Decoding Your Personality Through Music: Insights and an Innovative AI Experiment

Music is deeply personal, an art form that resonates with our moods, memories, and innermost feelings. It provides a safe space where we discover who we are, vent emotions, recharge, and sometimes find healing. Today, streaming services use advanced algorithms and machine learning models to anticipate our tastes and enhance our listening experience. Tomorrow, these same data-driven methods could evolve into tools that help us understand ourselves more deeply. What if, beyond recommending the next great artist, these platforms could derive insights into our personality, mental well-being, and even predict collective cultural shifts?

Spotify Wrapped: The Inspiration for the Whole Experiment

Spotify Wrapped is an annual feature released by Spotify that provides users with a personalized summary of their music listening habits over the past year. It has become a cultural phenomenon since its inception in 2016, eagerly anticipated by millions of users worldwide.

Key Features of Spotify Wrapped 2024

• Your Music Evolution: Explore how your musical tastes have changed throughout the year, identifying up to three distinct musical phases that characterize your listening experience.
• Longest Listening Streak: View your longest uninterrupted listening sessions for your favourite artists, highlighting the depth of your engagement.
• Top Listeners: See where you rank among the top listeners of your favorite artists, fostering a sense of community and friendly competition.
• Spotify Wrapped AI Podcast: Enjoy a personalised podcast generated by AI in collaboration with Notebook LM, narrating your musical journey with insightful commentary.
• Top Audiobooks: Discover the most popular audiobooks on Spotify alongside your music trends, showcasing broader consumption patterns.

Spotify Wrapped not only reflects individual listening habits but also captures broader trends across the platform, celebrating both artists and fans alike. It serves as a playful yet insightful mirror of our evolving musical preferences.

How Platforms Are Using Music Listening Data

Beyond the yearly spectacle of Wrapped, music streaming platforms engage in an ongoing, ever-evolving process of analysing listening data. This process goes far beyond counting plays and top artists. Modern services rely on machine learning models and neural networks that break down each track into a tapestry of attributes—tempo, rhythm, mood, lyrical content, instrumentation—and then correlate these with user behaviours.

Platforms like Spotify, Apple Music, and YouTube Music are essentially mapping our emotional landscapes one track at a time. They use large-scale data analysis to:

• Develop hyper-personalised recommendation systems that understand musical preferences with remarkable nuance, almost like an intuitive musical companion anticipating your emotional state.
• Generate predictive models about emerging musical trends, revealing how collective listening behaviours signal broader cultural shifts and aesthetic transformations.
• Create adaptive learning models that can recognise micro-variations in individual taste, understanding that musical preference is a dynamic, evolving form of personal expression.

This interplay of human expression and machine intelligence takes music consumption from a one-dimensional transaction (“I press play, I hear a song”) to a richly layered dialogue. It’s a space where a platform doesn’t just serve content—it interprets it, learns from it, and uses it to refine your listening experience.

A Futuristic View: What Else Could They Do?

As advanced as these systems are today, we’re only scratching the surface of what might be possible. Imagine stepping into a world where music streaming data transcends recommendation engines, becoming a kind of emotional and psychological support system. A few forward-looking possibilities include:

• Psychological Profiling and Emotional Insight: Your unique pattern of listening—when you turn to melancholic ballads, how often you seek upbeat tracks to lift your mood—could hint at your emotional baseline. Over time, these subtle cues might help identify shifts in your mental state, signaling stress, happiness, or changes in personal priorities.
• Mental Health Anomaly Detection: By establishing a baseline of your “normal” listening behaviors, algorithms could detect deviations that correlate with signs of anxiety, depression, or unrest. For instance, if someone who usually listens to a balance of genres suddenly becomes fixated on dark, slow-tempo tracks late at night, it might indicate emotional distress. In the future, these signals could prompt supportive interventions, from gentle playlist suggestions designed to help recalibrate mood to discreet nudges toward seeking professional guidance.
• Neurological Resonance Mapping: Music resonates in our minds at neural levels, influencing cognition, memory, and perception. Future systems might decode the nuances of this resonance. By analysing micro-patterns—preferences for intricate rhythms, certain lyrical themes, or frequencies—algorithms could glean insights into cognitive styles, problem-solving approaches, and even intellectual curiosities.
• Collective Emotional Epidemiology: On a societal scale, aggregated listening patterns could serve as a form of cultural MRI, revealing how entire communities feel during periods of economic stress, social change, or collective celebration. Such macro-level insights could inform leaders, policymakers, and cultural institutions, helping them understand communal sentiments and respond with sensitivity.

Ethical Considerations

All these possibilities must be approached with responsibility. The data is intimate—it reflects emotional states, personal tastes, and even vulnerabilities. Any large-scale analysis of such data should respect privacy and strive for empowerment rather than exploitation. It’s crucial to ensure that technological innovation enhances rather than diminishes human agency, maintaining ethical standards in the handling and interpretation of personal data.

Personality Computing with Music Listening 

This brings us to a concept known as “Personality Computing with Music Listening.” It’s a field at the intersection of psychology, computer science, and musicology, where researchers attempt to map personality traits onto music preferences and listening behaviours.

Personality is often described using the Big Five framework—Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Studies have found intriguing correlations:

• Openness: Individuals high in Openness tend to explore a variety of genres, often enjoying more complex and unconventional music forms such as classical, jazz, or global folk traditions.
• Conscientiousness: Those who lean towards structure and order in life might prefer more predictable and consistent musical styles. They may avoid intense, chaotic genres and gravitate toward cleaner, more structured sounds.
• Extraversion: Outgoing, socially engaged personalities often appreciate upbeat, energetic music that aligns with a lively lifestyle—electronic dance music, pop anthems, or anything that thrives in a group setting.
• Agreeableness: Warm, empathetic listeners might favour gentler, more harmonious tunes, choosing music that fosters connection and emotional resonance.
• Neuroticism: Individuals who experience higher emotional volatility or anxiety may display patterns in their listening—frequent playlist changes, shifting moods in song selections—that reflect inner turbulence, often gravitating towards introspective, sometimes melancholic tracks.

These correlations aren’t perfect. Music preference isn’t a definitive psychological test, and people’s tastes are shaped by countless factors—cultural background, personal history, current circumstances, and novelty-seeking. Still, this evolving field is opening doors to non-invasive, behavior-based personality inference. Instead of filling out long questionnaires, imagine gleaning personality insights from the soundtrack of your life.

More about in this comprehensive study Personality Computing With Naturalistic Music Listening Behavior: Comparing Audio and Lyrics Preferences

The Experiment Based on All of the Above (it’s cool)

How might this play out in practice? Consider a personal experiment:

1. Download Your Personal AI Podcast in Spotify Wrapped: Access and download your personalized AI-generated podcast from Spotify Wrapped, which narrates your musical journey over the past year.
2. Get a Transcript of It: Obtain a transcript of the podcast to have a text-based format of the podcast
3. Run the Prompt Below Through ChatGPT / Claude or Gemini:
4. Run a Separate Test (100 Questions) to See How the Result Matches Big Five Test: Complete a standard Big Five personality test and compare the results with the profile generated from your music listening data. Analyze where they coincide and where they diverge to understand the strengths and limitations of personality computing based on music habits.

Act as a Personality computing with music listening behavior algorithm. Analyze the following text data summarizing my annual music listening habits, including external factors like lyrics, mood tags, and genres, to derive insights into my personality traits using the Big Five framework and the principles of personality computing with music listening behavior.

Deliverables:

Personality Profile: Provide a detailed personality profile based on the Big Five traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism).

Contextual Explanations: Offer data-driven explanations for how each insight was derived, explicitly linking specific music attributes (e.g., genres, lyrics, mood tags) to each personality trait, while minimizing hallucination risks.

Personality Insights: Explain the implications of the results from a personality trait perspective, focusing on what they reveal about tendencies, behaviors, and preferences.

Process:

Use a step-by-step, transparent reasoning approach.

Incorporate Chain-of-Thought reasoning to ensure logical consistency.

Present results as a structured and professional analysis, reflecting both the sensitivity and importance of the topic.

Text Data: 

[Insert your NotebookLM Transcribe here]

6. Final Words

As we stand on the threshold of these possibilities, it’s worth pausing to consider what music truly means to us. It’s more than background noise or a commodity—it’s a language that communicates emotion, identity, and imagination. The fusion of data-driven analysis and human creativity offers us not just a deeper understanding of our tastes, but a richer sense of self-knowledge and personal potential.

Music was my first love and it will be my last. Music of the future and music of the past. To live without my music would be impossible to do. In this world of troubles, my music pulls me through.

In these lines, we find both comfort and direction. Music, eternal and evolving, continues to guide us through life’s complexities. Now, with the evolving capabilities of personality computing, it may also help us understand those complexities in ways we could scarcely have imagined before. The soundscape of the future is as much about who we are as it is about what we listen to—and that is a future worth exploring.