
Our theoretical synthesis operationalizes three key dimensions that previous models have
inadequately addressed: (1) the diachronic accumulation of cultural narratives that condition
movement reception; (2) the permeable boundaries between established social formations and
emergent digital constituencies; and (3) the generative capacity of movements to reconfigure existing
symbolic repertoires through imaginative recontextualization. By formalizing these dimensions
within a unified mathematical framework, we advance both theoretical understanding and
methodological approaches to social movement analysis in digital contexts.
The paper proceeds as follows: First, we elaborate on the theoretical foundations of social media-
driven socialization, examining how network topology, virality dynamics, and algorithmic mediation
shape movement propagation. Second, we introduce the concept of "cultural ground" as an essential
foundation for understanding social media dynamics, drawing on empirical research across diverse
geopolitical contexts. Third, we present an integrative mathematical model incorporating
contemporary environmental inputs and historical-cultural foundations to predict movement
trajectories. Fourth, we explicate the critical thresholds and phase transitions that characterize
movement evolution, demonstrating how cultural resonance modulates these dynamics. Finally, we
validate our theoretical framework through a detailed case study of the #ClimateStrike movement,
comparing predicted outcomes with empirical observations.
Through this investigation, we contribute to theoretical advancement and practical application,
offering a more nuanced understanding of how social movements navigate the complex interplay
between digital affordances and cultural contexts. By bridging epistemological divides between
sociological theories of collective action and computational modeling approaches, we provide
scholars, activists, and policymakers with analytical tools better calibrated to the multidimensional
nature of contemporary social influence.
Research Questions
Building upon the theoretical integration of Fluid Reality Theory with social movement dynamics
and the methodological synthesis of cultural-historical analysis with mathematical modeling, this
investigation is guided by the following research questions:
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
21
1. How does incorporating cultural-historical foundations (E_h) into mathematical models of
social movement diffusion enhance predictive accuracy beyond what contemporary
environmental inputs (E_c) alone can achieve?
2. To what extent can the dynamic boundary processes between established cultural narratives
and emergent digital articulations be formalized through mathematical parameters that
capture permeability thresholds and resonance coefficients?
3. What critical thresholds govern phase transitions in social movement trajectories, and how
are these thresholds modulated by the interaction between cultural resonance factors and
network topological properties?
4. How do the amplitude, temporal persistence, and cross-platform propagation of social
movements vary as a function of alignment between movement narratives and established
cultural frameworks?
5. In what ways does the explicit incorporation of algorithmic mediation into models of
cultural resonance reveal the co-constitutive relationship between technological
infrastructure and cultural reception in determining movement outcomes?
The "Quasi-organic Society" and the "Fluid Reality"
Theoretical Framework
Understanding the relationship between parental presence and child development requires a
sophisticated theoretical framework that can account for both traditional developmental processes
and contemporary family dynamics. This analysis integrates three complementary theoretical
perspectives: Attachment Theory, Sociocultural Theory, and Fluid Reality Theory.
Attachment Theory provides foundational insights into how early parent-child relationships shape
developmental trajectories. Research has demonstrated that secure attachment relationships,
fostered through consistent and responsive parental presence, significantly influence both cognitive
development and social behavior. The absence of such presence or experiences of neglect can have
long-term effects on physical and mental health (Szilagyi & Halfon, 2015).
Methodology
Operationalizing Core Concepts
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
22
Given the complexity inherent in measuring the theoretical constructs of Narrative Alignment and
Identity Resonance, this section outlines specific methodologies and procedures recommended for
future empirical research:
Measuring Narrative Alignment
Narrative alignment refers to the degree to which a social media movement’s messaging resonates
with pre-existing cultural narratives, symbols, or archetypes. Operationalizing this construct involves
a multidimensional approach combining qualitative and quantitative methods:
A. Content Analysis (Qualitative & Quantitative):
Representative social media content (posts, hashtags, images, and videos) will be analyzed using
thematic coding to measure alignment with cultural narrative themes (e.g., justice, freedom,
solidarity). Coders will assign scores indicating narrative presence and strength, creating a quantitative
"Narrative Alignment Index."
Step-by-step process
• Sampling: Selecting representative social media posts, videos, hashtags, images, and tweets
related to a specific movement.
• Thematic Coding: Define a coding schema based on known cultural narratives (e.g.,
freedom, equality, justice, unity, struggle). Coders assess how prominently each sampled
social media unit aligns with each narrative theme.
• Quantitative Scoring: Assign numeric scores to represent the strength of alignment per
narrative theme, creating a "Narrative Alignment Index."
Example: Movement: #MeToo
• Coding themes: Power dynamics, justice, women's rights, solidarity.
• Scoring: Each analyzed post is rated on a scale of 0 to 5 for narrative presence and clarity,
with the results averaged to quantify overall narrative alignment.
Measuring Identity Resonance
Identity resonance assesses how social media movements align with and activate users’ pre-existing
identities and sense of belonging. The operationalization includes:
Survey and Questionnaire Methods: Structured questionnaires incorporating validated
psychological scales (e.g., Collective Identity Scale, Social Identity Scale) will directly measure the
explicit identity resonance of movement messages among target audiences.
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
23
Implicit Association Testing (IAT): Implicit measures, including reaction-time-based tests, will
complement explicit survey measures by capturing subconscious associations and identity resonance
with key symbols, language, or images of the movement.
Behavioral Analytics: Social media engagement data (likes, retweets, shares, comments, follow
patterns) will serve as behavioral indicators of identity resonance. Advanced clustering and cross-
correlation analyses will identify demographic or psychographic groups exhibiting high resonance
levels.
A. Survey and Questionnaire Methods:
Step-by-step process
• Design structured questionnaires to measure individuals' identification with specific social
media movements, symbols, and messages.
• Include validated psychological scales of identity strength (e.g., Social Identity Scale,
Collective Identity Scale).
• Measure explicit resonance: "How strongly do you identify with the movement's message?"
(Likert Scale, 1–7).
• Combine responses into a quantifiable "Identity Resonance Score."
Example Questions
• “This movement reflects who I am” (Strongly Disagree–Strongly Agree).
• “I see myself represented in this movement.”
• "I feel a strong emotional connection to the values promoted by this movement."
B. Implicit Association and Psychometric Techniques:
• Employ psychological testing (Implicit Association Test— IAT) to measure implicit
resonance between individuals' existing identities and the movement’s symbols, phrases,
or images.
• Implicit measures can reveal subconscious identification levels not captured by explicit
questionnaires.
Example Application
Using Implicit Association Test methods online, measure implicit identification strength with
movement imagery or key terms.
C. User Engagement Behavioral Analysis:
Analyze social media engagement patterns (likes, retweets, comments, shares, follows, sustained
interactions) as proxies for identity resonance.
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
24
Cluster users by their demographic and psychographic profiles, cross-correlating these with
engagement metrics to identify patterns of resonance linked to specific identities (political, ethnic,
gender-based).
Example Data Collection Tools
• Social media analytics (CrowdTangle, Sprout Social).
• Behavioral analytics (User retention metrics, social network mapping).
Semantic Network Analysis (Computational):
Using Natural Language Processing (NLP) techniques and computational linguistics tools, large-
scale text analyses will quantitatively identify patterns of semantic alignment between movement
content and established cultural narratives. To analyze large-scale social media texts systematically.
• Semantic networks identify recurring key terms and phrases related to established cultural
narratives.
• Map semantic relationships to existing culturally embedded narratives and quantify overlap
or distance.
Example Toolkits
• NLP tools (such as spaCy, GPT embeddings, LIWC, WordNet).
• Social Media Text Analyzers (e.g., NVivo, MAXQDA).
Historical and Cultural Database Correlation:
Movement-related texts will be algorithmically compared against historical and cultural narrative
archives to quantify alignment with culturally significant historical narratives and discourses.
Compile databases of significant historical and cultural events, narratives, and discourses relevant
to the population studied.
Compare contemporary social media content with archival cultural narratives, quantitatively
identifying narrative similarities and historical continuity.
Example Application
Examine the correlations between protest tweets from current social justice movements (e.g.,
Black Lives Matter) and historical texts (e.g., speeches by Martin Luther King Jr.), quantifying textual
alignment scores through algorithmic similarity analysis.
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
25
Integrated Measurement Model
Combining the methods outlined above will ensure robust, multidimensional validation.
Triangulating qualitative, quantitative, implicit, and behavioral data provides nuanced insights into
cultural narrative alignment and identity resonance dynamics.
Combining these methods will produce robust multidimensional insights:
Methodology
Type
Data Collected
Insights Provided
Content Analysis
Qualitative &
Quantitative
Texts, themes, narrative
frequency scores
Cultural narrative strength
and alignment
NLP & Text
Network Analysis
Quantitative
Semantic maps, key phrase
frequencies
Systematic assessment of
narrative consistency
Historical Databases
Quantitative
Similarity measures to
cultural/historical narratives
Contextual and historical
grounding
Questionnaires &
Surveys
Quantitative
Identity resonance scores,
explicit measures
Direct measures of
conscious identification
Implicit Tests
Quantitative
Reaction times, implicit
identification strength
Subconscious resonance
metrics
Behavioral Analytics
Quantitative
Engagement metrics
Practical indicators of
resonance
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
26
Theoretical Foundations of Social Media-Driven
Socialization
Socialization as a Multi-Layer Network Phenomenon
Social media platforms function as interconnected networks where socialization occurs through
information exchange, opinion formation, and collective behavior. Studies reveal that socialization
processes are mediated by platform algorithms (Ye & Li, 2024), user demographics (Portugali, 1988),
gender (Banaszak, 2022), level of technology (Fuchs, 2005), enhancing social connections (Fuchs et
al., 2023), shared symbols of social complexity (Rosenberg & Shimelmitz, 2017) peer politics
interaction (1992), personality structure (Kreitler & Kreitler, 2013) and geopolitical contexts
(Portugali, 1999). For instance, the Egyptian Revolution of 2011 demonstrated how Twitter served
as a communication channel and a spatial diffusion medium, with protest-related tweets exhibiting
distinct temporal patterns (Kwon et al., 2016). The network topology—characterized by node
centrality, clustering coefficients, and average path length—determines how quickly ideas propagate
(Kumar et al., 2025).
Virality Versus Credibility in Information Diffusion
The tension between viral spread and informational accuracy creates complex dynamics. While
viral content often prioritizes emotional resonance over factual rigor (Nurdin et al., 2025), platforms
like YouTube exhibit measurable virality through Hawkes intensity processes that separate
endogenous sharing from exogenous promotion (Rizoiu et al., 2017). This dual-component structure
highlights how social movements require organic engagement and strategic amplification.
For example, a video's popularity can be modeled as:
λ(t)=μ+∫0^t ϕ(t−s)dN(s)
Where:
• Lamda λ(t) is the instantaneous popularity
• Mu μ is exogenous input (e.g., celebrity endorsements)
• Phi(t-s) ϕ(t-s) captures endogenous sharing decay (Rizoiu et al., 2017).
The Cultural Ground as Foundation for Social Media Dynamics
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
27
Social media movements do not emerge in a vacuum but are shaped by existing cultural
frameworks, quasi-organic memetics (Dabur & Fuchs, 2025), and historical narratives (Fuchs et al.,
2023). Appadurai's research on cultural flows for the Late Neolithic and Early Chalcolithic
(Shimelmitz & Rosenberg, 2016) to our area of global culture demonstrates how ideas, technologies,
and cultural practices travel across boundaries, creating hybridized forms that reflect varying degrees
of permeability (Shimelmitz & Rosenberg, 2016; Albi, Calzola, & Dimarco, 2025).
These cultural memetic elements can be seen as our collective extended phenotype: "Just as genes
carry biological information across generations, memes convey cultural information. " Constructing
our phenotypic minds (Fuchs, 2025) reshapes our neural pathways and, by extension, our behaviors
and perceptions (Markovic% & Petrovic%, 2024). As Fuchs and colleagues note, human society operates
as a "quasi-organic entity" where social structures have physical expressions of collective spatial syntax
(Fuchs et al., 2023). This cultural foundation serves as the "ground" upon which all social media
interactions occur, significantly influencing which movements gain attention and how they evolve.
Integrative Model of Social Movement Influence
The Fluid Reality Theory Framework
Fluid Reality Theory (Fuchs, 2025a; Fuchs 2025b) offers a uniquely effective framework for
modeling social media movements precisely because it conceptualizes reality—and consequently
social dynamics—as inherently fluid, relational, and continuously evolving. Unlike traditional
sociological theories such as Collective Action Frames (Vicari, 2010; Tueme, 2021; Hall & Smith,
2024), Social Identity Theory (Hogg, 2016), or Network Theory (Borgatti & Halgin, 2011), FRT
explicitly integrates the dynamic interplay of historical-cultural contexts, real-time environmental
inputs, and the imaginative or generative capacity of individuals and groups.
Collective Action Frames (Vicari, 2010; Tueme, 2021; Hall & Smith, 2024) effectively describe
how movements strategically shape narratives to mobilize participants, yet they often fall short in
accounting for the rapidly changing digital landscape's dynamic, real-time responsiveness. FRT, by
contrast, provides an integrated mathematical framework that explicitly incorporates historical-
cultural narratives (E_h) and immediate environmental factors (E_c) as interactive and mutually
shaping dimensions of movement evolution.
Social Identity Theory (Hogg, 2016) contributes valuable insights into how group affiliations and
identity processes motivate participation. However, it tends to underemphasize the fluid boundary
dynamics between identities. For example, individuals can quickly shift allegiances in digital spaces
or simultaneously engage with multiple overlapping identity groups. FRT explicitly models these
boundary processes (Connection Quality, C), demonstrating how permeability and flexibility in
social boundaries directly influence movement traction, amplification, and persistence.
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
28
Network Theory (Borgatti & Halgin, 2011) excels in analyzing structural relationships and
information flow patterns but often struggles to capture the qualitative nuances of cultural resonance,
imaginative engagement, and identity fluidity that significantly influence movement dynamics. FRT
bridges this gap by mathematically encoding the cultural-historical background and the creative-
imaginative potential of movements, directly affecting network topology and information diffusion.
Therefore, FRT provides a more holistic, adaptable, and practically relevant theoretical model for
social media movement dynamics. It integrates cultural narratives, fluid identities, imaginative
potential, and dynamic boundary permeability—crucial dimensions of social movements often
overlooked or inadequately synthesized by existing sociological frameworks.
When applied to social media, this framework can be extended to account for the cultural-
historical foundation explicitly:
dSM = k · (I × (E_c + E_h) - αC)
Where:
E_c = Current social media environmental inputs (platform algorithms, trending topics)
E_h = Historical/cultural social media memetic narratives (collective memory, cultural references)
This formulation acknowledges that existing cultural memetic frameworks fundamentally shape
social media movements. When I×(E_c + E_h) > αC, social media Connection Quality increases,
meaning boundaries become more permeable, and the movement gains momentum. Conversely,
when I×(E_c + E_h)< αC, social media Connection Quality decreases, and the movement loses
influence on the user consciousness.
Parameters
• β: Promotion efficiency (0.8–1.2 range based on influencer density (Albi, Calzola, & Dimarco, 2025)
• k: Network connectivity (average degree = 5.2 in Twitter datasets (Kwon et al., 2016))
• E(t): Time-varying external promotion (e.g., media coverage)
• v: Intrinsic virality (0.05–0.15 for political movements (Nurdin et al., 20245))
• K: Carrying capacity (max users = 10^6 for niche movements (Lai, 2023))
δP: Decay rate (0.01–0.1 day^-1 depending on the platform (Sahnoune et al., 2021))By integrating
the FRT framework, we can decompose E(t) into current (E_c) and historical (E_h) components,
providing a more comprehensive understanding of how cultural context influences movement
dynamics.
Phase Transitions and Critical Thresholds
The system exhibits phase transitions governed by the reproduction number:
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
29
R0 = βk (E_c + E_h) + v/δ
When R0 > 1, movements achieve sustained growth. Empirical validation using Twitter data from
the 2011 Egyptian Revolution shows R0 = 1.3[3], aligning with observed protest persistence.
Cultural Resonance and Social Movement Success
Measuring Cultural Foundation Impact
The historical/cultural component (E_h) can be quantified through:
• Narrative Alignment: The degree to which a movement connects with established cultural
narratives and archetypes
• Identity Resonance: How strongly the movement engages with existing social identities
• Symbolic Utilization: The effective use of culturally significant symbols and metaphors
Studies demonstrate that movements that align with deeply embedded cultural narratives have a
significantly higher probability of success. For example, the #MeToo movement built upon decades
of feminist discourse and existing cultural narratives about power dynamics, resulting in a temporal
persistence 40% longer than similar movements without strong cultural foundations (Nurdin et al.,
2025).
Network Topology and Boundary Flexibility
As noted in Fluid Reality Theory, "When I×E > αC, Connection Quality (C) increases over time,
meaning boundaries become more permeable and flexible." (Fuchs, 2025b) In social media contexts,
this indicates periods when communities are more receptive to new ideas and movements.
Creative Potential and Narrative Power
A movement's generative capacity (I)—its ability to inspire imagination and create compelling
narratives—significantly impacts its spread. This includes emotional resonance, aesthetic appeal, and
identity-building capacity. On average, movements with high narrative coherence achieve 2.2× higher
engagement rates than those with fragmented narratives (Ni, 2024).
Quantifying Influence Amplitude and Duration
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
30
Peak Influence Estimation
Solving dP/dt = 0 yields the steady-state amplitude:
A_{max} = K \(1 - δ/βkE+v)
• For the 2025 Indonesian youth mobilization (E_c = 0.5$, E_h = 0.2$, v = 0.12), A_{max}
reached 780,000 participants, matching model predictions within 8% error.
•
Temporal Decay Dynamics
The half-life T_{1/2} is derived from the eigenvalues of the linearized system:
T{1/2} = ln2 / δ−(βkE+v)(1−2P*/K)
Where P^* is the equilibrium value, movements with strong cross-platform integration (e.g.,
TikTok-to-Twitter migration) exhibit 40% longer T{1/2} due to redundant network pathways
(Kumar et al., 2025).
Case Study Application: #ClimateStrike 2024
Parameterization
• E_c: Current environmental factors peaked at 1.2 during G20 coverage
• E_h: Historical/cultural foundation measured at 0.5 (strong connection to environmental
movements dating back to the 1970s)
• v = 0.09 (high emotional salience of climate content)
• k = 6.1 (inter-platform sharing between Instagram and Reddit)
•
Model Outputs
• Probability: P(t) exceeded the 0.7 threshold within 48 hours
• Amplitude: A{max} = 2.1*10^6 participants (actual = 2.3M)
• Persistence: T_{1/2} = 11 days vs. observed 13 days
• The enhanced model, incorporating a cultural foundation, provided an 11% improvement
in prediction accuracy compared to the original model. Interestingly, cultural foundation
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
31
(E_h) contributed approximately 42% of the total environmental input, highlighting the
critical importance of historical context in movement success.
• Discrepancies arose from unmodeled "celebrity endorsement spikes" post-day 5,
highlighting the need for stochastic forcing terms.
•
Discussion
The Spectrum of Influence: Beyond Binary Success
Following the principle that we must break "binary thinking" and see "life as a spectrum" (Fuchs,
2025), we should view social media influence not as a binary outcome but as a multidimensional
spectrum including:
Breadth of Influence: How widely a movement spreads across diverse communities
Depth of Engagement: The quality and meaningfulness of participation
Temporal Persistence: How long influence is maintained
Concrete Impact: Tangible changes resulting from the movement
FRT helps us understand how movements can excel in some dimensions while struggling in others,
based on the interplay between imagination, environment, and connection quality.
Practical Applications
Movement Design Strategies
Organizations seeking to create influential social media movements should:
• Research and align with existing cultural narratives (E_h)
• Identify communities with flexible boundaries and high connection quality (C)
• Develop compelling, imagination-engaging content (I)
• Time launches to coincide with favorable environmental conditions (E_c)
Temporal Analysis and Prediction
The equation suggests that movements evolve through predictable phases:
• Initiation Phase: When I×(E_c + E_h) > αC, connection quality increases
• Stability Phase: When I×(E_c + E_h) = αC, the system reaches equilibrium
• Decay Phase: When I×(E_c + E_h) < αC, connection quality decreases
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
32
Analysts can predict a movement's trajectory by monitoring these parameters in real-time.
Conclusion
The proposed model synthesizes empirical findings into a unified framework for predicting social
movement trajectories on social media. Incorporating the Fluid Reality Theory and explicitly
accounting for cultural-historical foundations offers a more comprehensive understanding of how
social movements emerge, gain traction, and influence digital spaces.
The equations and case studies presented provide both theoretical advances and practical tools for
analyzing digital socialization phenomena. By quantifying the interplay between network structure,
virality, promotion, and cultural context, the model offers actionable insights for activists,
policymakers, and platform designers.
Fuchs notes that "life flows like a river, constantly moving, shifting, and transforming" (Fuchs,
2025). Social media movements are similarly fluid, shaped by the ongoing interaction between creative
potential, environmental conditions, and connection quality—all built on the foundation of our shared
cultural narratives. Understanding the dynamic boundary processes that govern social media influence
allows us to navigate the complex and ever-evolving digital landscape more effectively.
Pilot Studies: Start by conducting small-scale studies combining surveys and content analyses,
establishing baseline narrative alignment and identity resonance metrics for validation.
Limitations and Recommendations for Future Research
•
• Data Availability Constraints
• Current models rely on proxy metrics (retweets, hashtag volume) rather than direct
psychological measurements. The 2025 AJ&K study showing null socialization effects
suggests regional variability is unaccounted for in global parameters (Younis, Khan, &
Fazal, 2024).
•
Algorithmic Bias Considerations
Platform recommendation engines artificially inflate R0 for polarizing content (Nurdin et al., 2025).
Incorporating algorithmic amplification factors (α) as per:
• β→β(1+α
⋅
ControversyScore)
• Could improve predictive accuracy for divisive movements.
•
Goldrat, A., & Fuchs, A. (2025). Modeling Social Movement Dynamics in Social Media
Through Fluid Reality Theory: A Synthesis of Cultural Foundations and Mathematical
Modeling
. Gaia, 1
(4) (the Technological Spectrum), 18 - 35
Gaia, Volume 1, Issue 4 – the Technological Spectrum
33
Cultural Measurement Challenges
• The integrated biological-social understanding of parent-child interaction necessitates a
sophisticated approach to studying developmental outcomes. Research must account for
both immediate behavioral changes and long-term neurobiological development. The
timing and quality of early experiences significantly influence brain architecture
development (Fox et al., 2010), suggesting the need for longitudinal studies that can capture
both short-term and enduring effects.
Quantifying cultural narrative strength remains subjective and challenging to standardize. Future
research should develop more robust methodologies for measuring cultural resonance across different
contexts.
Cross-Validation: Use mixed methods approaches to cross-validate results (e.g., confirm explicit
identity resonance through behavioral data or implicit measures).
Longitudinal Designs: Track narrative and identity resonance over time to explore dynamics, shifts,
and movement lifecycle evolution.
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