Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing

Resilience in Web-Based Mental Health Communities: Building a Resilience Dictionary With Semiautomatic Text Analysis

Background

Resilience is an accepted strengths-based concept that responds to change, adversity, and crises. This concept underpins both personal and community-based preventive approaches to mental health issues and shapes digital interventions. Online mental health peer-support forums have played a prominent role in enhancing resilience by providing accessible places for sharing lived experiences of mental issues and finding support. However, little research has been conducted on whether and how resilience is realized, hindering service providers’ ability to optimize resilience outcomes.

Objective

This study aimed to create a resilience dictionary that reflects the characteristics and realization of resilience within online mental health peer-support forums. The findings can be used to guide further analysis and improve resilience outcomes in mental health forums through targeted moderation and management.

Methods

A semiautomatic approach to creating a resilience dictionary was proposed using topic modeling and qualitative content analysis. We present a systematic 4-phase analysis pipeline that preprocesses raw forum posts, discovers core themes, conceptualizes resilience indicators, and generates a resilience dictionary. Our approach was applied to a mental health forum run by SANE (Schizophrenia: A National Emergency) Australia, with 70,179 forum posts between 2018 and 2020 by 2357 users being analyzed.

Results

The resilience dictionary and taxonomy developed in this study, reveal how resilience indicators (ie, “social capital,” “belonging,” “learning,” “adaptive capacity,” and “self-efficacy”) are characterized by themes commonly discussed in the forums; each theme’s top 10 most relevant descriptive terms and their synonyms; and the relatedness of resilience, reflecting a taxonomy of indicators that are more comprehensive (or compound) and more likely to facilitate the realization of others. The study showed that the resilience indicators “learning,” “belonging,” and “social capital” were more commonly realized, and “belonging” and “learning” served as foundations for “social capital” and “adaptive capacity” across the 2-year study period.

Conclusions

This study presents a resilience dictionary that improves our understanding of how aspects of resilience are realized in web-based mental health forums. The dictionary provides novel guidance on how to improve training to support and enhance automated systems for moderating mental health forum discussions.

Exploring Young Millennials’ Motivations for Grieving Death Through Social Media

Past research has explored social media grief; however, the motivations for using a range of social media sites, specifically by young millennials, to grieve death fail to be explored expansively in existing thanatology research. Fourteen young millennials participated in individual semi-structured interviews, specifically questioning their motivations for using social media sites to grieve. The interviews were analysed using the thematic analysis framework identified by Braun and Clarke (2013). Four themes were generated: online influence, to announce the death, personal benefit and the hypocrisy of online mourning. The online influence theme suggests that individuals are motivated to grieve due to online influence and pressure. The personal benefit theme suggested social media present many benefits for the bereaved, including continuing bonds, which motivated them to use these platforms. The analysis also indicated that within the motivations there was hypocrisy regarding how young millennials perceive their grief posting activity when compared to others.

How social media data are being used to research the experience of mourning: A scoping review

Background

Increasingly, people are using social media (SM) to express grief, and researchers are using this data to investigate the phenomenon of mourning. As this research progresses, it is important to understand how studies are being conducted and how authors are approaching ethical challenges related to SM data.

Objective

The aim of this review was to explore how SM data are being used to research experiences of mourning through the following questions: a) ‘Which topics related to mourning are being studied?’; b) ‘What study designs have been used to analyse SM data’; c) ‘What type of data (natural or generated) have been used?’; and d) ‘How are ethical decisions being considered?’.

Methods

The JBI Scoping Review methodology guided this review. Eligibility criteria were determined using the PCC framework, and relevant key words and phrases derived from these criteria were used to search eight databases in September 2021 (CINAHL, Embase, LILACS, OpenGrey, ProQuest, PsycINFO, PubMed and Scopus). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines were used to report the results of this review.

Results

Database searches resulted in 3418 records, of which, 89 met eligibility criteria. Four categories of grief and mourning were identified. Most records were qualitative in nature and used natural data. Only 20% of records reported ethics approval by an Institutional Review Board, with several including measures to protect participants, for example, using pseudonyms.

Conclusions

This unique review mapped the diverse range of mourning-related topics that have been investigated using SM data and highlighted the variability in approaches to data analysis. Ethical concerns relating to SM data collection are identified and discussed. This is an emerging and rapidly changing field of research that offers new opportunities and challenges for exploring the phenomenon of mourning.

Sadness, despair and anger when a patient dies alone from COVID-19: A thematic content analysis of Twitter data from bereaved family members and friends

BACKGROUND

To inform clinical practice and policy, it is essential to understand the lived experience of health and social care policies, including restricted visitation policies towards the end of life.

AIM

To explore the views and experiences of Twitter social media users who reported that a relative, friend or acquaintance died of COVID-19 without a family member/friend present.

DESIGN

Qualitative content analysis of English-language tweets.

DATA SOURCES

Twitter data collected 7-20th April 2020. A bespoke software system harvested selected publicly-available tweets from the Twitter application programming interface. After filtering we hand-screened tweets to include only those referring to a relative, friend or acquaintance who died alone of COVID-19. Data were analysed using thematic content analysis.

RESULTS

9328 tweets were hand-screened; 196 were included. Twitter users expressed sadness, despair, hopelessness and anger about their experience and loss. Saying goodbye via video-conferencing technology was viewed ambivalently. Clinicians' presence during a death was little consolation. Anger, frustration and blame were directed at governments' inaction/policies or the public. The sadness of not being able to say goodbye as wished was compounded by lack of social support and disrupted after-death rituals. Users expressed a sense of political neglect/mistreatment alongside calls for action. They also used the platform to reinforce public health messages, express condolences and pay tribute.

CONCLUSION

Twitter was used for collective mourning and support and to promote public health messaging. End-of-life care providers should facilitate and optimise contact with loved ones, even when strict visitation policies are necessary, and provide proactive bereavement support.

Avoiding fights on social media: Strategies youth leverage to navigate conflict in a digital era

AIMS

Emerging qualitative work documents that social media conflict sometimes results in violence in impoverished urban neighborhoods. Not all experiences of social media conflict lead to violence, however, and youth ostensibly use a variety of techniques to avoid violent outcomes. Little research has explored the daily violence prevention strategies youth use on social media, an important gap given the omnipresence of social media in youth culture. This paper examines youth strategies and factors that avoid violence resulting from social media conflict.

METHOD

Four focus groups with 41 teenagers of color solicited strategies to prevent violence resulting from social media conflict. Three coders analyzed data in Dedoose, guided by systematic textual coding using a multi-step thematic analysis.

RESULTS

Four approaches emerged to avoiding violence from social media conflict: avoid, de-escalate, reach out for help, and bystander intervention.

CONCLUSION

Our findings position youth as key players in efforts to prevent violence from resulting from social media conflict.

A human-machine partnered approach for identifying social media signals of elevated traumatic grief in Chicago gang territories

There is a critical need to improve trauma-informed services in structurally marginalized communities impacted by violence and its associated traumatic grief. For community residents, particularly gang-associated youth, repeated exposure to traumatic grief causes serious adverse effects that may include negative health outcomes, delinquency, and future violent offenses. The recent proliferation of digital social media platforms, such as Twitter, provide a novel and largely underutilized resource for responding to these issues, particularly among these difficult-to-reach communities. In this paper, we explore the potential for using a human-machine partnered approach, wherein qualitative fieldwork and domain expertise is combined with a computational linguistic analysis of Twitter content among 18 gang territories/neighborhoods on Chicago's South Side. We first employ in-depth interviews and observations to identify common patterns by which residents in gang territories/neighborhoods express traumatic grief on social media. We leverage these qualitative findings, supplemented by domain expertise and computational techniques, to gather both traumatic grief- and gang-related tweets from Twitter. We next utilize supervised machine learning to construct a binary classification algorithm to eliminate irrelevant tweets that may have been gathered by our automated query and extraction techniques. Last, we confirm the validity, or ground truth, of our computational findings by enlisting additional domain expertise and further qualitative analyses of the specific traumatic events discussed in our sample of Twitter content. Using this approach, we find that social media provides useful signals for identifying moments of increased collective traumatic grief among residents in gang territories/neighborhoods. This is the first study to leverage Twitter to systematically ground the collective online articulations of traumatic grief in traumatic offline events occurring in violence-impacted communities. The results of this study will be useful for developing more effective tools-including trauma-informed intervention applications-for community organizations, violence prevention initiatives, and other public health efforts.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

OBJECTIVE

We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

METHODS

We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook.

RESULTS

In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.