Social Media Feed Value Alignment

• Social Media Feed Value Alignment is the design of ranking algorithms that integrate explicit human values using computational models and LLM-based value extraction.
• It leverages adjustable user controls and transparent interfaces to enable fine-grained, real-time value trade-offs in feed curation.
• Empirical evaluations demonstrate that value-aligned feeds can reduce polarization and enhance content recognizability, fostering pluralistic and equitable platforms.

Social media feed value alignment refers to the design and implementation of feed ranking algorithms that explicitly account for a wide range of user, societal, and political values—moving beyond the default paradigm of maximizing engagement to constructing informational environments that reflect articulated human values, priorities, and trade-offs. Contemporary approaches leverage computational modeling of values, user controls, transparency mechanisms, optimization under social objectives, and empirical evaluation, addressing the technical challenge that value-neutral feed curation is neither achievable nor desirable in complex social systems.

1. Theoretical Foundations and Value Modeling

Foundational to value alignment in social media feeds is the operationalization of explicit value systems as algorithmic objectives. Schwartz’s theory of Basic Human Values, which enumerates 19 fundamental values arranged on a circumplex (e.g., Achievement, Caring, Hedonism, Tradition), provides one widely adopted ontological backbone (Jahanbakhsh et al., 17 Sep 2025). Comprehensive approaches operationalize these values by assigning each social media post a high-dimensional vector of value expression scores, typically based on ordinal or continuous scales (0–6). These scores are derived by prompting LLMs such as GPT-4o to evaluate posts for the presence and intensity of each value, using value definitions and few-shot examples as context.

Broader pluralistic frameworks—such as Alexandria, which encompasses a library of 78 values drawn from multiple psychological and cultural systems (Rokeach, Maslow, Hofstede, and others)—demonstrate that many more than 19 value constructs are needed to capture the nuanced needs and priorities of a diverse user base (Kolluri et al., 16 May 2025). These libraries are deduplicated using correlation clustering and filtered to ensure that each value is identifiable at the level of an individual post.

Additional approaches have implemented models for recognizing value-expressive posts in specific linguistic or cultural contexts (e.g., using SVMs over Rubert-tiny2 embeddings to detect value-expressive content in Russian social media) (Milkova et al., 2023).

2. Algorithmic Feed Ranking and User Controls

Explicit value modeling enables algorithmic ranking mechanisms that integrate user- or community-articulated value preferences into feed construction. For a post $i$ represented by a value vector $v_i \in \mathbb{R}^d$ and a user-assigned weight vector $w \in [-1, 1]^d$, a linear ranking score is computed as:

$s_i = w \cdot v_i$

with posts ordered by $s_i$. This design allows fine-grained, multi-value trade-offs by letting users set positive or negative weights for each dimension via slider-based interfaces or survey-derived presets (Jahanbakhsh et al., 17 Sep 2025, Kolluri et al., 16 May 2025). Users’ active manipulation of weights shapes the composition of their feeds toward their specified value alignment, with empirical studies confirming that such feeds diverge substantially from engagement-optimized feeds both in rank correlation and user-recognizability.

Advanced architectures accommodate value-aligned ranking as a pipeline stage after engagement-based selection (e.g., operating as a client-side browser extension atop X/Twitter in Alexandria), demonstrating real-time, user-facing implementation feasibility within existing social media stacks.

Tables such as the one below summarize the core architecture:

Component	Description	Example Implementation
Value Modeling	LLM-based extraction of post value vectors	GPT-4o annotations per post
User Controls	Adjustable weights/sliders for value preferences	Alexandria Chrome extension
Feed Ranking	Linear dot product $s_i = w \cdot v_i$	Posts sorted client-side

Such modularity supports both single-value prioritization (high recognizability, low complexity) and multi-value trade-offs (higher expressive power, modest increase in cognitive load).

3. Measurement, Operationalization, and Empirical Evaluation

Operationalizing values at scale involves robust annotation pipelines, classifier calibration, and iterative model refinement. In Alexandria, the initial set of 145 values is reduced to 78 using correlation pruning, and LLMs are directly prompted with standardized value definitions, showing human-level agreement (e.g., binary accuracy ≈ 81%, mean absolute error ≈ 0.45 on a 0–2 scale) (Kolluri et al., 16 May 2025). Classifiers are evaluated via F1 and F1-macro scores, with active learning strategies enriching training sets with ambiguous or hard-to-classify examples, especially for linguistically complex corpora (Milkova et al., 2023).

Controlled experiments confirm that value-aligned feeds are preferred by users, with recognizability rates significantly above chance—76.1% for single-value and 63.4% for multi-value (Kendall’s τ ≈ 0.06 compared to engagement ranks) (Jahanbakhsh et al., 17 Sep 2025). Pluralistic value libraries also yield more precise, diverse user configurations: users with access to the full library select more values and fine-tune weights more than those with only a single system (Kolluri et al., 16 May 2025).

4. Social, Political, and Community-Ascribed Value Alignment

Extending individual value alignment, some frameworks operationalize values held by communities or broader democratic society—embedding “societal objective functions” into feed objectives. Embedding democratic values such as reduced partisan animosity is conducted by adapting validated social science constructs into post-level classifiers, which in turn inform re-ranking, downranking, or exclusion criteria (Jia et al., 2023). Technically, a post receives scores on pre-defined dimensions (e.g., “partisan animosity,” “support for undemocratic practices”) using LLMs or manual annotation, with the feed re-ordered accordingly. Empirical studies reveal that downranking posts high in anti-democratic attitude indices reduces partisan animosity by measurable effect sizes (d = 0.20–0.27) without negatively impacting user engagement.

Similarly, community-based or pluralistic feed architectures—in which users and communities can financially boost the prominence of content aligned with their values (parameterized by $\lambda$ in formulas such as

$$e(\tilde{z}; p_i) = \frac{\lambda(p_i) \psi(\tilde{z}; p_i) + \sum_{c \in C(p_i)} d(c; p_i) \lambda(c) \psi(\tilde{z}; c)}{\sum_{m \in M} [\lambda(p_i)\psi(m;p_i) + \sum_{c \in C(p_i)} d(c;p_i)\lambda(c)\psi(m;c)]}$$

)—incorporate not only individual preferences but also group-endorsed bridging or balancing priorities (Weyl et al., 15 Feb 2025).

Modeling value change dynamics also accounts for social influence. Bounded Confidence Models (BCMs) predict users' value score evolution as a function of peer influence, parameterized by convergence $\mu$ and confidence threshold $\sigma$, and optimized with methods such as particle swarm optimization and SVR to minimize predictive error (Mukta et al., 2021).

5. Transparency, Explainability, and Participatory Design

Value alignment frameworks increasingly incorporate transparency and participatory feedback. The FAIRY system constructs an interaction graph that traces actions and entities to reveal why specific content appears in a user’s feed, scoring paths according to relevance and surprisal using learning-to-rank models (Ghazimatin et al., 2019). Post-level explanations—such as surfacing the contribution of specific friends, categories, or actions—enhance user self-awareness and informed control, facilitating more deliberate feed re-alignment with personal or communal values.

Recent design explorations extend user agency via “teachable feed” paradigms that enable in-situ, multi-scale feedback on both structured (feature-level) and unstructured (natural language) signals, supporting aggregation into multiple curriculum feeds (Feng et al., 25 Jan 2024). Multiplicity of feeds, agential adjustment, and the explicit surfacing of value trade-offs counteract the opacity of traditional engagement-tuned algorithms.

Political ideology further shapes both implicit value alignment and system affordances. Co-design research illustrates systematic differences: right-leaning groups prefer market-based, popularity-driven visibility models, while left-leaning groups de-emphasize engagement metrics in favor of open, flexible, or justification-centric interaction models (Epp et al., 4 Nov 2024).

6. Systemic and Societal Outcomes: Polarization and Pluralism

Value alignment affects not only individual user experience but also system-level dynamics such as polarization, social sorting, and pluralism. Feed algorithms tuned solely for engagement may favor narrow, individualistic, or even polarizing content (Jahanbakhsh et al., 17 Sep 2025, Fraxanet et al., 20 Sep 2024). Empirical measurement frameworks capture ideological segregation, news quality divergence, and the feedback impact of algorithmic feed updates on collective behavior using time series, weighted averages, and segmentation regression (Fraxanet et al., 20 Sep 2024).

Value alignment interventions (e.g., downranking antidemocratic and polarizing content using LLM-based classifiers) can reduce affective polarization and negative emotion without significantly impacting engagement metrics (Piccardi et al., 22 Nov 2024). Computational re-weighting of link strengths in social networks, constrained by user preferences and disagreement indices, has been shown to reduce polarization and disagreement while preserving relevance (Cinus et al., 2023). Modeling the provenance and bridging/balancing character of content (e.g., in the Prosocial Media framework) supports a pluralistic platform design that explicitly aims to foster social cohesion and broad-based consensus (Weyl et al., 15 Feb 2025).

Decentralized protocols introduce variance in the loci of power over curation, identity, and moderation—each impacting value realization, transparency, and the potential for user-aligned pluralism (Oshinowo et al., 29 May 2025). Protocol-specific architectures (ActivityPub, AT Protocol, Nostr, Farcaster) distribute these authorities differently and thus instantiate specific trade-offs between autonomy, consistency, and the capacity for value alignment at scale.

7. Challenges, Equity, and Future Directions

Operationalizing value alignment presents open issues regarding annotation reliability, cross-cultural adaptability, cognitive complexity in multi-value interfaces, and trade-offs between usability and fine-grained control. Active learning, ensemble annotation (human-AI), and careful prompt engineering help but do not wholly resolve ambiguities inherent in subjective values (Milkova et al., 2023). Equity emerges as a critical consideration: frameworks that leverage subscription payments or weight boosting must provide subsidies and standing mechanisms to safeguard participation by underserved communities (Weyl et al., 15 Feb 2025).

As users’ values and preferences may evolve over time, dynamic adaptation mechanisms (for instance, adjusting recommendation algorithms based on predicted value change via BCM) can further support ongoing alignment (Mukta et al., 2021). Conversely, care must be taken to avoid amplifying filter bubbles: regular inclusion of non-similar content, Laplace smoothing, and explicit diversity objectives offer possible mitigations (South et al., 2022).

Finally, the governance of value libraries and alignment mechanisms, especially in open or federated contexts, remains a site of active research: models ranging from open-source curation to community-weighted voting may provide robust and context-sensitive solutions.

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In sum, research demonstrates that social media feeds can be value-aligned through the explicit modeling of values (individual, communal, societal), the design of value-sensitive ranking algorithms and user controls, transparency and explanation mechanisms, and the optimization of systemic trade-offs (e.g., between relevance and diversity). These approaches challenge engagement-centric orthodoxy and aim to engender feeds—and, by extension, social platforms—that are sensitive to, and reflective of, the articulated values of their participants.