Introduction
Loneliness is a global public health issue. Loneliness not only affects the quality of life but also leads to other mental health issues thus burdening the public health service system. Every year 162 000 Americans die from loneliness and social isolation.1 Every forty seconds someone, around the world, commits suicide while loneliness is shown to be a direct cause of suicide.2 Loneliness is shown to be associated with high risk for multiple health conditions such as physical and mental health, dementia and early mortality.3 4 Moreover, loneliness has been shown to increase the risk of death by 26%.4 Loneliness is also associated with additional cost to the healthcare infrastructure. For instance, in the USA, an additional US$6.7 billion are spent in expenses because of loneliness.5 Similarly, in terms of costs, loneliness costs US$154 billion to employers in terms of absenteeism and loss in productivity.5
Loneliness must be understood separately from the interlinked concept of isolation. Loneliness is the subjective perception of an individual’s actual and desired social connections and relationships. While social isolation on the other hand is an objective phenomenon of lack of social connections, be that with immediate family or larger community. The route to loneliness can vary from one person to the other. The relation between loneliness and social isolation with the determinant factors is complex and often bidirectional. For some people, loneliness is a prevalent state of mind. This can be the result of genetic influence or early adversity. Depression and social anxiety may lead some others to be lonely. While for some people, it may be the result of trauma and internalised stigma. These factors as well as others such as old age, economic status and negative self-image may contribute to loneliness.6 7 While transient loneliness can result in emotional distress, it is commonplace and can be overcome. But loneliness can become chronic and permanent because of lack of consistent and constant social connectedness, thus altering neurobiological and behavioural patterns and mechanisms.8
There are several intervention strategies for fighting off loneliness. These strategies are meant to mitigate the long-term mental health effects of loneliness. There have been technological interventions ranging from using social media for connectivity to videoconferencing and community-oriented interventions. The effectiveness of technology-based interventions to fight off loneliness has been studied in the literature. Authors in Döring et al’s study9 showed that communication-based technologies can reduce loneliness and isolation in older people. It was reported by Choi and Lee10 that older people using social platforms changed their behaviour through use of multifaceted technology platforms. These platforms enable social participation, cognition, nutrition and physical activity.
The recent trend in technology is towards the use of artificial intelligence (AI)-based conversational agents and chatbots. Xie and Pentina11 found out through survey of patients already using a chatbot that patients form an emotional attachment with the chatbots if the patients perceive the chatbots’ response to offer emotional support. The role of chatbots in interventions for mental health was studied by Boucher et al12 and the potential challenges were discussed. Similarly, Abd-Alrazaq et al13 found that patients have overall feeling of satisfaction with the use of chatbot through a systematic review. However, Manis and Matis14 also pointed out the benefits of technology and chatbots particularly in terms of long-term isolation. AI-based chatbots, thus, can counter loneliness given they are complemented with other interventions.
As mentioned, digital technology interventions are shown to help in reducing the feeling of loneliness, there is a need to understand the prevalence of loneliness to devise such technology-based and community-oriented strategies. This can be understood through a loneliness map. Health informatics is applied to the area of digital health and study of loneliness through various studies. There are other studies which use social media data to gain a detailed insight into the problem of loneliness.9 Building on the tools of health informatics and social media analysis of mental health, digital health and loneliness a detailed global map of loneliness can act as a guideline and as the foundational grounding for intervention strategies. Loneliness is a big burden on global public health spending, global loss of accumulated number of days of work as well as affecting the quality of life. What we need more in understanding of loneliness is from the health informatics perspective. The map, a part of which this paper will develop, will be our first towards loneliness informatics.
Through the global loneliness map, the approach is to explore the relationship between loneliness and mental health issues. This map can be used to zoom in on a country where the relationship of loneliness with negative sentiment is higher to derive further analysis. We will also provide a correlation of linguistic features representing respective personal and social categories, such as relationships, sleep habits and emotional dysregulation for different categories to show how these can vary across countries. This can be helpful in recognising and understanding the nature of association of loneliness with negative sentiments in different categories. The loneliness map will monitor the relationship of loneliness to mental health issues across the globe by analysing the data collected through ML and AI tools. The surveillance data on the relationship between loneliness and mental health issues can be used to design policy programmes to build a community of support.
This paper presents a proof of concept for such a global loneliness map. Developing the loneliness map which is exhaustive and backed by rigorous evidence is a time and resource intensive project. This paper presents the first step towards it. The remaining parts of the map, that is, using multiple data sources and analysing different regions and countries exhaustively will be carried out stepwise. Rather than using multiple sources of data we first focus on Twitter because the data it provides is diverse as well as from a limited dataset multiple insights can be gained as the users have to express themselves in limited characters. Moreover, we start with the USA. We collected data mentioning keywords associated with loneliness and found out that the data returned by the Twitter algorithm has more tweets from the USA. We collected global data on loneliness as we wanted a snapshot into loneliness rather than exhaustive analysis of one country. We retrieved the US cities which have more than 10 000 tweets each related to loneliness.
To develop the first part of loneliness map, we used sentiment analysis of Twitter data through natural a language processing tool. This is based on psycholinguistic model of understanding mental health issues. The collected tweets are stored in a database and then sentiment analysis using valence aware dictionary for sentiment reasoning (VADER)15 tool from the natural language toolkit (NLTK) is carried out. VADER is lexicon and rule-based model for sentiment analysis. The lexicon-based approach means that the algorithm is constructed using a dictionary which contains a detailed list of sentiment features. In addition, VADER also complements the lexicon-based dictionary with grammatical rules which are heuristic in nature. These rules complement the lexicon-based sentiment analysis to determine polarity of the sentiment. The result of the sentiment analysis tool gives us an indication of loneliness in the particular dataset.