# Harnessing Linguistic Analysis for ADHD Diagnosis
## Support

Florian Cafiero, Juan Barrios, Simon Gabay, Martin Debbané

► To cite this version:

Florian Cafiero, Juan Barrios, Simon Gabay, Martin Debbané. Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories. RaPID-5@LREC-COLING-2024, Association for Computational Linguistics, May 2024, Turin, Italy. 10.31234/osf.io/m97gx. hal-04558160

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<figure><img src="image_1.png" /></figure>
# Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories

Florian Cafiero¹, Juan Barrios², Simon Gabay², Martin Debbané²

¹ Université Paris Sciences et Lettres

florian.cafiero@chartes.psl.eu

² Université de Genève

{juan.barrios,simon.gabay,martin.debbane}@unige.ch

## Abstract

This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.

**Keywords:** ADHD, psycholinguistics, NLP, stylometry

## 1. Introduction

The use of Natural Language Processing (NLP) methods in psychology is both useful and complex. Useful because prediction tools have been proven for years to assist clinicians in their work. However, it is also complex because it is difficult to bring together enough people with a specific disorder to obtain a satisfactory corpus for a NLP experiment. This is particularly the case of Attention-Deficit/Hyperactivity Disorder (ADHD) in adolescents (cf. Barrios et al. 2023), a disorder with high prevalence in the population (±5.6% in teenagers aged 12 to 18 years, cf. Salari et al. 2023) producing a high level of impairment in daily life. In this paper, we propose to examine the question of ADHD in adolescents and, on the basis of a corpus recently collected in Geneva, to improve the diagnosis in young patients using machine learning techniques.

### 1.1. The impact of ADHD

ADHD is a prevalent neurodevelopmental disorder characterised by differences in large-scale neural connectivity (Rafi et al., 2023) and symptoms of inattention, hyperactivity, and impulsivity. Children with ADHD often struggle with impaired academic performance (Español-Martín et al., 2023), emotional regulation (Rathje et al., 2023), and different mentalisation abilities (Poznyak et al., 2023). As a result, these impairments often lead to disruptive behaviours, difficulties to maintain relationships,

and challenges in daily functioning (Barkley, 2015). If not addressed in time, these impairments can extend from adolescence to adulthood, contributing to academic underachievement, substance abuse, and mental health problems such as depression and anxiety (Faraone et al., 2024). It is therefore crucial to detect ADHD to tackle these psychosocial obstacles and difficulties as soon as possible, by providing support within educational settings and nurturing the growth of social skills, in order to promote positive development and improve the well-being of people with ADHD. (Barkley, 2015).

### 1.2. The detection of ADHD

The early diagnosis of ADHD is fundamental (Wolraich et al., 2019), but it is both complex and time consuming, especially because of the comorbidities that can co-occur with ADHD or that can mimic similar symptoms to ADHD (Barkley, 2014). Furthermore, it mostly relies on subjective evaluations of observed behaviours, which can produce biases during psychological assessment and for differential diagnoses (Miyasaka et al., 2018). The use of computerised tests to incorporate objective data in the assessment process has already been proposed to address the aforementioned issues (Gualtieri and Johnson, 2005), but alternative techniques always have to be tested.

The advent of NLP and stylometric methods presents new avenues for computerised assessment technology, enabling the generation of rich objective data to improve the diagnosis. By quan-
# Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories

Florian Cafiero¹, Juan Barrios², Simon Gabay², Martin Debbané²

¹ Université Paris Sciences et Lettres

florian.cafiero@chartes.psl.eu

² Université de Genève

{juan.barrios,simon.gabay,martin.debbane}@unige.ch

## Abstract

This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.

**Keywords:** ADHD, psycholinguistics, NLP, stylometry

## 1. Introduction

The use of Natural Language Processing (NLP) methods in psychology is both useful and complex. Useful because prediction tools have been proven for years to assist clinicians in their work. However, it is also complex because it is difficult to bring together enough people with a specific disorder to obtain a satisfactory corpus for a NLP experiment. This is particularly the case of Attention-Deficit/Hyperactivity Disorder (ADHD) in adolescents (cf. Barrios et al. 2023), a disorder with high prevalence in the population (±5.6% in teenagers aged 12 to 18 years, cf. Salari et al. 2023) producing a high level of impairment in daily life. In this paper, we propose to examine the question of ADHD in adolescents and, on the basis of a corpus recently collected in Geneva, to improve the diagnosis in young patients using machine learning techniques.

### 1.1. The impact of ADHD

ADHD is a prevalent neurodevelopmental disorder characterised by differences in large-scale neural connectivity (Rafi et al., 2023) and symptoms of inattention, hyperactivity, and impulsivity. Children with ADHD often struggle with impaired academic performance (Español-Martín et al., 2023), emotional regulation (Rathje et al., 2023), and different mentalisation abilities (Poznyak et al., 2023). As a result, these impairments often lead to disruptive behaviours, difficulties to maintain relationships,

and challenges in daily functioning (Barkley, 2015). If not addressed in time, these impairments can extend from adolescence to adulthood, contributing to academic underachievement, substance abuse, and mental health problems such as depression and anxiety (Faraone et al., 2024). It is therefore crucial to detect ADHD to tackle these psychosocial obstacles and difficulties as soon as possible, by providing support within educational settings and nurturing the growth of social skills, in order to promote positive development and improve the well-being of people with ADHD. (Barkley, 2015).

### 1.2. The detection of ADHD

The early diagnosis of ADHD is fundamental (Wolraich et al., 2019), but it is both complex and time consuming, especially because of the comorbidities that can co-occur with ADHD or that can mimic similar symptoms to ADHD (Barkley, 2014). Furthermore, it mostly relies on subjective evaluations of observed behaviours, which can produce biases during psychological assessment and for differential diagnoses (Miyasaka et al., 2018). The use of computerised tests to incorporate objective data in the assessment process has already been proposed to address the aforementioned issues (Gualtieri and Johnson, 2005), but alternative techniques always have to be tested.

The advent of NLP and stylometric methods presents new avenues for computerised assessment technology, enabling the generation of rich objective data to improve the diagnosis. By quan-
titatively analysing the linguistic and stylistic fea-
tures of texts written by diagnosed persons, re-
searchers can uncover linguistic fingerprints that
traditional methods may miss (Cafiero and Camps,
2022). Previous research has demonstrated no-
table linguistic differences between individuals with
ADHD and control groups (Yoder, 2006; Kim and
Lee, 2009; Kim et al., 2015). However, the idea of
using these differences to assist psychologists and
psychiatrists in diagnosing ADHD using traditional
stylometric methods (Barrios et al., 2023) is new.

## 1.3. Understanding Psychopathological Processes through Narrative

Integrating narrative approaches to psychopathol-
ogy (Lind et al., 2022) is a field in constant growth
(Waters and Fivush, 2015; Adler et al., 2016; Van-
den Poel and Hermans, 2019; Reed et al., 2020). It
specifically studies how people make sense of their
life's experiences and how the resulting script of
their life changes according to changing conditions,
the evolution of their main goals, etc. The profound
paradox of this process of meaning-making lies in
the fact that although individuals change in their
ways of being and living, they remain recognisable
as the same person, which remains in a certain
sense unchanged – a paradox very similar to that
of authorial attribution, according to which a per-
son's literary style remains stable despite stylistic
changes over a lifetime.

Such a narrative approach implies the existence
of written or oral linguistic material, the usage of
which (i.e., the choice of pronouns or function
words, etc.) provides significant understanding
of someone's psychological state (Tausczik and
Pennebaker, 2009; Pennebaker et al., 2003; Pen-
nebaker and King, 1999). This insight is pivotal, as
these identifiable linguistic patterns can serve as
a tool to help diagnose mental health, highlighting
the importance of language on understanding and
assessing mental well-being. Among the different
types of narratives that can be used, Autobiograph-
ical Memories (AM) are a particularly important
resource.

Indeed, AM encompass memories of personal
experiences and events, and therefore serve as the
foundation for constructing our life narratives and
ultimately the main script of our life story. Within
the framework of AM, certain memories known as
Self-Defining Memories (SDMs) are of particular
significance. SDMs refer to events that are highly
relevant to identity processes (Singer et al., 2007;
Blagov and Singer, 2004), characterised by their
vividness, emotional intensity, frequent recall and
focus on the individual persistent concerns or unre-
solved conflicts (Singer et al., 2012). As such, they
are the building blocks of an individual's life story

and are essential to form a coherent and contin-
uous sense of self (Conway and Pleydell-Pearce,
2000). In fact, while recalling and reflecting on
SDMs, individuals construct narratives that high-
light meaningful life events, significant relationships,
and / or central values (Singer and Blagov, 2004).
It is this repeated retrieval and reinterpretation over
time that reinforces certain aspects of identity, po-
tentially reshaping others (McAdams, 2013; Bluck
and Alea, 2002), and can influence an individual's
self-concept, its worldview, or emotional well-being
(Berntsen and Rubin, 2006).

During the transition from adolescence to early
adulthood, the emergence of SDMs marks a piv-
otal phase in psychological development. In this
period of life, people actively engage in identity ex-
ploration and self-reflection, constructing narratives
that shape their sense of self and their experiences
(McAdams, 2013). This phase is of particular signif-
icance for understanding psychopathology, as dis-
turbances in identity formation and autobiographi-
cal memory can contribute to various mental health
issues (Branje et al., 2021). Therefore, a com-
prehensive analysis of the content, structure, and
patterns of narratives during this period provides
insight into identity development and can be used
as a window into psychological processes (Conway
and Pleydell-Pearce, 2000; Berntsen and Rubin,
2006; Singer et al., 2007; McAdams, 2013) as well
as data for the automatic detection of ADHD.

## 2. Method

**2.1. Participants**

66 adolescents (15.55 ± 1.78 years; 25 men and 31
women) were included in the experiment (cf. tab. 1).
Adolescents with ADHD were recruited through ad-
vertisements in local parents' associations for chil-
dren with ADHD and through collaborations estab-
lished with local child psychiatrists. Participants
in the control group were recruited by undergradu-
ate students attending the Faculty of Psychology
and Sciences of Education at Geneva University,
Switzerland. The inclusion criteria for all the partic-
ipants were age (12-17 years), fluency in French,
and, for the ADHD group, meeting current diagnos-
tic criteria for ADHD (DSM-V, American Psychiatric
Association, 2013). Non-fluent francophone speak-
ers and individuals currently under psychiatric treat-
ment were excluded from the study.

The diagnostic criteria for ADHD were investi-
gated by detailed anamnestic interviews and con-
firmed using the “ADHD Child Evaluation” (ACE)
(Young, 2015). All diagnostic assessments were
conducted by experienced clinical psychologists
specialised in ADHD.

The final ADHD sample meeting inclusion criteria
titatively analysing the linguistic and stylistic fea-
tures of texts written by diagnosed persons, re-
searchers can uncover linguistic fingerprints that
traditional methods may miss (Cafiero and Camps,
2022). Previous research has demonstrated no-
table linguistic differences between individuals with
ADHD and control groups (Yoder, 2006; Kim and
Lee, 2009; Kim et al., 2015). However, the idea of
using these differences to assist psychologists and
psychiatrists in diagnosing ADHD using traditional
stylometric methods (Barrios et al., 2023) is new.

## 1.3. Understanding Psychopathological Processes through Narrative

Integrating narrative approaches to psychopathol-
ogy (Lind et al., 2022) is a field in constant growth
(Waters and Fivush, 2015; Adler et al., 2016; Van-
den Poel and Hermans, 2019; Reed et al., 2020). It
specifically studies how people make sense of their
life's experiences and how the resulting script of
their life changes according to changing conditions,
the evolution of their main goals, etc. The profound
paradox of this process of meaning-making lies in
the fact that although individuals change in their
ways of being and living, they remain recognisable
as the same person, which remains in a certain
sense unchanged – a paradox very similar to that
of authorial attribution, according to which a per-
son's literary style remains stable despite stylistic
changes over a lifetime.

Such a narrative approach implies the existence
of written or oral linguistic material, the usage of
which (i.e., the choice of pronouns or function
words, etc.) provides significant understanding
of someone's psychological state (Tausczik and
Pennebaker, 2009; Pennebaker et al., 2003; Pen-
nebaker and King, 1999). This insight is pivotal, as
these identifiable linguistic patterns can serve as
a tool to help diagnose mental health, highlighting
the importance of language on understanding and
assessing mental well-being. Among the different
types of narratives that can be used, Autobiograph-
ical Memories (AM) are a particularly important
resource.

Indeed, AM encompass memories of personal
experiences and events, and therefore serve as the
foundation for constructing our life narratives and
ultimately the main script of our life story. Within
the framework of AM, certain memories known as
Self-Defining Memories (SDMs) are of particular
significance. SDMs refer to events that are highly
relevant to identity processes (Singer et al., 2007;
Blagov and Singer, 2004), characterised by their
vividness, emotional intensity, frequent recall and
focus on the individual persistent concerns or unre-
solved conflicts (Singer et al., 2012). As such, they
are the building blocks of an individual's life story

and are essential to form a coherent and contin-
uous sense of self (Conway and Pleydell-Pearce,
2000). In fact, while recalling and reflecting on
SDMs, individuals construct narratives that high-
light meaningful life events, significant relationships,
and / or central values (Singer and Blagov, 2004).
It is this repeated retrieval and reinterpretation over
time that reinforces certain aspects of identity, po-
tentially reshaping others (McAdams, 2013; Bluck
and Alea, 2002), and can influence an individual's
self-concept, its worldview, or emotional well-being
(Berntsen and Rubin, 2006).

During the transition from adolescence to early
adulthood, the emergence of SDMs marks a piv-
otal phase in psychological development. In this
period of life, people actively engage in identity ex-
ploration and self-reflection, constructing narratives
that shape their sense of self and their experiences
(McAdams, 2013). This phase is of particular signif-
icance for understanding psychopathology, as dis-
turbances in identity formation and autobiographi-
cal memory can contribute to various mental health
issues (Branje et al., 2021). Therefore, a com-
prehensive analysis of the content, structure, and
patterns of narratives during this period provides
insight into identity development and can be used
as a window into psychological processes (Conway
and Pleydell-Pearce, 2000; Berntsen and Rubin,
2006; Singer et al., 2007; McAdams, 2013) as well
as data for the automatic detection of ADHD.

## 2. Method

2.1. Participants

66 adolescents (15.55 ± 1.78 years; 25 men and 31
women) were included in the experiment (cf. tab. 1).
Adolescents with ADHD were recruited through ad-
vertisements in local parents' associations for chil-
dren with ADHD and through collaborations estab-
lished with local child psychiatrists. Participants
in the control group were recruited by undergradu-
ate students attending the Faculty of Psychology
and Sciences of Education at Geneva University,
Switzerland. The inclusion criteria for all the partic-
ipants were age (12-17 years), fluency in French,
and, for the ADHD group, meeting current diagnos-
tic criteria for ADHD (DSM-V, American Psychiatric
Association, 2013). Non-fluent francophone speak-
ers and individuals currently under psychiatric treat-
ment were excluded from the study.

The diagnostic criteria for ADHD were investi-
gated by detailed anamnestic interviews and con-
firmed using the “ADHD Child Evaluation” (ACE)
(Young, 2015). All diagnostic assessments were
conducted by experienced clinical psychologists
specialised in ADHD.

The final ADHD sample meeting inclusion criteria
<table><caption>Table 1: Description of participants</caption><thead><tr><th></th><th>ADHD</th><th>Control</th></tr></thead><tbody><tr><th>Men</th><td>13 (52%)</td><td>22 (56.66%)</td></tr><tr><th>Women</th><td>12 (48%)</td><td>19 (46.34%)</td></tr><tr><th>12-15 yo</th><td>12 (48%)</td><td>9 (21.95%)</td></tr><tr><th>16-17 yo</th><td>13 (52%)</td><td>32 (78.05%)</td></tr><tr><th>Total</th><td>25 (100%)</td><td>41 (100%)</td></tr></tbody></table>

consisted of 25 participants, 16 were diagnosed with the inattentive modality of ADHD, 1 with the hyperactive modality, while 8 exhibited the mixed modality of ADHD.

## 2.2. Data

The SDMs were collected using the Self-Defining Memories task (Singer and Blagov, 2001; Thorne and McLean, 2001). Following its procedure, participants were asked to evoke personal memories of events (the SDMs) meeting six criteria: they (1) occurred at least one year ago and were (2) important and generally vividly represented; (3) meaningful and useful to help themselves or a significant other understand who they are; (4) were related to an important and enduring theme and linked to other events on the same topic; (5) were either positive or negative and generate strong feelings; and finally, (6) were recalled many times.

<figure><img src="image_1.png" /><figcaption>Figure 1: Number of tokens per SDM in ADHD group vs control group.</figcaption></figure>

Participants were then told to imagine a situation where they met someone they liked very much and with whom they agreed, during a walk, to talk about who they really are, their “Real Me”, shar-

ing several personal past events that powerfully convey how they have become the person they currently are. Participants were given three sheets of paper on which they had to write down, on each sheet, one SDM with a one-sentence summary. The SDMs were then transcribed by researchers, and the spelling corrected on the fly<fnref n="1" />. It is important to note that the SDMs produced by the two groups are quite different, particularly in terms of length (cf. fig. 1).

<table><caption>Table 2: Description of SDMs per group</caption><thead><tr><th></th><th>ADHD</th><th>Control</th></tr></thead><tbody><tr><th>SDMs</th><td>75</td><td>123</td></tr><tr><th>Positive Affect</th><td></td><td></td></tr><tr><th>0</th><td>20 (26.67%)</td><td>27 (21.95%)</td></tr><tr><th>1</th><td>1 (1.33%)</td><td>5 (4.07%)</td></tr><tr><th>2</th><td>0 (0%)</td><td>7 (5.69%)</td></tr><tr><th>3</th><td>4 (5.33%)</td><td>7 (5.69%)</td></tr><tr><th>4</th><td>4 (5.33%)</td><td>8 (6.5%)</td></tr><tr><th>5</th><td>11 (14.67%)</td><td>18 (14.63%)</td></tr><tr><th>6</th><td>35 (46.67%)</td><td>51 (41.46%)</td></tr><tr><th>Negative Affect</th><td></td><td></td></tr><tr><th>0</th><td>46 (61.33%)</td><td>50 (40.65%)</td></tr><tr><th>1</th><td>8 (10.67%)</td><td>11 (8.94%)</td></tr><tr><th>2</th><td>3 (4%)</td><td>13 (10.57%)</td></tr><tr><th>3</th><td>1 (1.33%)</td><td>3 (2.44%)</td></tr><tr><th>4</th><td>5 (6.67%)</td><td>12 (9.76%)</td></tr><tr><th>5</th><td>6 (8%)</td><td>9 (7.32%)</td></tr><tr><th>6</th><td>6 (8%)</td><td>25 (20.33%)</td></tr></tbody></table>

Thereafter, participants were asked to rate their feelings after recalling each SDM on a 7-point rating scale from 0 (: not at all) to 6 (: extremely). The score distribution follows a U-shape (cf. tab. 2), which implies a tendency to score affects at the extremes, and the values do not correlate with the two groups according to a $\chi^2$ analysis.

## 2.3. Textual profiling

### 2.3.1. Feature extraction

To predict if a text has been written by an adolescent diagnosed with ADHD or not, we train classifiers on a variety of linguistic features (character 3-grams, words, words bigrams, function words, type token ratio, text length, average sentence length) that have been proven reliable features by previous literature (Barrios et al., 2023), as well as with outputs from our experiment (text length, type/token ratio...) and information about the participant (age, gender...).

This consolidated feature matrix serves as the input to a machine learning pipeline, at the heart

<footnote n="1">¹This procedure was implemented before starting the computational experiments.</footnote>
<table><caption>Table 1: Description of participants</caption><thead><tr><th></th><th>ADHD</th><th>Control</th></tr></thead><tbody><tr><th>Men</th><td>13 (52%)</td><td>22 (56.66%)</td></tr><tr><th>Women</th><td>12 (48%)</td><td>19 (46.34%)</td></tr><tr><th>12-15 yo</th><td>12 (48%)</td><td>9 (21.95%)</td></tr><tr><th>16-17 yo</th><td>13 (52%)</td><td>32 (78.05%)</td></tr><tr><th>Total</th><td>25 (100%)</td><td>41 (100%)</td></tr></tbody></table>

consisted of 25 participants, 16 were diagnosed with the inattentive modality of ADHD, 1 with the hyperactive modality, while 8 exhibited the mixed modality of ADHD.

## 2.2. Data

The SDMs were collected using the Self-Defining Memories task (Singer and Blagov, 2001; Thorne and McLean, 2001). Following its procedure, participants were asked to evoke personal memories of events (the SDMs) meeting six criteria: they (1) occurred at least one year ago and were (2) important and generally vividly represented; (3) meaningful and useful to help themselves or a significant other understand who they are; (4) were related to an important and enduring theme and linked to other events on the same topic; (5) were either positive or negative and generate strong feelings; and finally, (6) were recalled many times.

<figure><img src="image_2.png" /><figcaption>Figure 1: Number of tokens per SDM in ADHD group vs control group.</figcaption></figure>

Participants were then told to imagine a situation where they met someone they liked very much and with whom they agreed, during a walk, to talk about who they really are, their “Real Me”, shar-

ing several personal past events that powerfully convey how they have become the person they currently are. Participants were given three sheets of paper on which they had to write down, on each sheet, one SDM with a one-sentence summary. The SDMs were then transcribed by researchers, and the spelling corrected on the fly<fnref n="1" />. It is important to note that the SDMs produced by the two groups are quite different, particularly in terms of length (cf. fig. 1).

<table><caption>Table 2: Description of SDMs per group</caption><thead><tr><th></th><th>ADHD</th><th>Control</th></tr></thead><tbody><tr><th>SDMs</th><td>75</td><td>123</td></tr><tr><th>Positive Affect</th><td></td><td></td></tr><tr><th>0</th><td>20 (26.67%)</td><td>27 (21.95%)</td></tr><tr><th>1</th><td>1 (1.33%)</td><td>5 (4.07%)</td></tr><tr><th>2</th><td>0 (0%)</td><td>7 (5.69%)</td></tr><tr><th>3</th><td>4 (5.33%)</td><td>7 (5.69%)</td></tr><tr><th>4</th><td>4 (5.33%)</td><td>8 (6.5%)</td></tr><tr><th>5</th><td>11 (14.67%)</td><td>18 (14.63%)</td></tr><tr><th>6</th><td>35 (46.67%)</td><td>51 (41.46%)</td></tr><tr><th>Negative Affect</th><td></td><td></td></tr><tr><th>0</th><td>46 (61.33%)</td><td>50 (40.65%)</td></tr><tr><th>1</th><td>8 (10.67%)</td><td>11 (8.94%)</td></tr><tr><th>2</th><td>3 (4%)</td><td>13 (10.57%)</td></tr><tr><th>3</th><td>1 (1.33%)</td><td>3 (2.44%)</td></tr><tr><th>4</th><td>5 (6.67%)</td><td>12 (9.76%)</td></tr><tr><th>5</th><td>6 (8%)</td><td>9 (7.32%)</td></tr><tr><th>6</th><td>6 (8%)</td><td>25 (20.33%)</td></tr></tbody></table>

Thereafter, participants were asked to rate their feelings after recalling each SDM on a 7-point rating scale from 0 (: not at all) to 6 (: extremely). The score distribution follows a U-shape (cf. tab. 2), which implies a tendency to score affects at the extremes, and the values do not correlate with the two groups according to a $\chi^2$ analysis.

## 2.3. Textual profiling

### 2.3.1. Feature extraction

To predict if a text has been written by an adolescent diagnosed with ADHD or not, we train classifiers on a variety of linguistic features (character 3-grams, words, words bigrams, function words, type token ratio, text length, average sentence length) that have been proven reliable features by previous literature (Barrios et al., 2023), as well as with outputs from our experiment (text length, type/token ratio...) and information about the participant (age, gender...).

This consolidated feature matrix serves as the input to a machine learning pipeline, at the heart

<footnote n="1">¹This procedure was implemented before starting the computational experiments.</footnote>
of which lies a Support Vector Classifier (SVC), with a grid search for optimal kernel (linear, polynomial, sigmoid, RBF) and hyper parameters (cost C and γ when relevant). This choice of classifier aligns with the high-dimensional nature of the feature space and is well-regarded for text classification tasks in recent articles and surveys (HaCohen-Kerner, 2022; Bevendorff et al., 2023; Fauzi et al., 2023), and particularly fit in the case of shorter texts (Cafiero and Camps, 2021, 2023; Vogel and Meghana, 2021; Suresh Kumar et al., 2024).

### 2.3.2. Model Evaluation

Model robustness and generalisability are assessed through Leave-One-Out Cross-Validation (LOOCV), an evaluation method that iterates over the dataset, using each document once as a test instance while training on the remainder. Such an approach ensures that every document contributes to the validation process, which is critical in scenarios with limited data such as ours, that hardly allow other methods such as K-fold cross validation.

To avoid overfitting, we run a grid search on the C parameter, and check if the models hold when it is set to low values. The model is thus encouraged to find a hyperplane with a larger margin, which can lead to better generalisation on unseen data, at the cost of possibly underfitting the training data.

## 3. Results

### 3.1. Classification of individual texts of Self Defining Memories

Classifying texts is paradoxically the easiest task in our context, as it triples our data points (each person has written 3 SDMs) in a relatively small database. The quality of the results holds even for very low values of the C parameter (0.01).

<table><caption>Table 3: SVM classification of individual SDMs: character 3-grams</caption><thead><tr><th></th><th>Precision</th><th>Recall</th><th>F1</th><th>Support</th></tr></thead><tbody><tr><td>ADHD</td><td>1.00</td><td>1.00</td><td>1.00</td><td>75</td></tr><tr><td>Control</td><td>1.00</td><td>1.00</td><td>1.00</td><td>123</td></tr><tr><td>Accuracy</td><td></td><td></td><td>1.00</td><td>198</td></tr><tr><td>Macro avg</td><td>1.00</td><td>1.00</td><td>1.00</td><td>198</td></tr></tbody></table>

### 3.2. Classification of individuals

In this experiment, we concatenate all three SDMs written by each participant, and try to predict if the person belongs to the ADHD group or the control group. The task is in our case counter-intuitively more complex because of the objectively important,

but statistically speaking small, number of participants: the quantity of text remains the same, but the number of data points diminishes. Classifying individuals is redundant in our case, because the results are already more than satisfactory at the text level (1 individual=3 texts). But as it artificially makes the task harder, it helps us evaluate more complex models that could prove to be helpful facing unseen data.

We test three settings:

1. a purely lexical and syntactic analysis of the texts;

2. a setup purely relying on self reported affects and information;

3. a mix of the most relevant items.

For each of these settings, we test the various combinations of point of measures at our disposal.

#### 3.2.1. Setup 1: linguistic classifier

The best setup we get according to our objectives does not rely only on character 3-grams, but concatenates character 3-grams, words, lexical richness and average sentence length as classifying features.

<table><caption>Table 4: SVM classification of individuals: best linguistic classifier for accuracy</caption><thead><tr><th></th><th>Prec.</th><th>Rec.</th><th>F1</th><th>Supp.</th></tr></thead><tbody><tr><td>ADHD</td><td>0.78</td><td>0.56</td><td>0.65</td><td>25</td></tr><tr><td>Control</td><td>0.77</td><td>0.90</td><td>0.83</td><td>41</td></tr><tr><td>Accuracy</td><td></td><td></td><td>0.77</td><td>66</td></tr><tr><td>Macro avg</td><td>0.77</td><td>0.73</td><td>0.74</td><td>66</td></tr><tr><td>Weighted avg</td><td>0.76</td><td>0.76</td><td>0.75</td><td>66</td></tr></tbody></table>

It yields a satisfactory accuracy but unfortunately fails to provide a good recall for the ADHD group, which means that some texts written by adolescents with ADHD have minimally significant linguistic markers. These results could be linked to the different forms of ADHD (cf. § 2.1).

#### 3.2.2. Setup 2: background and self-report affect

Classifying only on reported affect, be it the positive or negatives values given for each text, or an aggregated global value, are insufficient to give an accurate prediction in any combination possible. The classifier always ends up predicting one class only, even when implementing imbalance correction strategies. This indicates that the data is not sufficient in itself to predict the categories. We thus do not give a detailed report on the best models.
of which lies a Support Vector Classifier (SVC), with a grid search for optimal kernel (linear, polynomial, sigmoid, RBF) and hyper parameters (cost C and γ when relevant). This choice of classifier aligns with the high-dimensional nature of the feature space and is well-regarded for text classification tasks in recent articles and surveys (HaCohen-Kerner, 2022; Bevendorff et al., 2023; Fauzi et al., 2023), and particularly fit in the case of shorter texts (Cafiero and Camps, 2021, 2023; Vogel and Meghana, 2021; Suresh Kumar et al., 2024).

### 2.3.2. Model Evaluation

Model robustness and generalisability are assessed through Leave-One-Out Cross-Validation (LOOCV), an evaluation method that iterates over the dataset, using each document once as a test instance while training on the remainder. Such an approach ensures that every document contributes to the validation process, which is critical in scenarios with limited data such as ours, that hardly allow other methods such as K-fold cross validation.

To avoid overfitting, we run a grid search on the C parameter, and check if the models hold when it is set to low values. The model is thus encouraged to find a hyperplane with a larger margin, which can lead to better generalisation on unseen data, at the cost of possibly underfitting the training data.

## 3. Results

### 3.1. Classification of individual texts of Self Defining Memories

Classifying texts is paradoxically the easiest task in our context, as it triples our data points (each person has written 3 SDMs) in a relatively small database. The quality of the results holds even for very low values of the C parameter (0.01).

<table><caption>Table 3: SVM classification of individual SDMs: character 3-grams</caption><thead><tr><th></th><th>Precision</th><th>Recall</th><th>F1</th><th>Support</th></tr></thead><tbody><tr><td>ADHD</td><td>1.00</td><td>1.00</td><td>1.00</td><td>75</td></tr><tr><td>Control</td><td>1.00</td><td>1.00</td><td>1.00</td><td>123</td></tr><tr><td>Accuracy</td><td></td><td></td><td>1.00</td><td>198</td></tr><tr><td>Macro avg</td><td>1.00</td><td>1.00</td><td>1.00</td><td>198</td></tr></tbody></table>

### 3.2. Classification of individuals

In this experiment, we concatenate all three SDMs written by each participant, and try to predict if the person belongs to the ADHD group or the control group. The task is in our case counter-intuitively more complex because of the objectively important,

but statistically speaking small, number of participants: the quantity of text remains the same, but the number of data points diminishes. Classifying individuals is redundant in our case, because the results are already more than satisfactory at the text level (1 individual=3 texts). But as it artificially makes the task harder, it helps us evaluate more complex models that could prove to be helpful facing unseen data.

We test three settings:

1. a purely lexical and syntactic analysis of the texts;

2. a setup purely relying on self reported affects and information;

3. a mix of the most relevant items.

For each of these settings, we test the various combinations of point of measures at our disposal.

#### 3.2.1. Setup 1: linguistic classifier

The best setup we get according to our objectives does not rely only on character 3-grams, but concatenates character 3-grams, words, lexical richness and average sentence length as classifying features.

<table><caption>Table 4: SVM classification of individuals: best linguistic classifier for accuracy</caption><thead><tr><th></th><th>Prec.</th><th>Rec.</th><th>F1</th><th>Supp.</th></tr></thead><tbody><tr><td>ADHD</td><td>0.78</td><td>0.56</td><td>0.65</td><td>25</td></tr><tr><td>Control</td><td>0.77</td><td>0.90</td><td>0.83</td><td>41</td></tr><tr><td>Accuracy</td><td></td><td></td><td>0.77</td><td>66</td></tr><tr><td>Macro avg</td><td>0.77</td><td>0.73</td><td>0.74</td><td>66</td></tr><tr><td>Weighted avg</td><td>0.76</td><td>0.76</td><td>0.75</td><td>66</td></tr></tbody></table>

It yields a satisfactory accuracy but unfortunately fails to provide a good recall for the ADHD group, which means that some texts written by adolescents with ADHD have minimally significant linguistic markers. These results could be linked to the different forms of ADHD (cf. § 2.1).

#### 3.2.2. Setup 2: background and self-report affect

Classifying only on reported affect, be it the positive or negatives values given for each text, or an aggregated global value, are insufficient to give an accurate prediction in any combination possible. The classifier always ends up predicting one class only, even when implementing imbalance correction strategies. This indicates that the data is not sufficient in itself to predict the categories. We thus do not give a detailed report on the best models.
### 3.2.3. Setup 3: mixed classifier

Mixed classifiers, i.e. classifiers relying on any combination of linguistic and background information, never outperform classifiers based on linguistic features only, and provide at the very maximum the exact same performance, in terms of precision, F1 and recall, as purely linguistic classifiers alone. We thus do not give a detailed report on the best models.

## 4. Discussion

Regarding the satisfactory accuracy but the limited recall of linguistic classifiers for the ADHD group at the person's level, it could reflect the intricate nature of the disorder and its numerous comorbidities, and/or underline the existence of coping strategies as well as the quality of the support systems. In fact, although clinical studies have found that rates of language impairment in children with ADHD often exceed 50% (Mueller and Tomblin, 2012) and that these children present greater difficulties in expressive writing, spelling, and writing speed (Re et al., 2007), it is worth noting that some individuals with ADHD may also possess high IQ (High Intellectual Potential, cf. Tordjman et al. 2007; Rommelse et al. 2016) and excel academically and socially, which could introduce some heterogeneity in the linguistic markers of ADHD. Moreover, many people who are not diagnosed with ADHD may suffer from a variety of its symptoms to a subclinical degree, or may have ADHD and have simply not be diagnosed despite our efforts. This introduces a second source of fuzziness, this time in the control group. However, despite these inherent complexities linked to a psychological disorder, a signal is detected and warrants further investigation from a psycholinguistic point of view.

At a more general level, the implications of our findings are twofold, offering potential benefits in both clinical and linguistic domains:

1. **Enhancing Early Identification:** The ability to infer ADHD-related characteristics from textual analysis could serve as a supplementary tool for early identification of potential ADHD cases. This is particularly relevant in contexts where there is a pronounced increase in the demand for diagnostic evaluations, potentially alleviating some of the pressure on clinical services.

2. **Contributing to Psycholinguistic Insights:** By examining the nuances of language use among individuals with ADHD, our study contributes to a deeper understanding of how ADHD influences linguistic expression. This exploration not only enriches our knowledge

of psycholinguistics but also opens avenues for further research into the intersection of language and psychological disorders.

Despite our promising results, it is important to state that automated text analysis for the identification of ADHD should not be viewed as a replacement for professional diagnosis. It should be envisaged as a supportive tool, that can contribute to the early detection and the understanding of ADHD through linguistic patterns.

## 5. Further work

A psycholinguistic analysis of the features is essential, in order to understand the linguistic particularities of ADHD. This involves not only identifying markers, which SVCs make it easier to do than LLMs, but also understanding the use of these markers. This type of analysis, however, is more likely to be done at the group level than at the text or individual level.

As our corpus is small, it is also important to obtain new data. In order to accelerate the acquisition of these, it could be useful to change method, and abandon manual writing for oral recitation, automatically transcribed with speech to text technologies. A study of the impact of such a change of medium would be interesting to carry out, in terms of quantity of data on the one hand, but also in terms of results on the other.

## 6. Acknowledgements

[Author 2] was funded by the Chilean National Agency for Research and Development (ANID Chile) through the PhD Abroad Scholarship, 2018 award. [Author 4] was funded by the Swiss National Science Foundation (Grant number 100014 179033), as well as the Marina Picasso Prize of the AEMD Foundation 2018.

## 7. Data availability

Data used in this study can be made available by the corresponding author on written request, as the authors have no legal or ethical restrictions to share the collected data in anonymised format. The code is available at the following address: 10.5281/zenodo.10953603

## 8. Statement of Ethics

The clinical study protocol was reviewed and approved in 2019 by the Swiss Ethics Committee whereas the control data collection was reviewed and approved in 2015 by the University of Geneva
### 3.2.3. Setup 3: mixed classifier

Mixed classifiers, i.e. classifiers relying on any combination of linguistic and background information, never outperform classifiers based on linguistic features only, and provide at the very maximum the exact same performance, in terms of precision, F1 and recall, as purely linguistic classifiers alone. We thus do not give a detailed report on the best models.

## 4. Discussion

Regarding the satisfactory accuracy but the limited recall of linguistic classifiers for the ADHD group at the person's level, it could reflect the intricate nature of the disorder and its numerous comorbidities, and/or underline the existence of coping strategies as well as the quality of the support systems. In fact, although clinical studies have found that rates of language impairment in children with ADHD often exceed 50% (Mueller and Tomblin, 2012) and that these children present greater difficulties in expressive writing, spelling, and writing speed (Re et al., 2007), it is worth noting that some individuals with ADHD may also possess high IQ (High Intellectual Potential, cf. Tordjman et al. 2007; Rommelse et al. 2016) and excel academically and socially, which could introduce some heterogeneity in the linguistic markers of ADHD. Moreover, many people who are not diagnosed with ADHD may suffer from a variety of its symptoms to a subclinical degree, or may have ADHD and have simply not be diagnosed despite our efforts. This introduces a second source of fuzziness, this time in the control group. However, despite these inherent complexities linked to a psychological disorder, a signal is detected and warrants further investigation from a psycholinguistic point of view.

At a more general level, the implications of our findings are twofold, offering potential benefits in both clinical and linguistic domains:

1. **Enhancing Early Identification:** The ability to infer ADHD-related characteristics from textual analysis could serve as a supplementary tool for early identification of potential ADHD cases. This is particularly relevant in contexts where there is a pronounced increase in the demand for diagnostic evaluations, potentially alleviating some of the pressure on clinical services.

2. **Contributing to Psycholinguistic Insights:** By examining the nuances of language use among individuals with ADHD, our study contributes to a deeper understanding of how ADHD influences linguistic expression. This exploration not only enriches our knowledge

of psycholinguistics but also opens avenues for further research into the intersection of language and psychological disorders.

Despite our promising results, it is important to state that automated text analysis for the identification of ADHD should not be viewed as a replacement for professional diagnosis. It should be envisaged as a supportive tool, that can contribute to the early detection and the understanding of ADHD through linguistic patterns.

## 5. Further work

A psycholinguistic analysis of the features is essential, in order to understand the linguistic particularities of ADHD. This involves not only identifying markers, which SVCs make it easier to do than LLMs, but also understanding the use of these markers. This type of analysis, however, is more likely to be done at the group level than at the text or individual level.

As our corpus is small, it is also important to obtain new data. In order to accelerate the acquisition of these, it could be useful to change method, and abandon manual writing for oral recitation, automatically transcribed with speech to text technologies. A study of the impact of such a change of medium would be interesting to carry out, in terms of quantity of data on the one hand, but also in terms of results on the other.

## 6. Acknowledgements

[Author 2] was funded by the Chilean National Agency for Research and Development (ANID Chile) through the PhD Abroad Scholarship, 2018 award. [Author 4] was funded by the Swiss National Science Foundation (Grant number 100014 179033), as well as the Marina Picasso Prize of the AEMD Foundation 2018.

## 7. Data availability

Data used in this study can be made available by the corresponding author on written request, as the authors have no legal or ethical restrictions to share the collected data in anonymised format. The code is available at the following address: 10.5281/zenodo.10953603

## 8. Statement of Ethics

The clinical study protocol was reviewed and approved in 2019 by the Swiss Ethics Committee whereas the control data collection was reviewed and approved in 2015 by the University of Geneva
Ethics Committee (Faculty of Psychology and Education Sciences). Written informed consent was obtained from all participants (and their parents for underage subjects).

## 9. Conflict of Interest Statement

The authors have no conflict of interest to declare.

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Ethics Committee (Faculty of Psychology and Education Sciences). Written informed consent was obtained from all participants (and their parents for underage subjects).

## 9. Conflict of Interest Statement

The authors have no conflict of interest to declare.

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American Psychiatric Association. 2013. *Diagnostic and statistical manual of mental disorders: DSM-5*, 5th ed. edition. American Psychiatric Association, Washington, DC.

Russell A Barkley, editor. 2014. *Attention-deficit hyperactivity disorder*, 4th ed. edition. Guilford Publications, New York, NY.

Russell A. Barkley. 2015. *Attention-Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment*, 3rd ed. edition. Guilford Press, New York - London.

Juan Barrios, Simon Gabay, Florian Cafiero, and Martin Debbané. 2023. *Detecting psychological disorders with stylometry*. In *Computational Humanities Research*, Paris, France. CEUR.

Dorthe Berntsen and David C. Rubin. 2006. *Emotion and vantage point in autobiographical. Cognition & Emotion*, 20(8):1193–1215.

Janek Bevendorff, Ian Borrego-Obrador, Mara Chinea-Ríos, Marc Franco-Salvador, Maik Fröbe, Annina Heini, Krzysztof Kredens, Maximilian Mayerl, Piotr Pęzik, Martin Potthast, et al. 2023. *Overview of pan 2023: Authorship verification, multi-author writing style analysis, profiling cryptocurrency influencers, and trigger detection: Condensed lab overview*. In *International Conference of the Cross-Language Evaluation Forum for European Languages*, pages 459–481. Springer.

Pavel Blagov and Jefferson Singer. 2004. *Four dimensions of self-defining memories (specificity, meaning, content, and affect) and their relationships to self-restraint, distress, and repressive defensiveness*. *Journal of Personality*, 72(3):481–511.

Susan Bluck and Nicole Alea. 2002. Exploring the functions of autobiographical memory: Why do I remember the autumn? In *Critical advances in reminiscence work: From theory to application.*, pages 61–75. Springer Publishing Company.

Susan Branje, Elisabeth L de Moor, Jenna Spitzer, and Andrik I Becht. 2021. *Dynamics of identity development in adolescence: A decade in review*. *J. Res. Adolesc.*, 31(4):908–927.

Florian Cafiero and Jean-Baptiste Camps. 2021. 'Psyché' as a Rosetta Stone? assessing collaborative authorship in the French 17th century theatre. *Proceedings http://ceur-ws.org ISSN*, 1613:0073.

Florian Cafiero and Jean-Baptiste Camps. 2022. *Affaires de style: du cas Molière à l'affaire Grégory: la stylométrie mène l'enquête*. Le Robert, Paris.

Florian Cafiero and Jean-Baptiste Camps. 2023. *Who could be behind QAnon? authorship attribution with supervised machine-learning*. *Digital Scholarship in the Humanities*, 38(4):1418–1430.

M. A. Conway and C. W. Pleydell-Pearce. 2000. *The construction of autobiographical memories in the self-memory system*. *Psychological Review*, 107(2):261–288.

Gemma Español-Martín, Mireia Pagerols, Raquel Prat, Cristina Rivas, Josep Antoni Ramos-Quiroga, Miquel Casas, and Rosa Bosch. 2023. The impact of attention-deficit/hyperactivity disorder and specific learning disorders on academic performance in Spanish children from a low-middle- and a high-income population. *Front. Psychiatry*, 14:1136994.

Stephen V Faraone, Mark A Bellgrove, Isabell Brikell, Samuele Cortese, Catharina A Hartman, Chris Hollis, Jeffrey H Newcorn, Alexandra Philipsen, Guilherme V Polanczyk, Katya Rubia, Margaret H Sibley, and Jan K Buitelaar. 2024. *Attention-deficit/hyperactivity disorder*. *Nat. Rev. Dis. Primers*, 10(1).

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Tilmann Habermas. 2011. Autobiographical reasoning: arguing and narrating from a biographical perspective. *New Directions for Child and Adolescent Development*, 2011(131):1–17.

Yaakov HaCohen-Kerner. 2022. Survey on profiling age and gender of text authors. *Expert Systems with Applications*, 199:117140.

Kyungil Kim and Chang Hwan Lee. 2009. Distinctive linguistic styles in children with ADHD. *Psychological Reports*, 105(2):365–371.

Kyungil Kim, Seongjik Lee, and Changhwan Lee. 2015. College students with ADHD traits and their language styles. *Journal of Attention Disorders*, 19(8):687–693.

Dijana Kosmajac. 2020. *Author and Language Profiling of Short Texts*. Ph.D. thesis, Dalhousie University.

Majse Lind, Carla Sharp, and William L Dunlop. 2022. Why, how, and when to integrate narrative identity within dimensional approaches to personality disorders. *J. Pers. Disord.*, 36(4):377–398.

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