Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count

Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter

Introduction

The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints. As a result, there is an increasing interest in accessing social media data to study the effects of wildfires on mental health.

Methods

In this study, we focused on Twitter users affected by the California Tubbs Fire in 2017 to extract data signals related to emotional well-being and mental health. Our analysis aimed to investigate tweets posted during the Tubbs Fire disaster to gain deeper insights into their impact on individuals. Data were collected from October 8 to October 31, 2017, encompassing the peak activity period. Various analytical methods were employed to explore word usage, sentiment, temporal patterns of word occurrence, and emerging topics associated with the unfolding crisis.

Results

The findings show increased user engagement on wildfire-related Tweets, particularly during nighttime and early morning, especially at the onset of wildfire incidents. Subsequent exploration of emotional categories using Linguistic Inquiry and Word Count (LIWC) revealed a substantial presence of negative emotions at 43.0%, juxtaposed with simultaneous positivity in 23.1% of tweets. This dual emotional expression suggests a nuanced and complex landscape, unveiling concerns and community support within conversations. Stress concerns were notably expressed in 36.3% of the tweets. The main discussion topics were air quality, emotional exhaustion, and criticism of the president's response to the wildfire emergency.

Discussion

Social media data, particularly the data collected from Twitter during wildfires, provides an opportunity to evaluate the psychological impact on affected communities immediately. This data can be used by public health authorities to launch targeted media campaigns in areas and hours where users are more active. Such campaigns can raise awareness about mental health during disasters and connect individuals with relevant resources. The effectiveness of these campaigns can be enhanced by tailoring outreach efforts based on prevalent issues highlighted by users. This ensures that individuals receive prompt support and mitigates the psychological impacts of wildfire disasters.

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.

Childhood conduct problems and parent-child talk during social and nonsocial play contexts: a naturalistic home-based experiment

Parent-child interactions are a critical pathway to emotion socialization, with disruption to these processes associated with risk for childhood behavior problems. Using computational linguistics methods, we tested whether (1) play context influenced parent-child socioemotional language, and (2) child conduct problems or callous-unemotional traits were associated with patterns of socioemotional or nonsocial language across contexts. Seventy-nine parent-child dyads (children, 5-6 years old) played a socioemotional skills ("social context") or math ("nonsocial context") game at home. We transcribed and analyzed game play, which had been audio recorded by participants. The social context elicited more socioemotional and cognitive words, while the nonsocial context elicited more mathematical words. The use of socioemotional language by parents and children was more strongly correlated in the social context, but context did not moderate the degree of correlation in cognitive or mathematical word use between parents and children. Children with more conduct problems used fewer socioemotional words in the social context, while children with higher callous-unemotional traits used fewer cognitive words in both contexts. We highlight the role of context in supporting socioemotionally rich parent-child language interactions and provide preliminary evidence for the existence of linguistic markers of child behavior problems. Our results also inform naturalistic assessments of parent-child interactions and home-based interventions for parents and children facing socioemotional or behavioral challenges.

Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach

BACKGROUND

Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences.

OBJECTIVE

The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings.

METHODS

We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ≥18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs.

RESULTS

Overall, 250 postings were included, and 200 (80%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs.

CONCLUSIONS

OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively.

Public health messages during a global emergency through an online community: a discourse and sentiment analysis

The growing popularity of social media and its ubiquitous presence in our lives brings associated risks such as the spread of mis- and disinformation, particularly when these may be unregulated in times of global crises. Online communities are able to provide support by enabling connection with others and also provide great potential for dynamic interaction and timely dissemination of information compared with more traditional methods. This study evaluates interactions within the Essex Coronavirus Action/Support Facebook private group, which set out to prevent the spread of COVID-19 infection by informing Essex residents of guidance and helping vulnerable individuals. At the outset, 18 community administrators oversaw the group, which attracted approximately 37,900 members. Longitudinal Facebook group interactions across five periods spanning the UK lockdowns 2020-2021 were analysed using psychological discourse analysis and supplementary computed-mediated analysis to further explore sentiment and linguistic features. The findings endorsed that the group provided a protected space for residents to express their feelings in times of crises and an opportunity to address confusion and concern. The effective communication of public health messages was facilitated by promoting desired interaction and the construction of group identities. Administrators worked with group members to achieve a shared understanding of others' perspectives and the COVID-19 evidence base, which led to a mobilisation of the provision of support in the community. This was accomplished through the application of rhetorical and interactional devices. This study demonstrates how online groups can employ discursive strategies to engage audiences, build cohesion, provide support, and encourage health protective behaviours. This has implications for public health teams in terms of designing, implementing, or evaluating such interventions.

A 20-year review comparing the use of 'schizophrenia' and 'psychosis' in UK newspapers from 2000 to 2019: Implications for stigma reduction

BACKGROUND

There have been increasing calls for schizophrenia to be renamed to reduce negative associations with the diagnosis. 'Psychosis' is one proposed alternative, yet some research suggests this has greater negative connotations. This study compared the use of both terms within UK newspapers longitudinally to assess which is more negatively portrayed in the media.

STUDY DESIGN

Using LexisLibrary News, six UK newspaper archives were searched for the terms 'schizophrenia' and 'psychosis' in articles published between 2000 and 2019; the included article total was n = 9802. Articles were analysed using Linguistic Inquiry and Word Count software.

STUDY RESULTS

A two-level multilevel model was created of newspaper articles nested within newspapers. Adding predictors revealed that diagnostic term was a significant predictor of the emotional tone of articles (β = -2.30, SE = 0.51, p < .001, 95 % CI -3.30 to -1.30), with 'psychosis' having a more negative tone than 'schizophrenia.' Tabloids were shown to have a significantly more negative tone than broadsheets (β = -9.32, SE = 1.04, p < .001, 95 % CI -11.36 to -7.28), and emotional tone of writing had become more negative over time (β = -0.26, SE = 0.04, p < .001, 95 % CI -0.34 to -0.18).

CONCLUSIONS

These findings suggest 'psychosis' is associated with greater negativity than 'schizophrenia' within UK newspapers, and may be a poor substitute term to reduce stigma. As article tone worsened for both diagnoses over time, increased stigma reduction campaigns are required to target negativity within the media.

How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media

The rise of cyberbullying has been of great concern for the general public. This study aims to explore public attitudes towards cyberbullying on Chinese social media. Cognition and emotion are important components of attitude, and this study innovatively used text analysis to extract the cognition and emotion of the posts. We used a web crawler to collect 53,526 posts related to cyberbullying in Chinese on Sina Weibo in a month, where emotions were detected using the software “Text Mind”, a Chinese linguistic psychological text analysis system, and the content analysis was performed using the Latent Dirichlet Allocation topic model. Sentiment analysis showed the frequency of negative emotion words was the highest in the posts; the frequency of anger, anxiety, and sadness words decreased in turn. The topic model analysis identified three common topics about cyberbullying: critiques on cyberbullying and support for its victims, rational expressions of anger and celebrity worship, and calls for further control. In summary, this study quantitatively reveals the negative attitudes of the Chinese public toward cyberbullying and conveys specific public concerns via three common topics. This will help us to better understand the demands of the Chinese public so that targeted support can be proposed to curb cyberbullying.

Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment

Internet-Delivered Psychological Treatments (IDPT) consider the mental problems based on Internet interaction. As the increases of the pandemic, more online tools are then widely used to result in evidence-based mental health serves. This increase helps to cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help to solve mental health issues speedily. In this research, we propose a fuzzy contrast-based model that uses the attention network for positional weighted words, classifies mental patient authored text into distinct symptoms. After that, the trained embedding is then used to label the mental data. Then attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes. The method is compared with non-embedding and traditional techniques to demonstrate the proposed model. From the experiments, the feature vector can achieve a high ROC-Curve of 0.82 with nine symptoms problems.

Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment

With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts.