
Features & Interaction Model

Probable
02
Slowing emotional contagion through logic-driven interface design
#SpeculativeDesign
#UXDesign
#NeuroDesign
#Misinformation
#AI
A speculative interface designed to delay emotional reactions to fake news. By integrating real-time coherence scoring, sentiment dampening filters, and logic-based interaction locks, this project challenges the viral logic of misinformation and reimagines how truth might be earned in digital spaces.
Problem:
Emotionally charged fake news bypasses cognitive reasoning, triggering impulsive social reactions (e.g., riots, conspiracy propagation).
Behavioral Insight:
Based on neuroscience research, the amygdala responds before the prefrontal cortex can evaluate. Current news interfaces amplify this imbalance.
Design Opportunity:
Build a speculative tool that delays emotional impact, invites logic, and reprograms digital trust.
Speculative Premise
What if we could delay the emotional response triggered by misleading news by embedding logic-based doubt directly into the interface?
Emotional intensity triggers UI dampening
Logical coherence is rewarded with visibility
Belief is no longer automatic — it’s earned through structure
Tools :
Framer – UI design, layout, and prototyping
Illustrator / Photoshop – Image editing
Prototype
Features & Interaction Model

















Social Count
Unfinished
03
An emotional analytics tool that decodes your digital conversations to reveal the hidden patterns shaping your closest relationships.
#SpeculativeDesign
#UXDesign
#sustainableDesign
#Relationship
#AI
Social Count is a speculative relational analytics tool that mirrors private chat data (e.g., WhatsApp) to visualize the emotional climate and interaction dynamics between two individuals over time. It does not predict feelings but interprets patterns in tone, reciprocity, and emotional behavior—encouraging reflection over confrontation.
2. Core Vision
Enable individuals to monitor and understand the emotional trajectory of their relationships.
Identify warmth, silence, overload, distance, or dependency loops through subtle clues in chat patterns.
Empower users to spot early signs of imbalance or toxicity before they escalate.
3. Visual Language
Use blended color gradients for mixed messages (e.g., red-yellow for passive-aggressive tones).
Silence indicators: Gray bars between active conversation clusters.
Topic shifts: Icon markers when discussion moves to external subjects (third person, events).
Mirroring Visualization: Two colored bands (User A and B) shifting over time—overlaps and gaps are interpreted.
4. Tools :
Figma – UI design, layout, and prototyping
Dale-e – Image Generation
5. Ethical Focus
Designed not for surveillance, but for self-awareness.
Gives users full control over their data, with local encryption.
Avoids scoring personalities—instead reflects interaction quality.
Warns of emotional projection or misreading, allowing interpretation to remain open-ended.
Features & Interaction Model
1. Sentiment Decoding
Messages are analyzed into emotional tones: positive, neutral, negative.
2. Interaction Timeline
Color-coded emotional timeline showing relationship pulses over time.
3. Symmetry Analysis
Tracks mutual effort: who initiates, who sustains, who withdraws.
4. Meta-conversation Flags
Identifies indirect communication patterns like avoidance, third-party projection, or emotional redirection.
5. Relational Health Score
A cumulative index updated weekly based on sentiment balance, response time, and topical focus.
6. Use Cases
Detecting a decline in emotional support from a close friend or partner.
Understanding the difference between external stress reflection vs. actual relational neglect.
Observing one’s own compulsive or avoidant behavior over time.
Highlight the available ingredients, fresh items, about to expire soon, out of stock ingredients.