Patterns and Longitudinal Changes in Negative Emotions of People with Depression on Sina Weibo

Understanding Social Media Information Sharing in Individuals with Depression: Insights from the Elaboration Likelihood Model and Schema Activation Theory

Purpose

How individuals engage with social media can significantly impact their psychological well-being. This study examines the impact of social media interactions on mental health, grounded in the frameworks of the Elaboration Likelihood Model and Schema Activation Theory. It aims to uncover behavioral differences in information sharing between the general population and individuals with depression, while also elucidating the psychological mechanisms underlying these disparities.

Methods

A pre-experiment (N=30) and three experiments (Experiment 1a N=200, Experiment 1b N=180, Experiment 2 N=128) were executed online. These experiments investigated the joint effects of information quality, content valence, self-referential processing, and depression level on the intention to share information. The research design incorporated within-subject and between-subject methods, utilizing SPSS and SPSS Process to conduct independent sample t-tests, two-factor ANOVA analyses, mediation analyses, and moderated mediation analyses to test our hypotheses.

Results

Information quality and content valence significantly influence sharing intention. In scenarios involving low-quality information, individuals with depression are more inclined to share negative emotional content compared to the general population, and this tendency intensifies with the severity of depression. Moreover, self-referential processing acts as a mediator between emotional content and intention to share, yet this mediation effect weakens as the severity of depression rises.

Conclusion

Our study highlights the importance of promoting viewpoint diversity and breaking the echo chamber effect in social media to improve the mental health of individuals with depression. To achieve this goal, tailoring emotional content on social media could be a practical starting point for practice.

Effect of interpersonal psychotherapy on social functioning, overall functioning and negative emotions for depression: A meta-analysis

BACKGROUND

Interpersonal psychotherapy (IPT) has been widely used for depression. However, current studies of IPT have been restricted to depressive symptoms, and the results for improving social functioning were controversial.

METHODS

A comprehensive literature search of randomized controlled trials (RCTs) was conducted through eleven databases. Data analysis was performed by RevMan5.3, and effects were summarized by using a random effects model of mean differences with 95 % confidence intervals.

RESULTS

From 2443 records, eleven studies met inclusion and exclusion criteria were used for meta-analysis. The results showed that IPT had significant effects on improving social functioning (SMD: -0.53, 95 % CI: -0.80 to -0.26), reducing depression (SMD: -0.49, 95 % CI: -0.80 to -0.19) and anxiety (SMD: -0.90, 95 % CI: -1.28 to -0.52), but the effect on the overall functioning (SMD: -0.37, 95 % CI: -0.73 to -0.01) is not obvious. Moreover, subgroup analysis showed that IPT was effective in improving social functioning in adolescent depression (SMD: -0.35, 95 % CI: -0.58 to -0.13) and perinatal depression (SMD: -1.01, 95 % CI: -1.35 to -0.67), while there was no significant difference in the adult depression group (SMD: -0.39, 95 % CI: -1.05 to 0.27).

LIMITATION

The blind method cannot be carried out in most studies due to the particularity of psychotherapy, heterogeneity in some results.

CONCLUSION

IPT has a significant effect on improving social functioning and reducing depression and anxiety, while the effect on overall functioning requires further research. Overall, IPT is one of the effective nonpharmacological treatments for depression.

What Are the Characteristics of User Texts and Behaviors in Chinese Depression Posts?

Social media platforms provide unique insights into mental health issues, but a large number of related studies have focused on English text information. The purpose of this paper is to identify the posting content and posting behaviors of users with depression on Chinese social media. These clues may suggest signs of depression. We created two data sets consisting of 130 users with diagnosed depression and 320 other users that were randomly selected. By comparing and analyzing the two data sets, we can observe more closely how users reveal their signs of depression on Chinese social platforms. The results show that the distribution of some Chinese speech users with depression is significantly different from that of other users. Emotional sadness, fear and disgust are more common in the depression class. For personal pronouns, negative words and interrogative words, there are also great differences between the two data sets. Using topic modeling, we found that patients mainly discussed seven topics: negative emotion fluctuation, disease treatment and somatic responses, sleep disorders, sense of worthlessness, suicidal extreme behavior, seeking emotional support and interpersonal communication. The depression class post negative polarity posts much more frequently than other users. The frequency and characteristics of posts also reveal certain characteristics, such as sleep problems and reduced self-disclosure. In this study, we used Chinese microblog data to conduct a detailed analysis of the users showing depression signs, which helps to identify more patients with depression. At the same time, the study can provide a further theoretical basis for cross-cultural research of different language groups in the field of psychology.

Characteristics of High Suicide Risk Messages From Users of a Social Network-Sina Weibo "Tree Hole"

BACKGROUND

People with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and "Zoufan" is one of the largest depression "Tree Holes." To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the "Zoufan" tree hole via artificial intelligence robots.

OBJECTIVE

To explore characteristics of time, content and suicidal behaviors by analyzing high suicide risk comments in the "Zoufan" tree hole.

METHODS

Knowledge graph technology was used to screen high suicide risk comments in the "Zoufan" tree hole. Users' level of activity was analyzed by calculating the number of messages per hour. Words in messages were segmented by a Jieba tool. Keywords and a keywords co-occurrence matrix were extracted using a TF-IDF algorithm. Gephi software was used to conduct keywords co-occurrence network analysis.

RESULTS

Among 5,766 high suicide risk comments, 73.27% were level 7 (suicide method was determined but not the suicide date). Females and users from economically developed cities are more likely to express suicide ideation on social media. High suicide risk users were more active during nighttime, and they expressed strong negative emotions and willingness to end their life. Jumping off buildings, wrist slashing, burning charcoal, hanging and sleeping pills were the most frequently mentioned suicide methods. About 17.55% of comments included suicide invitations. Negative cognition and emotions are the most common suicide reason.

CONCLUSION

Users sending high risk suicide messages on social media expressed strong suicidal ideation. Females and users from economically developed cities were more likely to leave high suicide risk comments on social media. Nighttime was the most active period for users. Characteristics of high suicide risk messages help to improve the automatic suicide monitoring system. More advanced technologies are needed to perform critical analysis to obtain accurate characteristics of the users and messages on social media. It is necessary to improve the 24-h crisis warning and intervention system for social media and create a good online social environment.

Capturing the Trajectory of Psychological Status and Analyzing Online Public Reactions During the Coronavirus Disease 2019 Pandemic Through Weibo Posts in China

When a major, sudden infectious disease occurs, people tend to react emotionally and display reactions such as tension, anxiety, fear, depression, and somatization symptoms. Social media played a substantial awareness role in developing countries during the outbreak of coronavirus disease 2019 (COVID-19). This study aimed to analyze public opinion regarding COVID-19 and to explore the trajectory of psychological status and online public reactions to the COVID-19 pandemic by examining online content from Weibo in China. This study consisted of three steps: first, Weibo posts created during the pandemic were collected and preprocessed on a large scale; second, public sentiment orientation was classified as "optimistic/pessimistic/neutral" orientation via natural language processing and manual determination procedures; and third, qualitative and quantitative analyses were conducted to reveal the trajectory of public psychological status and online public reactions during the COVID-19 pandemic. Public psychological status differed in different periods of the pandemic (from December 2019 to May 2020). The newly confirmed cases had an almost 1-month lagged effect on public psychological status. Among the 15 events with high impact indexes or related to government decisions, there were 10 optimism orientation > pessimism orientation (OP) events (2/3) and 5 pessimism orientation > optimism orientation (PO) events (1/3). Among the top two OP events, the high-frequency words were "race against time" and "support," while in the top two PO events, the high-frequency words were "irrationally purchase" and "pass away." We proposed a hypothesis that people developed negative self-perception when they received PO events, but their cognition was developed by how these external stimuli were processed and evaluated. These results offer implications for public health policymakers on understanding public psychological status from social media. This study demonstrates the benefits of promoting psychological healthcare and hygiene activity in the early period and improving risk perception for the public based on public opinion and the coping abilities of people. Health managers should focus on disseminating socially oriented strategies to improve the policy literacy of Internet users, thereby facilitating the disease prevention work for the COVID-19 pandemic and other major public events.

A Comparative Study of Online Depression Communities in China

Online communities have become a tool for researchers to understand and help individuals with depression. According to their operation mode in terms of management, communities can be divided into management depression communities (MDCs) and lacking-management depression communities (LDCs). This study aimed to investigate the characteristics and impact of LDCs in comparison with MDCs. All postings from the previous year were collected from the LDC and MDC. Keywords were extracted and coded to identify the themes, and a text classifier was built to identify the type of emotions and social support expressed in the postings. Community members were then clustered to explore their different participation patterns. We found that in the LDC, the expression of negative emotions was the most popular theme, there was a lack of information about the treatment of depression and a lack of social support providers, the level of engagement of providers was low, and support seekers did not receive attention. These results reveal the need for community management and can be used to develop more effective measures to support members of online depression communities.