Head versus heart: social media reveals differential language of loneliness from depression

Understanding Loneliness Through Analysis of Twitter and Reddit Data: Comparative Study

BACKGROUND

Loneliness is a global public health issue contributing to a variety of mental and physical health issues. It increases the risk of life-threatening conditions and contributes to the burden on the economy in terms of the number of productive days lost. Loneliness is a highly varied concept, which is associated with multiple factors.

OBJECTIVE

This study aimed to understand loneliness through a comparative analysis of loneliness data on Twitter and Reddit, which are popular social media platforms. These platforms differ in terms of their use, as Twitter allows only short posts, while Reddit allows long posts in a forum setting.

METHODS

We collected global data on loneliness in October 2022. Twitter posts containing the words "lonely," "loneliness," "alone," "solitude," and "isolation" were collected. Reddit posts were extracted in March 2023. Using natural language processing techniques (valence aware dictionary for sentiment reasoning [VADER] tool from the natural language toolkit [NLTK]), the study identified and extracted relevant keywords and phrases related to loneliness from user-generated content on both platforms. The study used both sentiment analysis and the number of occurrences of a topic. Quantitative analysis was performed to determine the number of occurrences of a topic in tweets and posts, and overall meaningful topics were reported under a category.

RESULTS

The extracted data were subjected to comparative analysis to identify common themes and trends related to loneliness across Twitter and Reddit. A total of 100,000 collected tweets and 10,000 unique Reddit posts, including comments, were analyzed. The results of the study revealed the relationships of various social, political, and personal-emotional themes with the expression of loneliness on social media. Both platforms showed similar patterns in terms of themes and categories of discussion in conjunction with loneliness-related content. Both Reddit and Twitter addressed loneliness, but they differed in terms of focus. Reddit discussions were predominantly centered on personal-emotional themes, with a higher occurrence of these topics. Twitter, while still emphasizing personal-emotional themes, included a broader range of categories. Both platforms aligned with psychological linguistic features related to the self-expression of mental health issues. The key difference was in the range of topics, with Twitter having a wider variety of topics and Reddit having more focus on personal-emotional aspects.

CONCLUSIONS

Reddit posts provide detailed insights into data about the expression of loneliness, although at the cost of the diversity of themes and categories, which can be inferred from the data. These insights can guide future research using social media data to understand loneliness. The findings provide the basis for further comparative investigation of the expression of loneliness on different social media platforms and online platforms.

How developments in natural language processing help us in understanding human behaviour

The ways people use language can reveal clues to their emotions, social behaviours, thinking styles, cultures and the worlds around them. In the past two decades, research at the intersection of social psychology and computer science has been developing tools to analyse natural language from written or spoken text to better understand social processes and behaviour. The goal of this Review is to provide a brief overview of the methods and data currently being used and to discuss the underlying meaning of what language analyses can reveal in comparison with more traditional methodologies such as surveys or hand-scored language samples.

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure

Introduction

The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts.

Aim

This study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count).

Materials and methods

A sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them.

Assessment tools

(1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 - Linguistic Inquiry and Word Count - a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use.

Results

Depressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form - not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money.

Conclusion

Depressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.

Disentangling boredom from depression using the phenomenology and content of involuntary autobiographical memories

Recurrent involuntary autobiographical memories (IAMs) are memories retrieved unintentionally and repetitively. We examined whether the phenomenology and content of recurrent IAMs could differentiate boredom and depression, both of which are characterized by affective dysregulation and spontaneous thought. Participants (n = 2484) described their most frequent IAM and rated its phenomenological properties (e.g., valence). Structural topic modeling, a method of unsupervised machine learning, identified coherent content within the described memories. Boredom proneness was positively correlated with depressive symptoms, and both boredom proneness and depressive symptoms were correlated with more negative recurrent IAMs. Boredom proneness predicted less vivid recurrent IAMs, whereas depressive symptoms predicted more vivid, negative, and emotionally intense ones. Memory content also diverged: topics such as relationship conflicts were positively predicted by depressive symptoms, but negatively predicted by boredom proneness. Phenomenology and content in recurrent IAMs can effectively disambiguate boredom proneness from depressive symptoms in a large sample of undergraduate students from a racially diverse university.

Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?

BACKGROUND

Relatively little is known about how communication changes as a function of depression severity and interpersonal closeness. We examined the linguistic features of outgoing text messages among individuals with depression and their close- and non-close contacts.

METHODS

419 participants were included in this 16-week-long observational study. Participants regularly completed the PHQ-8 and rated subjective closeness to their contacts. Text messages were processed to count frequencies of word usage in the LIWC 2015 libraries. A linear mixed modeling approach was used to estimate linguistic feature scores of outgoing text messages.

RESULTS

Regardless of closeness, people with higher PHQ-8 scores tended to use more differentiation words. When texting with close contacts, individuals with higher PHQ-8 scores used more first-person singular, filler, sexual, anger, and negative emotion words. When texting with non-close contacts these participants used more conjunctions, tentative, and sadness-related words and fewer first-person plural words.

CONCLUSION

Word classes used in text messages, when combined with symptom severity and subjective social closeness data, may be indicative of underlying interpersonal processes. These data may hold promise as potential treatment targets to address interpersonal drivers of depression.