Natural Language Processing (NLP) is increasingly being integrated into several application domains. Google AI recently introduced BERT (Bidirectional Encoder Representations from Transformers) [1] to match search queries with more relevant results for optimizing Google searches. Facebook AI also achieved impressive breakthroughs, such as by tackling harmful or improper content by means of Whole Post Integrity Embeddings (WPIE) [2]. Other examples can be found in mobile apps, such as virtual assistants like Amazon’s Alexa or Apple’s Siri [3]. Application domains range from cultural heritage [4] to the identification of concepts and relationships in a body of research papers [5] or clinical decision support systems [6].

Words that occur in similar contexts tend to have similar meanings. This was likely first formulated in 1954 by Harris [7]. But the most famous statement of this principle came a few years later from linguist JR Firth: “You shall know a word by the company it keeps!” [8].

One of the strongest trends in NLP at the moment is the use of word embeddings, which are vectors whose relative similarities correlate with semantic similarity, building on the ideas of Harris and Firth.

The approval of the International Classification of Functioning, Disability, and Health (ICF) [9] by the World Health Assembly in May 2001 has marked a paradigm shift in the way health and disability are understood and measured [10]. The ICF conceptualizes disability not solely as a problem that resides in the individual but as a health experience that occurs in a context [11].

Disability and functioning are, according to the ICF model, outcomes of interactions between health conditions (diseases, disorders, and injuries) and contextual factors [9].

In spite of both sharing context as a key component, word embeddings have been scarcely applied in the field of disability, to the best of our knowledge.

In this paper, we hypothesize that social media can, indeed, link them. Word embeddings are usually learned from a general-purpose corpus; when it doesn't match the domain's vocabulary (including the same words or using words in the same senses), it is a problem that cannot simply be fixed with a lot of data. More data could just pull word contexts and representations towards generic, rather than domain-specific, values.

Our hypotheses in this paper are the following: (1) Such domain-specific values can be extracted from public domain-specific social media (2) in a sufficient number for the embedding to be relevant to the ICF model and (3) verifiable by sound theoretical semantic tests (4) consistent with state-of-the-art publications and (5) providing actionable knowledge to onfield specialists.

Social media statistics from 2019 show that there are 3.2 billion social media users worldwide, and this number is growing [12].

Recent analyses remark that 42% of internet users take advantage of social media for health information, 32% of social media users in the United States share their health care experiences and family’s struggle stories, and 29% search for health information via social media platforms to observe others’ experiences with their diseases. Furthermore, 51% of those who live with a chronic disease have used the internet for information about health topics, such as details of a specific disease, medical procedures, drugs, medical devices, or health insurances [13].

In the following subsections, we describe the specific characteristics and objectives of this study.

Reddit data were collected from pushshift.io [26] by the pushshift.io API (application programming interface). pushshift.io is a website that stores all publicly available Reddit submissions and comments, allowing researchers to collect and share Reddit datasets for research purposes, with extensive publications in related research (eg, Lama et al [27]). In this paper, we used the pushshiftr R package as a wrapper for the pushshift.io API [26].

For training the word2vec model, we used the wordVectors R package [28]. It implements the original C code for word2vec [20].

Given a vectorial representation of 2 words, their semantic similarity (S) was calculated using the cosine similarity measure between their respective vectorial representation, S(v1,v2). The semantic distances between 2 words, d(v1; v2), was calculated as 1 minus the semantic similarity, d(v1; v2)=1–S(v1; v2) [28].

In this test, we measured the capabilities of the model to represent the semantic categories based on the Battig and Montague category norms, an invaluable tool for researchers in many fields, with a recent literature search revealing their use in over 1600 publications in more than 200 different journals [29].

In this study, we use Van Overschelde’s [29] updated and expanded version of the Battig and Montague norms (expanded from 56 to 70 semantic categories).

In order to measure how well a word i is grouped in relation to the other words in its semantic category, we used the silhouette coefficients, s(i).

Data were collected from 20,344 submissions and 96,314 comments from the disability subreddit.

Total comments were posted by 10,411 Redditors, and the total submissions were posted by 9658 Redditors.

The total number of different Redditors that have posted a submission or a comment is 15,072.

Considering that Reddit moderators remove a percentage of submissions (eg, for not following the Reddit posting guidelines), the number of different Redditors (15,072) is quite close to the 17,545 subscribers presented in Figure 1.

The first comment on the disability subreddit included in this analysis was published in February 2009, and the last was published in December 2019.

Note that these data were publicly accessible on Reddit and that no personally identifiable information is included in this study. The dataset is publicly available per request.

This paper focuses on the analysis of the 96,314 comments; Multimedia Appendix 1 presents further details on the retrieved data. For example, the most common 10 words are people (appearing 4231 times), disability (3619), time (3418), disabled (2479), feel (2457), lot (2433), person (2264), life (2162), day (1841) and job (1811). Multimedia Appendix 1 also features a plot of the percentage of comments containing specific words (eg, anger, hope, change, education) by year, the fastest-growing words, and the words with the steepest increase in past years.

We then ran the train_word2vec function of the wordVectors R package to train the model, with the following parameters: vectors=200, threads=4, window=12, iter=5, negative_samples=0.

We performed a preliminary validation using a reduced subset of the original Mikolov paper analogies [20]. We selected only some of those that might fit in our context; therefore, we did not include, for example, the well-known analogy

but we obtained promising results in a variety of analogies, such as

This means that if, for our trained model, we execute the nearest_to function as follows:

nearest_to(model[[“brother”]]-model[[“sister“]]+model[[“husband”]],5)

then we obtain “wife” as one of our top 5 nearest terms. We obtained several of them, with promising results. For example:

For the first 5 words of each of the first 65 semantic categories of the updated version of the Battig and Montague norm [29], we calculated the silhouette coefficients. The complete list of the 325 words and silhouette calculation is presented in Multimedia Appendix 2.

Multimedia Appendix 3, which shows the silhouette coefficients for the first 60 semantic categories, displays promising results. The numbers identifying each category are those presented in the original norm; for example, the first row is “3. A relative” because this is the third semantic category presented in the norm.

When analyzing the lower silhouette scores, we identified remarkable reasons for the miscategorization of the terms. For example, regarding the first semantic category, “1. A precious stone,” we selected i=diamond, and our model identified the category “43. A vegetable.” To be closer to i than category 1, we tried to find out why broccoli is closer to diamond than, for example, a ruby, and we found that diamond is a well-known class of broccoli. Similar explanations can be found for the other miscategorizations; for example, for category “59. A liquid,” we obtained a(i)=0.490 for i=water, but b(i) is lower [b(i)=0.381] because it is obtained for category “38. A non-alcoholic beverage.” This is because water is the first term in category 38 and is included in the mean distance calculation for b(i), (distance=0), but it is not included in the calculation of a(i) because the term i is not included in any calculation of a(i) (Multimedia Appendix 2). The same situation occurs with the category “20. An alcoholic beverage.”

It is important to note the lowest silhouette we obtained, s(i)=-0.645, for i=tuna in the “52. A fish” category (Multimedia Appendix 3). In this case, b(i) is remarkably lower because the distances to category “43. A vegetable” are lower for all words in the category. This means that our model finds a closer relation between, for example, broccoli or lettuce with tuna (a healthy diet) than between tuna and shark, bass, beta, or cod, which are very difficult to relate only to fish names.

In spite of obtaining some low silhouette codes, the detailed analysis led to promising results. Table 1 shows the algebraic operations (eg, food+cholesterol) that we used to focus the closest terms onto the specific ICF subcategories, avoiding the miscategorization problems.

Table 1 presents the closest terms to the representative subcategories of the products and technology chapter of the Environmental Factors. We present specific terms (eg, food and cholesterol, as illustrative examples) to show the potential of the model to discover relevant terms. In order to validate the results from Table 1, we went through the closest terms for each subcategory and identified recent publications and evidence.

The first terms for e1100, fruits and vegetables (veggie), have been extensively reported to be related to LDL (low-density lipoprotein) cholesterol [30], as well as wheat, oat [31], and broccoli [32].

We then considered other aspects related to food [eg, celiac disease (CD)]; we obtained gluten as the closest result, followed by mitochondrial (eg, reported by Picca et al [33]). The prevalence of the third term, coronary artery disease (CAD), increases nearly twofold in patients with CD, as reported by Gajulapalli et al [34].

Ungprasert et al [35] demonstrated a significantly higher risk of CD among patients with psoriasis (fourth closest term) as well as genetic factors [36].

In relation to e1101 (drugs) cannabis, there is an increasing interest in the medical use of it (eg, in chronic pain, which is very common in disability) [37].

In relation to assistive products (eg, related to speech and voice), several state-of-the-art solutions were retrieved, such as Speechify, Zoomtext, Voiceover, Siri, Dragon, and Alexa.

In relation to e120, mobility transportation, desirable properties for outdoor transportation were retrieved, such as lightest or reclining, as well as top product providers (eg, Invacare and wijits). For indoor transportation, the closest terms were hoist, stow, and hoyer, as opposed to powerchair, which was retrieved for outdoors.

We then explored transportation services (taxi+wheelchair), and the closest terms were uber, lyft, and paratransit. It is important to remark that Uber [38] and Lyft provide specific disability policies [39].

In relation to e140, culture recreation and sport, several paralympic well-known ports emerged, such as kayaking or archery. Another close term was boccia, which is another (but perhaps less popular) Paralympic sport [40].

Finally, in relation to e1501, outdoor wayfinding, the wheelchair+app operation retrieved ableroad [41] and wheelmap [42] as closest terms.

Multimedia Appendix 2 includes a similar analysis for chapters e2-e5.

As shown in Multimedia Appendix 2, for the e398 support and relationships subcategory, the closest term identified by our model is resilience. Resilience has been recently regarded as a key feature of rehabilitation and living life well following a disability [43]. Evidence shows that resilience-based skills have multiple benefits once applied to people’s lives (eg, a carry-over effect to other life domains). People do not have to be born resilient to become resilient; it can be improved with intentional practice [44].

It is unclear how well resilience or strategies to cultivate resilience are currently promoted as a component of rehabilitation programs. Furthermore, it does not yet have one unified definition, and research scholars have not decided on one specific understanding of what resilience means. Understanding resilience is an important component of building resilience [43].

Therefore, we used our model to identify its closest terms, and then we linked those terms to relevant publications. This is proposed as a straightforward example of how it can contribute to the understanding of a relevant term in rehabilitation. Maturity, compassion, anger, resentment, grief, insecurity, contentment, and resenting were identified by our model as the closest terms to resilience. Resilience and maturity have been extensively reported on (eg, by Davies et al [45]). The practice of compassion has been highlighted as an “essential component in nurturing resilience” [46]. As reported by Baldachino et al [47], the inability to cope effectively with anger may negatively impact a patient’s physical and psychological well-being in the realm of resilience. Designing evidence-based interventions aimed at decreasing the negative impact of anger on resilience can be advanced by examining the potential mediation effect between anger and resilience [48].

As noted by Howard and Meichenbaum [49], resentment is a way of undermining resilience; resentment is a form of chronic, deep-seated anger. Holding onto resentment and not letting it go can have deleterious health effects and undermine the development of resilience. In relation to grief, it has also been related to resilience (eg, Bonano et al [50]).

As was presented in the EU Social Insecurities and Resilience Report [51], people who have low levels of social insecurities more often report high levels of resilience. In recent psychological research [52], contentment was also directly associated with both resilience and life satisfaction and mediated the relationship between these 2 aspects of well-being. Details and further analysis is presented in Multimedia Appendix 1.

In this paper, we proposed social media as the link between word embeddings and the ICF’s environmental factors in a General Public License (GPL) framework (R-3.5.1). We applied a set of publicly available R libraries for collecting, model training, and analyzing Reddit public data. We trained a word2vec model using a small dataset and obtained encouraging results in king-queen–type analogies. Further, we obtained remarkable results in a standardized semantic categorization test. When mapping the discovered closest terms to representative subcategories of all ICF environmental factors, we verified them with scientific publications.

The obtained results open up interesting opportunities for exploration. Similarity isn’t just a way of finding the nearest words; it is also a way of extracting items of a single class in every environmental factor that makes up the physical, social, and attitudinal environment in which people live and conduct their lives, ranging from apps ”to know accessibility before you go“ to adapted sports (boccia). Therefore, it can be thought of as a form of topic modeling; however, rather than letting the algorithm choose a fixed number of topics, it gives us the option of choosing the specific term (such as resilience) and how expansive we want the explored space to be.

Medical professionals are currently being encouraged to participate in social media, as remarked upon by Stukus [53]; even if a health care professional is not interested in engaging in social media, they must still be aware of the information people may be encountering online in order to provide anticipatory guidance in the clinical setting [47]. Therefore, this study also intends to be a step in that direction.

The collected sample was not intended to be either representative or a comprehensive set of all comments posted by all persons with disabilities during the period under study. It includes all comments posted in Reddit’s disability subreddit; we did not include comments from other subreddits addressing specific disability causes (eg, stroke) because, in this study, we follow the approach of studying disability in general (instead of any specific group or etiology), an approach also adopted in other recent literature reviews (eg, Hästbacka et al [54]). In addition, the ICF is grounded in the principle of universality, namely, that functioning and disability are applicable to all people, irrespective of health condition. The ICF is committed to the principle of parity, which states that functional status is not determined by background etiology or, in particular, by whether one has a physical rather than mental health condition [9].

Furthermore, including only 93,614 comments allowed us to verify our hypotheses.

We did not include submissions in our analysis; we collected them (20,344 in total), but we did not use them for model training because only 2745 of them were labeled as “questions” while 1256 were labeled “articles and news,” 438 of them were labeled “videos,” and 13,570 were not labeled.

As presented in Multimedia Appendix 1, under the total number of comments by year, almost 80% of all comments took place during 2016-2019; therefore, the results of this study are weighted more strongly toward the recent years (2016-2019) rather than the early years (2009-2015) of the Disability subreddit.

Other relevant limitations to our study are related to the geographic location, spatial trajectory, or the time of day on which a comment was posted. As noted by Padilla et al [55] and Gore et al [56], such factors are relevant in social media. Spatiotemporal aspects are not controlled in our study; however, Reddit is most popular in the United States, with American users (representing 54% of Reddit’s user base) far outnumbering those from other countries. Second to the United States, the UK represents the next highest share of Reddit traffic, at 8%, with Canada rounding out third, at 6.4%. Reddit is most popular amongst young adults between the ages of 25-34 years, which make up more than half of the site’s users. Nevertheless, Reddit still draws in a large number of middle-aged users. A 2016 study found that people between the ages of 30-49 years accounted for 33% of the site’s users, indicating that Reddit is a viable platform for reaching both young and middle-aged adults. Reddit is particularly popular among males, who make up more than two-thirds of the site’s users [16].

In medical applications, word2vec has recently been applied to larger datasets, such as 641,279 French health-related documents produced in a professional context (Dynomant et al [57]), 880,165 papers in a biomedical publication venue (Feng et al [58]), 1,451,413 abstracts for Adverse Drug Event Discovery Using Biomedical Literature (Tafti et al [59]), and 1,749,870 reviews in Online Doctor Reviews [60].

Reddit has recently been used as a data source for studies in chronic diseases, such as Foufi et al’s [13] analysis of 17,624 text posts for entity and relation extraction, employing the PKDE4J tool. Sharma et al [61] performed a qualitative analysis of Reddit discussions regarding motivations and limitations associated with vaping among people with mental illness, a thematic analysis that included 3263 comments from 133 discussion threads.

In a small corpus (37,000 and 140,000 documents) using a semantic categorization test, word2vec was applied in analyzing and disambiguating the content of dreams [62]; this research field addresses questions such as “how do gender, cultural background, and waking-life experiences shape dream content?”.

Facebook groups, discussion forums, and chat rooms were recently analyzed [63] to explore and compare the interactions and connections among online support groups to better understand how people with disabilities were utilizing different social networks to facilitate communication interchange. They concluded that through participation on different platforms, persons with disabilities are able to provide and receive social support in various ways, without the barriers and constraints often experienced by this population.

Our approach is completely different in that we provide a tool for discovering terms of interest (in this paper, we applied it to the ICF environmental factors, but it could also be applied to, for example, the ICF’s body functions and structures). For example, regarding e5 services and associations, in using the 3 terms hear+association+kid, we obtained depaul as one of the closest results. The DePaul School for Hearing and Speech teaches children who are deaf or hard of hearing to listen and speak without sign language [64]. Another obtained close term is saskatoon, a new early learning pilot program in Saskatoon that impacts preschoolers who are deaf or hard of hearing by breaking down communication barriers [65].

Therefore, it can be addressed to a wide variety of involved stakeholders besides people with disabilities themselves, such as informal caregivers, health care professionals, and private or public associations.

This study explored the ability of word2vec to extract the main factors affecting the lives of people with disabilities within the ICF framework from a small dataset, showing promising results.

Our results open up interesting opportunities for exploration and discovery. Similarity is revealed as not only a way of finding the nearest words but also a way of extracting out items related to specific elements. Therefore, it can be thought of as a form of topic modeling, where users can focus on a particular term in-breath or in-depth.