Social Context Features

• Social context features are structured, dynamic, and multi-modal attributes that capture interactions between agents and environments through practices, roles, and relationships.
• They are applied in fields like human-computer interaction, recommender systems, and social sensing by leveraging theoretical models such as social practice theory and graph-based representations.
• Extraction and fusion techniques, including vector concatenation and graph neural networks, improve model accuracy and support privacy-preserving, interpretable system designs.

Social context features encompass the structured, dynamic, and often multi-modal attributes that define, mediate, or constrain the interactions between agents (human or artificial) and their environments, as well as with each other. In contemporary computational settings, social context features serve as central elements for understanding phenomena in human–computer interaction (HCI), social sensing, recommender systems, and a wide span of AI-driven environments. Unlike generic contextual information (e.g., pure location or time), social context features specifically encode the entanglement of activities, roles, relationships, histories of interaction, and normative expectations as these unfold in real world or digitally mediated settings (Uhde et al., 2021, Thompson et al., 19 Aug 2025, Génois et al., 2022, Campana et al., 2022, Shu et al., 2017).

1. Theoretical Foundations and Formalism

Three principal frameworks dominate the explicit formalization of social context features:

1. Social Practice Theory: Social context is modeled as a temporarily co-located bundle of social practices, each represented as a triad $P = (M, C, N)$:
   • $M$ (Material): physical artifacts and technological affordances (e.g., devices, form factors)
   • $C$ (Competence): skills, routines, cognitive abilities, habits required for or shaped by the practice
   • $N$ (Meaning): social-symbolic, normative, or emotional significance attached to the practice The social context in a setting is an indexed set $\{P_1, \ldots, P_k\}$; compatibility and, critically, social acceptability, emerge from the interplay—overlap or friction—between respective $M, C, N$ components of these practices (Uhde et al., 2021).
2. Attribute Graph/Entity–Environment Models: In human–robot interaction (HRI) and related domains, social context is the set of attributes attached to relevant agents, environments, and their associations. A context is depicted as a graph—nodes for agents and environments; edges for associations (spatial, social, hierarchical). Each node and edge is enriched with features: actions, roles, demographic details, cognitive and affective states, behavior constraints, and so forth (Thompson et al., 19 Aug 2025).
3. Interaction Networks and Data-Driven Ego Networks: In mobile and ubiquitous computing, social context is captured through ego networks derived from sensing (physical proximity, digital exchange, OSN interactions), where edge weights encode tie strength as a function of interaction frequency, modality (physical/cyber), and duration. Layering schemes segment alters into concentric groups ordered by tie strength (Campana et al., 2022).

2. Taxonomy and Extraction of Social Context Features

A broad range of social context features have been operationalized across domains:

Category	Example Features / Mathematical Representation	Source References
Social-Practice Triads	Material (M), Competence (C), Meaning (N): $P = (M, C, N)$	(Uhde et al., 2021)
Agent Attributes	Role, action, physical state, demographic profile, mental/affective state	(Thompson et al., 19 Aug 2025, Génois et al., 2022)
Association Attributes	Physical distance, relationship type (friendship, authority), team membership	(Thompson et al., 19 Aug 2025, Génois et al., 2022)
Environmental Attributes	Location type, environmental affordances, social/behavioral norms	(Thompson et al., 19 Aug 2025, Maddali et al., 2023)
Relational Ties	Tie strength $$\omega_s(e,a) = \lambda \omega_v(e,a) + (1-\lambda)\omega_p(e,a)$$	(Campana et al., 2022, Sarker, 2018)
Propagation/Interaction	User–item shares, retweets/replies graph, triadic news–user–publisher links	(Shu et al., 2017, Saikia et al., 2022, Nguyen et al., 2020)
Group/Co-Presence	Inferred group size, dyadic/group flag, sex composition, Bluetooth proximity stats	(Mader et al., 2023, Meegahapola et al., 2021, Reddy et al., 2024)
Socio-Demographic Context	Age, gender, professional status, Big Five, motivations, crowd composition	(Génois et al., 2022)
Behavioral Cues	App usage, screen activity, touch events, sensor on/off patterns	(Mader et al., 2023, Meegahapola et al., 2021)

Extraction and aggregation vary by domain: structured surveys (demographic and psychological traits), sensor and device logs (Bluetooth/Wi-Fi proximity, smartphone usage), network traces (social graphs, interaction history), event- or window-centric feature engineering, and manual or automated annotation of practice types.

3. Computational Representation and Model Integration

Social context features are encoded and injected into computational models using strategies tailored to the task and data modality:

• Vector Concatenation and Fusion: Features are standardized (e.g., one-hot encoding, z-score normalization), vectorized, and concatenated to form multimodal input vectors. Fusion occurs at input (feature-level), mid (embedding-level), or output (ensemble-level) stages (Truică et al., 2023, Peters et al., 2023).
• Graph-Based Models: In networked domains, features populate nodes and edges in a heterogeneous graph or attributed network. Graph Neural Networks (GCN, GraphSAGE, GAT) propagate and aggregate these social-context features to generate high-level embeddings for classification or regression tasks (Nguyen et al., 2020, Shu et al., 2017, Saikia et al., 2022).
• Practice-Network Diagrams: Social-practice models utilize diagrams where each practice’s $(M, C, N)$ are explicitly visualized or indexed as nodes and edges, with incompatibilities or overlaps computed via set operations or custom compatibility functions (Uhde et al., 2021).
• Ego-Network Layering: Features derived from tie-strength matrices are subjected to hierarchical clustering or distance-based layering (Dunbar circles), generating context vectors that summarize peer interaction configuration at each temporal window (Campana et al., 2022).

4. Domains of Application and Empirical Impact

Social context features underpin critical advances in several research fields:

• Human–Computer Interaction and Acceptability: Modeling acceptability as a function of practice compatibility has enabled more nuanced intervention design—targeting M, C, or N for improved user experience rather than suppressing interaction globally (Uhde et al., 2021).
• Fake News and Content Moderation: Joint modeling of social context (user/user/follower graphs, triadic publisher–news–user relations, stance networks) and text content consistently yields significant boosts in classification F1, AUC, and early detection performance, outperforming content-only models by 2–12 percentage points (Shu et al., 2017, Saikia et al., 2022, Nguyen et al., 2020, Dar et al., 2024).
• Recommender Systems: Implicit social-context signals (social graph, action networks, demographic/temporal context) fundamentally increase the MAP of multifaceted factorization models and lead to state-of-the-art ensemble predictors (Chen et al., 2021).
• Social Sensing and Context Inference: In mobile and ubiquitous computing, sensor-derived features (physical proximity, app usage, activity recognition, environmental context) achieve AUCs above 0.90 for personalized models in inferring co-presence or group composition, and provide essential context for privacy-preserving on-device inference (Mader et al., 2023, Campana et al., 2022, Meegahapola et al., 2021, Reddy et al., 2024).
• XR and Virtual Collaboration: Social context features contextualize physical reconstructions, enabling meaningful embodied experience (memory, history, motivation, privacy affordances) beyond raw geometric fidelity (Maddali et al., 2023).

5. Design, Privacy, and Interpretability Implications

Active research into social context features has exposed key implications for system design and use:

• Targeted Remediation: By diagnosing the source of practice incompatibility (M, C, N), system designers or intervention modules can select the most effective lever for increasing acceptability or compatibility (Uhde et al., 2021).
• User Involvement and Co-Creation: Especially in XR and participatory settings, enabling users to define meaningful zones, annotate with private/public, and signal salient histories or emotional ties ensures that reconstructions and inferences accord with lived social reality (Maddali et al., 2023).
• Privacy-Preserving Architectures: On-device modeling of ego-networks and context inference, encrypted identifier exchange, and deletion of raw sensor or audio data are essential for minimally invasive, privacy-protective use of social context signals (Campana et al., 2022, Reddy et al., 2024).
• Model Efficiency and Generalization: Augmenting behavioral or content models with contextual features (connectivity, location, socio-demographic, device state) allows for accurate short-horizon prediction, dramatically reducing the need for long histories and supporting efficient, interpretable models (Peters et al., 2023).
• Interpretability and Diagnosability: Structural holes, conflict or overlap matrices, and ablation studies of social context features provide direct insight into design weaknesses, unexpected incompatibilities, and emergent phenomena in digitally mediated environments (Uhde et al., 2021, Dar et al., 2024, Thompson et al., 19 Aug 2025).

6. Limitations, Open Challenges, and Future Directions

Several methodological and substantive constraints limit current treatment of social context features:

• Formality and Scalability: While taxonomies and graph-based representations have been formalized, a universally adopted calculus for practice interaction compatibility or higher-order context graphs remains undeveloped (Uhde et al., 2021, Thompson et al., 19 Aug 2025).
• Data and Generalization: Social context inference degrades outside the training environment—cross-country, cross-cultural, and real-to-virtual transfer generalization remain open problems (Mader et al., 2023, Meegahapola et al., 2021, Reddy et al., 2024).
• Unobservable and Emergent States: Inferring latent agent states (trust, motivation, hidden group dynamics) from low-level features is an unsolved challenge requiring more multimodal, interactive, and causally structured models (Thompson et al., 19 Aug 2025).
• Privacy/Ethical Considerations: Accurate social-context modeling must balance granularity and user control with data minimization practices (Campana et al., 2022, Meegahapola et al., 2021).

A persistent agenda is to formalize compatibility and transformation across multiple, simultaneous social practices, to embed robust context-awareness in AI systems across modalities and environments, and to ensure that personalization and privacy co-exist as first-class principles in context-aware technologies.