Loneliness Episodes: A Japanese Dataset for Loneliness Detection and Analysis (2024)

Abstract: Loneliness, a significant public health concern, is closely connected to both physical and mental well-being. Hence, detection and intervention for individuals experiencing loneliness are crucial. Identifying loneliness in text is straightforward when it is explicitly stated but challenging when it is implicit. Detecting implicit loneliness requires a manually annotated dataset because whereas explicit loneliness can be detected using keywords, implicit loneliness cannot be. However, there are no freely available datasets with clear annotation guidelines for implicit loneliness. In this study, we construct a freely accessible Japanese loneliness dataset with annotation guidelines grounded in the psychological definition of loneliness. This dataset covers loneliness intensity and the contributing factors of loneliness. We train two models to classify whether loneliness is expressed and the intensity of loneliness. The model classifying loneliness versus non-loneliness achieves an F1score of 0.833, but the model for identifying the intensity of loneliness has a low F1-score of 0.400, which is likely due to label imbalance and a shortage of a certain label in the dataset. We validate performance in another domain, specifically X (formerly Twitter), and observe a decrease. In addition, we propose improvement suggestions for domain adaptation.

heading: 1 Introduction
text: Loneliness has become a major global concern, affecting both mental and physical well-being. Previous research shows that loneliness constitutes a significant risk factor for both coronary heart disease and stroke (Valtorta et al., 2016). Another study shows that loneliness is a risk factor for morbidity and mortality (Luo et al., 2012). It also increases health-risk behaviors (Shankar et al., 2011). Hence, addressing the issue of loneliness is crucial.
The degree of negative impact varies depending on the intensity of loneliness. Beutel et al. (2017) indicate that the greater the intensity of loneliness, the higher the proportion of individuals experiencing depression and suicidal ideation. Furthermore, it is noteworthy that over half of individuals experiencing strong loneliness report depressive symptoms. Lee et al. (2019) demonstrate that people who feel more loneliness have lower resilience, optimism, and mental well-being. For these reasons, classifying the intensity of loneliness is as important for intervention as discovering individuals experiencing loneliness.
Identifying loneliness and intervening for these people are crucial. People who feel lonely tend to have less contact with supportive family and friends than those who do not, and they often use social media more frequently (Lampraki et al., 2022). Numerous previous studies have created corpora using keyword-based approaches on social media (Andy et al., 2022; Kiritchenko et al., 2020). These corpora only considered loneliness when it was explicitly mentioned, using specific keywords to label posts. This limits finding posts where loneliness is stated implicitly rather than explicitly, possibly leading to underreporting because users hesitate to share loneliness posts due to social stigma around admitting loneliness. An example of loneliness stated explicitly is “I feel lonely and isolated at work,” where the explicit expressions “isolate” and “lonely” are included. In contrast, an example of loneliness stated implicitly is “I am being ignored by various people at school,” where there is no direct expression, but loneliness can be inferred from “being ignored.”
Prior work has created a dataset that could also encompass cases where loneliness was implicitly expressed. A recent study collected text and psychological characteristics (Nakai et al., 2023), but this text is not publicly available. On the contrary, a publicly available dataset has been pub-
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lished (Jiang et al., 2022). However, their study has two limitations: the annotation process relies on subjectivity due to the absence of a clear definition of loneliness, and they did not differentiate by intensity.
This study aims to provide a publicly available Japanese loneliness dataset with clear annotation guidelines. We created annotation guidelines based on the psychological definition of loneliness and annotated a corpus that is publicly available. Moreover, we labeled whether loneliness is expressed, its intensity in detecting people suffering from severe loneliness, and the contributing factors.
To create the loneliness dataset, we used a Japanese short episode corpus1, called LIFE STORY. This corpus is composed of episodes recalled from emotions collected through crowdsourcing. In line with previous work, creating datasets using social media would become difficult to utilize if API regulations change. Considering that the LIFE STORY corpus is freely available, it is advantageous for research from the perspective of reproducibility and ease of use.
Our contributions are as follows:
• We built a Japanese loneliness dataset2 by annotating an episode corpus, which is created for the detection and analysis of loneliness (Section 3);
• We constructed classifiers to determine whether loneliness is expressed and to assess the intensity of loneliness using our dataset (Section 4);
• We indicated the feasibility of domain adaptation, employing posts from X (Section 5).

heading: 2 Related Work

heading: 2.1 Definition of Loneliness and Social Needs
text: Many definitions of loneliness have been proposed (Taylor, 2020; Sullivan, 1953; Ma et al., 2020). However, they all share three important common points (Peplau and Perlman, 1982). First, loneliness arises from inadequacies in an individual’s social connections. Second, loneliness is subjective; it does not equate to objective social isolation. It is possible for an individual to be
1https://sociocom.naist.jp/life-story-data/ 2https://github.com/sociocom/
Japanese-Loneliness-Dataset
alone without experiencing loneliness, and conversely, one can feel lonely even when surrounded by a crowd. Third, loneliness is uncomfortable and distressing. The differences among various definitions of loneliness are due to the nature of social deficiency. One approach emphasizes social needs, and another approach emphasizes cognitive processes. The social needs approach suggests that individuals may experience loneliness without explicitly identifying themselves as lonely or consciously understanding the nature of their distress. In contrast, the cognitive approach focuses on the perceptions and self-reports of loneliness, paying attention to those who recognize themselves as lonely.
For interventions in loneliness, it is important to identify not only those who recognize their loneliness but also those who are emotionally distressed without recognizing it as loneliness. Therefore, when defining loneliness, we prioritize the social needs aspect. We introduced the definition of loneliness presented by Hawkley and Cacioppo (2010).
Social needs are conceptualized in various ways (Deci and Ryan, 2008; Lindenberg, 1996; Steverink et al., 2020). We have chosen the approach by Ormel et al. (1999), which is one of the most representative, and is composed of Affection, Behavioral Confirmation, and Status.

heading: 2.2 Loneliness Dataset
text: Research on constructing datasets related to implicit loneliness is limited (Nakai et al., 2023; Jiang et al., 2022)
tators determined whether it expressed loneliness. Posts judged as expressing loneliness were further annotated with duration, situation, interpersonal relationships, and interactions. However, the criteria for determining whether a post expresses loneliness are not clearly defined. In previous studies, the definition of loneliness for dataset construction has traditionally relied on the subjective judgment of annotators. In contrast, we attempted annotation based on a definition of loneliness that allows for objective interpretation. We believe that this initial attempt is crucial for detecting loneliness to prevent serious conditions such as depression and suicide.

heading: 3 A Japanese Dataset for Loneliness

heading: 3.1 Corpus
text: This study leverages a Japanese short episode corpus, called LIFE STORY3, for constructing the Japanese dataset. The LIFE STORY corpus, which is freely available, has been continuously collected since 2017, offering age, gender, and open-ended Japanese episodes associated with seven primary emotions: sadness, anxiety, anger, disgust, trust, surprise, and joy.
We focused on sadness and anxiety as emotions related to loneliness (Cacioppo et al., 2010; Mullarkey et al., 2018; Meltzer et al., 2012). In a preliminary study, we evaluated 50 episodes of each emotion to determine if they expressed loneliness based on the criteria specified in the annotation guidelines (details in Section 3.2.2). For sad episodes, out of 50 episodes, one annotator classified 12 episodes as expressing loneliness, while the other annotator classified 16 episodes. For anxious episodes, one annotator classified 1 episode as expressing loneliness, and the other annotator classified 0 episodes. Due to the very low relationship observed between anxiety and loneliness in this sample, we chose to annotate only sad episodes when creating the corpus. We annotated sad episodes extracted from the LIFE STORY corpus. Examples translated from Japanese of such episodes include: “I couldn’t purchase the desired item at the auction” and “I had to decline my friend’s invitations because I was short on money during Golden Week.”
3https://sociocom.naist.jp/life-story-data/

heading: 3.2 Label Definitions

heading: 3.2.1 Overview
text: We annotated the episodes in the LIFE STORY corpus with the following two steps:
Step 1 Loneliness or Non-Loneliness: We first labeled episodes to determine whether they express loneliness or not. Loneliness episodes were labeled as TRUE, while non-loneliness episodes were labeled as FALSE. Additionally, we also labeled unsatisfied social needs as loneliness factors. Social needs include Love and Affection, Social Recognition, and Self-esteem and Belonging.
Step 2 Intensity of Loneliness: For loneliness episodes, we further categorized the intensity levels of such expressions. The intensity of loneliness is either STRONG or WEAK.
Figure 1 illustrates the hierarchical labeling structure. For example, consider the episode: “I couldn’t get my parents’ approval and they started ignoring me.” This is labeled as TRUE for step 1 and WEAK for step 2 based on the criteria (details in Sections 3.2.2 and 3.2.3). Loneliness factors are labeled as unsatisfied social needs for Love and Affection and Social Recognition.

heading: 3.2.2 Loneliness or Non-Loneliness
text: This section details the annotation scheme used to identify loneliness episodes and categorize their loneliness factors. We adopted the definition of loneliness proposed by Hawkley and Cacioppo
(2010): Loneliness is defined as a distressing feeling that accompanies the perception that one’s social needs are not being met by the quantity or especially the quality of one’s social relationships.
Based on the definition, episodes in which any one of the social needs is not satisfied are judged to express loneliness. In other words, a single unsatisfied social need can lead to the judgment of loneliness. In psychology, it has been proposed that social needs are composed of Affection, Behavioral Confirmation, and Status (Ormel et al., 1999). For the purpose of this study, we have slightly adjusted these categories to better relate to loneliness and have classified them into three categories: Love and Affection, Social Recognition, and Self-esteem and Belonging. Status can be considered as a factor contributing to loneliness. However, when looking at the definitions, we can see that the definition of Status is actually derived from our definition of social needs rather than directly causing loneliness itself. Therefore, in this study we consider that the source of loneliness from Status arises from a lack of Self-esteem and Belonging, and Social Recognition. Below is a description of the social needs employed in this study:
Love and Affection This involves receiving affection from people one cares about (e.g., family, friends). It is derived from Affection (one of the social needs proposed by Ormel et al. (1999)), with the addition of “love” to enhance understanding. An example of a lack of Love and Affection is as follows: “I had a disagreement with my parents and lost touch with them.”
Social Recognition This involves receiving external validation and acceptance through one’s behavior in a social environment. It is derived from Behavioral Confirmation (one of the social needs proposed by Ormel et al. (1999)), which is the sense of approval by others. It depends on external evaluations. An example of a lack of Social Recognition is as follows: “Many of my friends at school are fashionable, but I don’t have the money to buy a variety of clothes, so I can’t be fashionable and join in the conversation.”
Self-esteem and Belonging This involves the internal sense of being accepted and valued within a group or society. It is also rooted
from Behavioral Confirmation. It depends on internal evaluations. An example of a lack of Self-esteem and Belonging is as follows: “I feel out of place because I am the only one without a Ph.D.”
Furthermore, we introduced an Others label to address situations where none of the specified unsatisfied social needs were identified, yet loneliness was perceived. The example is “It’s sad to spend every day feeling lonely and in a depressed state.” If an episode does not lack any of the social needs, we assigned only a FALSE label for loneliness.
To ensure consistent labeling within the dataset, we added two criteria for episodes labeled as FALSE. First, episodes where loneliness arises from an external source rather than the writer themselves are classified as FALSE. The example is “My wife said she doesn’t fit in at work and feels lonely.” Second, episodes lacking explicit vocabulary related to social connection are also labeled as FALSE. On this criterion, the example of TRUE is “I have a disagreement with my parents and have lost touch with them” because it involves both explicit social connection vocabulary (“parent” and “lost touch with”) and expresses loneliness. In contrast, the example of FALSE is “I had a dream about a sad event from the past, and the sadness came back to me when I remembered it”.

heading: 3.2.3 Intensity of Loneliness
text: We also assigned the intensity of loneliness to episodes classified as TRUE, distinguishing between two levels: STRONG and WEAK. STRONG denotes situations in which loneliness markedly disrupts daily life or is explicitly accompanied by the expression of negative emotions. In contrast, all episodes that are not labeled STRONG are labeled WEAK. STRONG episodes include sentences such as “I can’t work since my parents passed away” and “I can’t sleep at night because my beloved dog died.” Conversely, WEAK episodes include sentences such as “My family is busy with work and I’m lonely” and “I was sad when I had to transfer schools and say goodbye to my friends.”

heading: 3.3 Dataset Construction
text: Two annotators independently labeled the data. We preprocessed the sad episodes excerpted from the LIFE STORY corpus (May and August 2023
surveys) by removing noises (# and * symbols, which are used in preprocessing the LIFE STORY corpus) and excluding texts shorter than or equal to 10 characters. We also converted text to lowercase, normalized Unicode using NFKC, and replaced numbers with 0. The magnitude of numbers can be important for understanding loneliness; however, NLP models often struggle to handle these numbers effectively. Therefore, numbers were not used as a feature in this model.
Each annotator labeled a total of 600 episodes including 200 common episodes for interannotator agreement calculation. In cases where annotators encountered difficulty determining loneliness or non-loneliness, they labeled it as TRUE to prioritize recall. Moreover, when faced with uncertainty regarding the intensity, annotators assigned a label of WEAK to prioritize the precision of STRONG.
Our dataset contains 800 annotated episodes, along with an additional 200 common episodes for inter-annotator agreement calculation. We randomly sampled 100 episodes each from these 200, ensuring an equal proportion of labels from both annotators. These were combined with the remaining 800 episodes for a total of 1,000 episodes. The breakdown of labels is as follows: there are 350 episodes labeled as TRUE and 650 episodes labeled as FALSE. Among those labeled as TRUE, 25 are classified as STRONG and 325 as WEAK. In the original Japanese dataset, the average length of the texts was 28.6 characters, with a standard deviation of 25.3 characters and a median of 21.0 characters.

heading: 3.4 Inter-Annotator Agreement
text: To calculate the agreement between annotators, we used the agreement rate and Cohen’s κ coefficient (Cohen, 1960). The agreement rates for the labels are notably high, with 0.935 (187 out of 200) for determining loneliness or non-loneliness, 0.905 for social needs (181 out of 200), 0.984 (62 out of 63) for intensity among episodes determined as TRUE by the two annotators, and 0.905 (181 out of 200) encompassing all labels (loneliness or non-loneliness, social needs, intensity). Moreover, Cohen’s κ coefficient also indicates substantial agreement, measuring at 0.857 for loneliness or non-loneliness and 0.849 for intensity among episodes determined as TRUE by the two annotators. This suggests that we have constructed a consistent dataset and that our def-
inition is clear enough to understand, enabling researchers to easily expand the dataset.
We conducted a qualitative analysis to understand the limitations of the guidelines. Examples translated from Japanese where the annotations do not match are shown in Table 1
labeled as STRONG. This shortage results in a deflated F1-score, despite achieving a precision of 1.0 for the STRONG class.

heading: 4.3 Discussion
text: We conducted an error analysis on the test data. Table 3 shows examples translated from Japanese of episodes, ground truth labels, and predicted labels by Mlonely. Note that original examples and their transliterations are seen in in Appendix A. Examples (6)-(10) represent correct predictions by Mlonely, whereas examples (11)-(15) represent incorrect ones. Mlonely accurately classified the loneliness experienced when the relationship with the person who you loved drifts away, as exemplified in (6), as well as the loneliness resulting from the loss of family members or pets, as depicted in (7) and (8). However, Mlonely tended to misclassify instances where Social Recognition or Selfesteem and Belonging were unsatisfied, as seen in (11) and (12). When annotators label episodes involving Social Recognition and Self-esteem and Belonging, there is often inconsistency, which is considered difficult for Mlonely to predict. Furthermore, Mlonely occasionally misclassified the episode where someone else, not the author themselves, felt lonely as TRUE, as observed in (13). Moreover, there were several episodes, such as (14) and (15), where FALSE was the ground truth label but could potentially be labeled as TRUE by annotators. Episodes like these are challenging to
label even manually and are also difficult for machine learning models to predict.
Table 4 shows examples translated from Japanese of episodes, ground truth labels, and predicted labels by Minten. Note that original examples and their transliterations are seen in Appendix A. Examples (16)-(20) represent correct predictions by Minten, whereas examples (21)- (23) represent incorrect ones. Minten correctly classified the episode with a strong negative expression indicating a loss of trust in people as STRONG, as exemplified in (16), which is only episode classified as STRONG. Minten also correctly classified the episode mentioning only the death of a relative or a pet as WEAK in (17) and (18). In addition, Minten correctly classified episodes where there were no strong negative expressions and no interference with daily life as WEAK in (19) and (20). In contrast, Minten did not correctly classify some episodes with interference in daily life or strong negative expressions as STRONG, as seen in (21), (22), and (23). Annotators classify these episodes as STRONG using these words as clues, such as “sad every day” in (22) and “really sad” in (23), following the guidelines. Minten does not predict them as STRONG and is not learning in accordance with the guidelines. There are only 17 episodes of the label STRONG in the training data, which is insufficient for achieving consistent classification for Minten.

heading: 5 Prediction of Social Media Posts
text: To assess the feasibility of domain application, we evaluated the created models using X data.

heading: 5.1 Social Media Posts
text: We collected posts from Japan from July 1 to 31, 2022 by using the X (formerly Twitter) API5.
5https://developer.x.com/en/docs/twitter-api
We preprocessed the collected posts by removing emojis, URLs, mentions, RT, tweets from users with ‘bot’ in their username, and duplicate posts, in addition to the preprocessing steps performed on the LIFE STORY corpus. The number of posts after preprocessing is 750,240. To align with the sad category of the annotated LIFE STORY corpus used to fine-tune our BERT models (Mlonely and Minten), we conducted emotion analysis on X posts by creating a new model using the LIFE
STORY corpus with Naive Bayes (MultinomialNB 6 from scikit-learn) to extract posts expressing sadness. Through this process, we can better ensure that the sadness posts extracted from X, used as input, will be of the same nature as the data used for fine-tuning our models. We used the emotion categories of the corpus as ground truth labels and calculated the probabilities for classification into each emotion category. Note that for the preliminary experiment, we also constructed BERT trained on LIFE STORY corpus for emotion analysis. However, since the performance did not differ significantly from Naive Bayes, we used Naive Bayes due to its high interpretability. Subsequently, we extracted the posts classified as sad, which is 57,648 posts, and inputted them into Mlonely. We fed the posts classified as TRUE by Mlonely into Minten. Finally, 6,902 and 496 posts
6https://scikit-learn.org/stable/modules/ generated/sklearn.naive_bayes.MultinomialNB.html
were classified as TRUE and STRONG, respectively.

heading: 5.2 Results and Discussion
text: To evaluate the performance of the two models (Mlonely and Minten), we randomly sampled and manually annotated 300 posts: 150 posts classified as TRUE and 150 posts classified as STRONG. We calculated precision from these annotated posts. The reason for using precision as the evaluation metric instead of F1-score or accuracy is due to the inability to annotate on a large scale. To use F1score or accuracy, posts need to be annotated before prediction. Due to the extremely low number of posts classified as TRUE and STRONG within the data from X, a large number of annotations are necessary to properly evaluate it. Therefore, this time annotations were made after predictions, on posts labeled by the model as TRUE and STRONG to calculate precision. Calculating accuracy and F1-score is planned for future work. The resulting precision for Mlonely was 0.113, and for Minten it was 0.02, which is significantly lower compared to its performance on the LIFE STORY corpus. From those results, we can conclude that these models lacked the ability for domain adaptation for X posts. These inferior results can be attributed to the significant differences in syntax, vocabulary, and word usage between social media texts and the LIFE STORY episodes.
Table 5 lists examples translated from Japanese of posts predicted as TRUE by Mlonely, along with their ground truth labels. Note that original examples and their transliterations are listed in Appendix A. As seen in (24), Mlonely can detect loneliness when someone wants to meet but cannot. However, Mlonely often mistakenly predicts TRUE when influenced by expressions related to “death” because those words frequently occur in loneliness episodes in our dataset, as seen in (25). Many of the posts predicted as TRUE included greetings,
as seen in (26). As LIFE STORY corpus does not contain greetings, it appears that the model cannot predict accurately when the text contains only greetings.
Table 6 shows examples translated from Japanese of posts predicted as STRONG by Minten, along with their ground truth labels. Note that original examples and their transliterations are listed in Appendix A. As shown in (27), Minten correctly classified the posts expressing hardship over the loss of a mother with strong negative expressions as STRONG. Similar to the evaluation on the dataset, consistency in Minten was not observed.

heading: 6 Limitations
text: Based on the definition of loneliness, we classified whether the text expressed loneliness, but the emotions perceived by the readers and the writers may be different (Kajiwara et al., 2021; Ramos et al., 2022). Accordingly, we plan to collect texts and loneliness scores of their writers through crowdsourcing. In terms of completeness, we annotated episodes evoked by sadness; hence, loneliness that occurs alongside other emotions or loneliness that occurs independently of other emotions may not be captured. In addition, the loneliness dataset we created lacks a sufficient number of STRONG labels for learning. We plan to expand our dataset to secure an ample number of STRONG labels, thereby addressing the low recall issue in the model for classification of intensity. Regarding the guidelines, the disagreement among annotators often arises from the fact that different people perceive the same situation differently. This is evident in texts involving Social Recognition and Self-esteem and Belonging.
We created a BERT-based classifier, but using other models may result in higher performance. Considering the rapid development in recent years, it is also necessary to consider us-
ing generative models represented by GPT-4
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heading: A Appendix
text: Episode Social needs
(1)
Episode Gold Pred
(6)
Episode Gold Pred
(16)
Episode Gold Pred
(27) Twitter

heading: Future Work
text: Calculating accuracy and F1-score is planned for future work. The resulting precision for Mlonely was 0. 113, and for Minten it was 0. 02, which is significantly lower compared to its performance on the LIFE STORY corpus. From those results, we can conclude that these models lacked the ability for domain adaptation for X posts. These inferior results can be attributed to the significant differences in syntax, vocabulary, and word usage between social media texts and the LIFE STORY episodes. Table 5 lists examples translated from Japanese of posts predicted as TRUE by Mlonely, along with their ground truth labels. Note that original examples and their transliterations are listed in Appendix A. As seen in (24), Mlonely can detect loneliness when someone wants to meet but cannot. However, Mlonely often mistakenly predicts TRUE when influenced by expressions related to “death” because those words frequently occur in loneliness episodes in our dataset, as seen in (25). Many of the posts predicted as TRUE included greetings,
as seen in (26). As LIFE STORY corpus does not contain greetings, it appears that the model cannot predict accurately when the text contains only greetings. Table 6 shows examples translated from Japanese of posts predicted as STRONG by Minten, along with their ground truth labels. Note that original examples and their transliterations are listed in Appendix A. As shown in (27), Minten correctly classified the posts expressing hardship over the loss of a mother with strong negative expressions as STRONG. Similar to the evaluation on the dataset, consistency in Minten was not observed