1.1 Related works

2.1 Data collection and preprocessing

The data for this study was obtained from the Reddit Pushshift archive, specifically the repository available at https://the-eye.eu/redarcs/ [44]. The posts and comments data for subreddit r/ADHD were downloaded in ZST format and subsequently converted into CSV format for the analysis. The raw dataset consisted of 6,42,683 posts and 54,16,531 comments spanning the years 2011–2022. Metadata included timestamps, post/comment bodies, author usernames (anonymized for analysis), upvote counts, and submission titles where applicable. All posts and comments analyzed were in English. As Reddit does not provide user-level geographic information, we assume that most contributors are from English-speaking countries, consistent with the language and primary user base of the platform.

The collection and analysis of this data were conducted in compliance with Reddit’s terms of service and API usage policies at the time of retrieval. Since the dataset consists of publicly available posts and comments with no private or deleted content included, this study adheres to ethical guidelines for research using publicly accessible online data. User identifiers were anonymized during processing to protect privacy, and no attempts were made to link data to individual identities. Additionally, the Pushshift archive operates under fair-use principles for academic research, as outlined in its documentation.

The dataset underwent several rigorous cleaning and preprocessing steps to prepare it for analysis. First, post titles which had been removed or deleted were discarded, and any special characters or emoji present in titles were removed to ensure text consistency. Since the focus of this study is on user data, the post titles which contained external links were excluded and only user posts were retained. Next, posts with fewer than 5 comments were removed to maintain a threshold of engagement. As a final filtering step, only posts containing “music”, “Song”, or “songs” were selected, yielding 1,943 music-related posts. These posts were then merged with corresponding comments data. The comments were cleaned in a similar fashion to the posts by discarding removed or deleted comments and removing special characters and emojis from comments. Duplicate comments and posts were also removed to retain unique posts and comments. For the analysis, only the necessary variables were retained, which included the following: post ID, comment ID, post upvote score, comment upvote score, post title, post content, comment body, number of comments, postdate, and comment date.

2.2 Categorization of posts using Gemini 1.5 pro

A manual content analysis was conducted on a representative sample of r/ADHD posts to guide the development of coding categories for subsequent analysis. Leveraging the advanced natural language understanding capabilities of large language model Gemini 1.5 Pro [45], which demonstrates remarkable contextual comprehension and interpretation of text, a structured categorization approach was developed. Similarly, computational approaches are being used to understand ADHD through the analysis of various data types [46]. The categorization framework distinguished between: (1) focus music – titles indicating music used for concentration and productivity, (2) stuck songs – titles describing repetitive or intrusive musical experiences, and (3) general purpose – titles not falling into the above two primary musical experience categories.

Based on the initial analysis, a prompt for Gemini 1.5 Pro was crafted to systematically categorize these posts. An iterative prompt refinement process was employed, with each iteration involving manual checking of the categories and post titles. This approach allowed for progressive improvement of the categorization methodology, ensuring a more accurate classification of the musical experiences described in the r/ADHD community. The prompt explicitly instructed the model to objectively categorize each title based on the predefined labels, taking into account the subtle linguistic cues that might indicate different types of musical interaction in ADHD.

2.3 Song Information Extraction (SIE)

To extract song and artist names from user comments, LLAMA 3.1 [47] was employed, outperforming traditional Named Entity Recognition (NER) methods such as SpaCy, NLTK, and Flair Python libraries. These traditional methods struggled to handle the informal language and subtle ways in which users referred to music in comments. LLAMA 3.1, with its advanced contextual understanding and ability to process complex user-generated content, was better suited for extracting music-related entities with greater accuracy and flexibility. This highlights the increasing capability of advanced AI models in handling complex, real-world data, a trend observed in the broader development of artificial general intelligence [48].

Traditional NER methods often face challenges in user-generated contexts due to slang, abbreviations, and varied phrasing. In contrast, LLAMA 3.1 leverages large-scale pre-trained models capable of understanding such intricacies [49]. The GROQ API [50] was used to access the LLAMA3-8b-8192 model for this task. The model was prompted to identify and extract both song and artist names, even when multiple entities appeared within the same comment. For comments where no entities were found, the model returned “none.”

To ensure compliance with GROQ’s token limits, a rate limiter, and a batch size of 30 comments were implemented during the extraction process. The output was formatted in JSON, containing the original comment, song name, and artist name. This structured output was then seamlessly integrated into the dataset for further analysis. Using this approach, Song Information Extraction (SIE) successfully identified 9,261 tracks, significantly enhancing the dataset’s utility for subsequent analyses.

2.4 Song features extraction

Using track information (song and artist names), the Spotify API [51] was employed to extract detailed audio features for each track, providing a rich dataset for analysis. These features included the duration of the track in milliseconds, a binary variable indicating whether the track contains explicit language, and valence, which measures the musical positiveness conveyed by the track, with higher values representing more positive or happy tunes. Additional features extracted were tempo, measured in beats per minute (BPM), instrumentalness, indicating the likelihood that a track is instrumental, and popularity based on user engagement and streaming activity. The dataset also included features such as danceability, which assesses the track’s suitability for dancing by considering rhythm stability and beat strength, key, representing the musical key, and mode, which specifies whether the track is in a major or minor key. other audio characteristics included speechiness, reflecting the presence of spoken words; loudness, the average volume in decibels; acousticness, the degree to which the track sounds acoustic; liveness, indicating the presence of an audience; and genres associated with the artist. These audio features provide valuable insights into the musical and emotional characteristics of the tracks, enabling an in-depth analysis of their traits. Audio features for 9,215 tracks were successfully extracted using the Spotify API.

In addition to the audio features, lyrics were obtained using the Genius API [52] and subjected to a rigorous cleaning process to ensure the data was suitable for analysis. This process began by removing unwanted phrases at the beginning of the lyrics, such as contributor annotations like “1 Contributor” or “11 Contributors,” and instances of the word “lyrics” paired with the track name. Text in square brackets, often used for annotations such as “[Chorus]” or “[Verse],” was removed both at the beginning and throughout the lyrics. Promotional phrases, such as “See...Live” or “You might also like,” were filtered out, along with trailing patterns like “embed” found at the end of some lyrics. To improve readability, special characters were stripped, extra spaces were eliminated, and the text was reformatted. Finally, duplicate entries were identified and removed based on the combination of song and artist names, ensuring that each track’s lyrics were uniquely represented in the dataset. This comprehensive cleaning process resulted in a structured and high-quality lyrical dataset. Using the Genius API, lyrics for 4,423 tracks were successfully obtained.

2.5 Statistical analysis

To explore the lyrical and musical characteristics of each track, several statistical analyses were conducted. Two key textual features were derived from the lyrics: lexical diversity and repeatability. Lexical diversity was quantified using the Type-Token Ratio (TTR), a widely used metric in text analysis [53]. It is calculated as:

This metric evaluates the variety of unique words in relation to the total word count, providing a measure of linguistic richness. Repeatability, a complementary feature, was calculated as 1 − TTR, representing the extent of word repetition in the lyrics. These features offer insight into the lyrical complexity and redundancy of the tracks.

In addition to lexical features, sentiment analysis of the lyrics was performed using the syuzhet library in R [54]. The lyrics were tokenized, and sentiment scores were assigned to individual words based on the Bing sentiment lexicon, which is a reliable resource for word-level sentiment classification. The overall sentiment of each track’s lyrics was computed by summing the sentiment scores of all tokens. The sentiment scores were normalized to fall within a range of −1–1, where −1 indicates the most negative sentiment, and 1 indicates the most positive sentiment.

To evaluate differences in audio features across topic groups, Analysis of Variance (ANOVA) was initially considered [55]. However, after examining the data distributions (Fig 2), it was observed that several variables did not meet ANOVA’s assumptions of normality and homogeneity of variance. Therefore, the non-parametric Kruskal-Wallis test was applied, which does not require these assumptions and is more appropriate for the data characteristics. The analysis was conducted using the ggstatsplot library in R [56], enabling both statistical computation and comprehensive visualizations. Where significant differences were identified, Dunn’s post-hoc test with Holm’s correction method was used to control family-wise error rates and pinpoint specific group differences. Effect sizes were calculated as epsilon-squared (ε²ordinal) values, with interpretations based on Cohen’s guidelines [57], where values around 0.01 indicate small effects, 0.06 medium effects, and 0.14 large effects.

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Fig 2. Analysis of audio and lyrical features across three music categories in r/ADHD.

https://doi.org/10.1371/journal.pone.0324369.g002

Finally, Pearson correlation analysis was used to examine the relationships between audio features, lyrical features (lexical diversity and repeatability), and sentiment across different topic groups. Following Cohen’s guidelines [57], correlations are interpreted consistently as small (r = 0.1), medium (r = 0.3), and large (r = 0.5).

3.1 Categories in r/ADHD

As shown in Table 1, the categorization revealed distinct patterns of musical experiences among individuals with ADHD. The focus music category, with 208 posts, illustrated how individuals use music as a strategy for concentration and task management. The stuck songs category, comprising 224 posts, captured the phenomenon of repetitive musical thoughts frequently reported in ADHD. The largest category, general purpose, with 503 posts, demonstrated the broad and varied musical discussions within the ADHD community.

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Table 1. Post distribution and representative titles across music-related discussion categories.

https://doi.org/10.1371/journal.pone.0324369.t001

3.2 ANOVA and comparative analysis

Fig 2 presents a detailed comparison of nine song features across the general purpose, focus music, and stuck songs topics, utilizing violin plots overlaid with box plots to demonstrate both distribution patterns and central tendencies. Kruskal-Wallis tests revealed significant differences across all features (Table 2).

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Table 2. Kruskal-Wallis test results for musical and Lyrical Features.

https://doi.org/10.1371/journal.pone.0324369.t002

By examining the shape and spread of the violin plots, we can observe several key differences in the distribution of variables among the topics. The focus music category tends to have a more distinct profile, with higher concentrations around certain musical attributes. For example, the focus music category exhibits a more concentrated distribution of positive valence, lower loudness, and greater instrumentalness compared to the general purpose and stuck songs categories. In contrast, the stuck songs category stands out with higher concentrations of energy, acousticness, and danceability. Looking at the median values further reinforces these observations. The stuck songs have the highest median energy, acousticness, and danceability, suggesting they tend to be the most energetic, acoustically driven, and danceable. The focus music, on the other hand, has the highest median valence and instrumentalness, indicating it contains the most positively valanced and instrument-focused music. The general purpose category generally falls between the focus music and stuck songs in terms of the median values, exhibiting a more balanced profile across the various features. However, it also shows wider distributions, suggesting more variability in the musical and lyrical characteristics within this category.

The comparison of song features across the three topics revealed statistically significant differences, as shown in Table 2. All examined characteristics displayed statistical significance, with varying degrees of strength. The analysis focused on effect sizes, measured by ε²ordinal, which represents the proportion of variance in the dependent variable accounted for by the independent variable. As illustrated in Fig 2, Instrumentalness showed the highest effect size among the analyzed features (ε²ordinal = .04, p < .001), though still within the small-to-medium range. This indicates that Instrumentalness exhibits the most pronounced variation across the three topics. Valence followed with an effect size of ε²ordinal = .03 (p < .001), suggesting differences in the emotional qualities of the music between topics. Danceability and Loudness both exhibited effect sizes of ε²ordinal = .02 (p < .001), indicating differences across topics. Energy and Acousticness demonstrated smaller effect sizes (ε²ordinal = 6.72e-03 and 3.18e-03 respectively, p < .001), while still contributing to the differentiation between topics.

The remaining features showed the smallest effect sizes: Lexical Diversity (ε²ordinal = 3.91e-03, p < .001), Lyrics Sentiment (ε²ordinal = 2.38e-03, p < .01), and Tempo (ε²ordinal = 6.94e-04, p = .04). The confidence intervals for all effect sizes ranged from their specific lower bounds to 1.00, indicating some variability in the estimates.

3.3 Correlation analysis

Fig 3 presents a pair plot that visualizes the correlations between various audio features across the three categories: general purpose music, focus music, and stuck songs. The pair plot provides a comprehensive overview of the bivariate relationships between the features, allowing for a deeper understanding of the underlying patterns.

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Fig 3. Pairwise correlations and distributions of audio and lyrical features across music categories.

https://doi.org/10.1371/journal.pone.0324369.g003

The correlation analysis reveals distinct patterns of musical attributes across ADHD-related contexts. Across all categories, two robust relationships emerge: a large positive correlation between energy and loudness (r ≈ 0.74 in all categories) and a medium-to-large correlation between danceability and valence (ranging from r = 0.41 in general purpose to r = 0.56 in focus music).

Beyond these similarities, the categories exhibit notable differences. Focus music displays a unique small positive correlation between energy and danceability (r = 0.12), contrasting with the absence of correlation in both general purpose and stuck songs categories (both r = −0.05). This suggests that energetic music becomes more danceable specifically in focus contexts.

The relationship between valence and instrumentalness also varies across categories. Although all correlations fall within the small effect size range, focus music shows the strongest negative correlation (r = −0.28), followed by general purpose (r = −0.21) and stuck songs (r = −0.16). This indicates that instrumental pieces in focus contexts tend to have less positive emotional valence.

Speechiness correlations further differentiate the categories. Focus music exhibits the strongest relationship between energy and speechiness (r = 0.23), compared to general purpose (r = 0.11) and stuck songs (r = 0.09), suggesting that vocal elements contribute more to perceived energy in focus contexts.

Similarly, tempo relationships show category-specific patterns, with focus music demonstrating the strongest correlation between energy and tempo (r = 0.24), compared to the overall sample (r = 0.18). This suggests faster tempos may be more integral to energy perception in focus music.

Lexical diversity and lyric sentiment correlations, while small in magnitude, reveal additional distinctions. Stuck songs show the strongest relationship between lexical diversity and energy (r = −0.14), while focus music exhibits a unique positive correlation between acousticness and lyrics sentiment (r = 0.19). These subtle differences highlight how linguistic features interact differently with musical elements across listening contexts.

3.4 Sentiment and popularity of lyrics across topics

Fig 4 presents a scatter plot comparing lyrics sentiment and popularity across three topics: focus music, general purpose, and stuck songs. The plot visualizes the top 50 songs per topic based on upvote scores, with lyrics sentiment on the x-axis and popularity on the y-axis. A horizontal line at 50 popularity and a vertical line at 0 sentiment divide the plot into quadrants, facilitating comparison across topics and sentiment-popularity relationships.

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Fig 4. Lyrics sentiment vs song popularity: Top 50 most upvoted songs per music category.

https://doi.org/10.1371/journal.pone.0324369.g004

The distribution of songs across the plot reveals distinct patterns for each topic. focus music (red) tends to cluster more towards positive sentiment, with varying popularity levels, hinting at a preference for more upbeat or neutral lyrical content in music used for concentration. general purpose (blue) shows a wider spread, with songs distributed across all quadrants, indicating a diverse range of sentiment and popularity combinations. stuck songs (green) display a slight bias towards positive sentiment, with many highly popular tracks, suggesting that songs that become “earworms” are often both well-known and emotionally positive or neutral [38]. The distribution of stuck songs (green) in the figure reveals that many of these highly popular “earworm” tracks have received considerable upvote scores compared to the general purpose two topics.

Quadrant analysis provides further insights. The upper right quadrant (positive sentiment, high popularity) contains many stuck songs, while the upper left (negative sentiment, high popularity) features several general purpose songs. This distribution suggests that popular “earworms” often have positive lyrical content, while some popular music discussed in other contexts may have more negative or complex emotional themes. The lower quadrants show a mix of songs from all categories, with general purpose having a notable presence in the lower right (positive sentiment, lower popularity).

Notable outliers include “As It Was” by Harry Styles in the stuck songs category, standing out as a highly popular song with positive sentiment. These outliers highlight the unique characteristics of certain songs that resonate strongly within specific contexts.

4.1 Implications and future research

The findings of this study underscore the intricate interplay between music, cognition, and emotion in individuals with ADHD, presenting several compelling implications. The preference for focus music, characterized by higher instrumentalness and positive valence, aligns with several theories. These frameworks suggest that music serves as a scaffolding tool, helping individuals manage attention and regulate emotional states. For individuals with ADHD, this strategic use of music may act as a compensatory mechanism for executive function deficits, including challenges in sustaining attention and managing impulsivity. Extending this analysis, neurocognitive studies should focus on how ADHD is associated with unique patterns of auditory processing, which may amplify music’s effectiveness in modulating neural activity tied to focus and emotional regulation. This could refine our understanding of how music interacts with ADHD-specific neural pathways.

These findings have significant interdisciplinary implications as well, particularly for music therapy, education, and technological interventions. Music therapists could draw on insights about stuck songs and focus music to design personalized interventions that cater to ADHD-specific needs. Similarly, educators might integrate music strategically into learning environments to support students with ADHD, using curated playlists to enhance concentration or reduce anxiety. Furthermore, technology developers could harness these insights to create adaptive music applications that dynamically adjust to an individual’s cognitive and emotional states, offering real-time support for focus and mood regulation. An interdisciplinary approach integrating neuroscience, psychology, and musicology will be essential in developing personalized music interventions that support cognitive and emotional adaptation.

Future research must pursue longitudinal studies tracking the evolution of musical interventions for ADHD, examining how musical preferences impact cognitive management strategies over time. Methodological innovations are crucial, requiring more sophisticated data collection techniques that capture the musical experiences of individuals with ADHD. The community-driven insight done in the present study complements clinical perspectives, offering valuable qualitative data that reflects real-world practices. However, cultural factors could influence music preferences, suggesting a need for cross-cultural studies to explore how ADHD-related music behaviors vary globally.