#Alzheimer's and Dementia: Expressions of Memory Loss on Twitter

Staying connected: Alzheimer's hashtags and opportunities for engagement and overcoming stigma

Alzheimer's disease (AD) is a terminal, neurodegenerative disease, and consequently is difficult to communicate about as it is stigmatized, and discussions are rife with misconceptions. By situating AD conversations in the sociocultural space of the opportunity model of presence during the end-of-life process, a framework developed illustrating the potential trajectory from living with illness through death and into bereavement, we examined networked discussions surrounding Alzheimer's related hashtags on Twitter (N = 132,803) between January 1st, 2010, and September 29th, 2021. Using the mixed-method approach of the Analysis of Topic Model Network (ANTMN) framework, results revealed 30 topics clustered into five distinct themes: promotion, education, action, "You aren't alone", and dementia. Results indicated that discussions surrounding World Alzheimer's Day focused on changing stigma and promoting engagement in difficult conversations. The frequency of themes over time remained relatively stable. By understanding how Twitter's online discourse may be used to overcome stigmatized topics, we can continue to tailor messages to reduce stigma and provide support for those who experience similar health issues.

Automatically Identifying Twitter Users for Interventions to Support Dementia Family Caregivers: Annotated Data Set and Benchmark Classification Models (Preprint)

Background

More than 6 million people in the United States have Alzheimer disease and related dementias, receiving help from more than 11 million family or other informal caregivers. A range of traditional interventions has been developed to support family caregivers; however, most of them have not been implemented in practice and remain largely inaccessible. While recent studies have shown that family caregivers of people with dementia use Twitter to discuss their experiences, methods have not been developed to enable the use of Twitter for interventions.

Objective

The objective of this study is to develop an annotated data set and benchmark classification models for automatically identifying a cohort of Twitter users who have a family member with dementia.

Methods

Between May 4 and May 20, 2021, we collected 10,733 tweets, posted by 8846 users, that mention a dementia-related keyword, a linguistic marker that potentially indicates a diagnosis, and a select familial relationship. Three annotators annotated 1 random tweet per user to distinguish those that indicate having a family member with dementia from those that do not. Interannotator agreement was 0.82 (Fleiss kappa). We used the annotated tweets to train and evaluate support vector machine and deep neural network classifiers. To assess the scalability of our approach, we then deployed automatic classification on unlabeled tweets that were continuously collected between May 4, 2021, and March 9, 2022.

Results

A deep neural network classifier based on a BERT (bidirectional encoder representations from transformers) model pretrained on tweets achieved the highest F₁-score of 0.962 (precision=0.946 and recall=0.979) for the class of tweets indicating that the user has a family member with dementia. The classifier detected 128,838 tweets that indicate having a family member with dementia, posted by 74,290 users between May 4, 2021, and March 9, 2022—that is, approximately 7500 users per month.

Conclusions

Our annotated data set can be used to automatically identify Twitter users who have a family member with dementia, enabling the use of Twitter on a large scale to not only explore family caregivers’ experiences but also directly target interventions at these users.

Twitter use by the dementia community during COVID-19: a user classification and social network analysis

Purpose

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

Design/methodology/approach

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Findings

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

Originality/value

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.

Using Twitter to Examine Stigma Against People With Dementia During COVID-19: Infodemiology Study

BACKGROUND

During the pandemic, there has been significant social media attention focused on the increased COVID-19 risks and impacts for people with dementia and their care partners. However, these messages can perpetuate misconceptions, false information, and stigma.

OBJECTIVE

This study used Twitter data to understand stigma against people with dementia propagated during the COVID-19 pandemic.

METHODS

We collected 1743 stigma-related tweets using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets.

RESULTS

Based on our analysis, 4 main themes were identified: (1) ageism and devaluing the lives of people with dementia, (2) misinformation and false beliefs about dementia and COVID-19, (3) dementia used as an insult for political ridicule, and (4) challenging stigma against dementia. Social media has been used to spread stigma, but it can also be used to challenge negative beliefs, stereotypes, and false information.

CONCLUSIONS

Dementia education and awareness campaigns are urgently needed on social media to address COVID-19-related stigma. When stigmatizing discourse on dementia is widely shared and consumed amongst the public, it has public health implications. How we talk about dementia shapes how policymakers, clinicians, and the public value the lives of people with dementia. Stigma perpetuates misinformation, pejorative language, and patronizing attitudes that can lead to discriminatory actions, such as the limited provision of lifesaving supports and health services for people with dementia during the pandemic. COVID-19 policies and public health messages should focus on precautions and preventive measures rather than labeling specific population groups.

Exploring Online Twitter Conversations surrounding National Healthcare Decisions Day and Advance Care Planning from a Socio-Cultural Perspective: A Computational Mixed-Methods Analysis (Preprint)

Background

Within the cultures and societies of the United States, topics related to death and dying continue to be taboo, and as a result, opportunities for presence and engagement during the end of life, which could lead to a good death, are avoided. Several efforts have been made to help people engage in advance care planning (ACP) conversations, including completing advance care directives so that they may express their goals of care if they become too sick to communicate their wishes. A major effort in the United States toward encouraging such challenging discussions is the annual celebration of the National Healthcare Decisions Day.

Objective

This study aimed to explore ACP from a sociocultural perspective by using Twitter as a communication tool.

Methods

All publicly available tweets published between August 1, 2020, and July 30, 2021 (N=9713) were collected and analyzed using the computational mixed methods Analysis of Topic Model Network approach.

Results

The results revealed that conversations driven primarily by laypersons (7107/7410, 95.91% of tweets originated from unverified accounts) surrounded the following three major themes: importance and promotion, surrounding language, and systemic issues.

Conclusions

On the basis of the results, we argue that there is a need for awareness of the barriers that people may face when engaging in ACP conversations, including systemic barriers, literacy levels, misinformation, policies (including Medicare reimbursements), and trust among health care professionals, in the United States. This is incredibly important for clinicians and scholars worldwide to be aware of as we strive to re-envision ACP, so that people are more comfortable engaging in ACP conversations. In terms of the content of tweets, we argue that there is a chasm between the biomedical and biopsychosocial elements of ACP, including patient narratives. If used properly, Twitter conversations and National Health Care Decision Day hashtags could be harnessed to serve as a connecting point among organizations, physicians, patients, and family members to lay the groundwork for the trajectory toward a good death.

Health Forums and Twitter for Dementia Research: Opportunities and Considerations

BACKGROUND/OBJECTIVES

Social media platforms are promising sources for large quantities of participant-driven research data and circumvent some common challenges when conducting dementia research. This study provides a summary of key considerations and recommendations about using these platforms as research tools for dementia.

DESIGN

Mixed methods.

SETTING

Alzheimer's Society's online Dementia Talking Point forum from inception to April 17, 2018, and Twitter in February and March 2018.

PARTICIPANTS

All users of Dementia Talking Point who posted in subforums labeled "I have dementia" and "I care for a person with dementia," and Twitter users whose posts contained the keywords "dementia," "Alzheimer," or "Alzheimer's."

MEASUREMENTS

We quantified the average daily number of dementia-related posts on each platform and number of words per post. Guided by a codebook, we conducted thematic content analysis of 5% of the 15,513 posts collected from Dementia Talking Point, and 10% of the 25,948 comprehensible posts from Twitter containing "dementia," "Alzheimer," or "Alzheimer's." We also summarized research-relevant characteristics inherent to platforms and posts.

RESULTS

On average, Dementia Talking Point provided less than two new daily dementia-related posts with 213.5 to 241.5 words, compared with 7,883 new daily Twitter posts with 14.5 words. Persons with dementia (PWDs) commonly shared dementia-related concerns (75.7%), experiences (68.6%), and requests for, as well as offers of, information and support (44.3% and 38.6%, respectively). Caregivers commonly shared caregiving experience (67.0%) and requests for information and support (52.5%). Most common dementia-related Twitter posts were derogatory use of the term dementia (14.5%), advocacy, fundraising, and awareness (11.6%), and research dissemination (8.0%). Recommendations about these platforms' unique technical and ethical considerations are outlined.

CONCLUSIONS

Understanding the priorities of PWDs and their caregivers remains important to understand how clinicians can best support them. This study will help clinicians and researcher to better leverage online health forums and Twitter for such dementia-related information.

Evaluation of a dementia awareness programme in UK schools: a qualitative study

OBJECTIVES

'Dementia Friends' is a programme used to raise awareness of dementia, developed by the Alzheimer's Society, which has been delivered across the UK to diverse populations, including adolescents. However, there is little evidence available with regards to adolescents' perceptions of the programme and its impact. This study aims to explore this in a group of adolescents from the south of England.

STUDY DESIGN

Focus group discussions.

METHODS

Thirty adolescents aged between 11 and 16 years were recruited from two schools in East Sussex, England. All had participated in a Dementia Friends session in the past month. Focus group discussions were transcribed, coded and themes were created using inductive thematic analysis.

RESULTS

Four themes were identified: (1) perceptions and experiences of dementia, (2) outcomes and learning from Dementia Friends session, (3) reactions to the Dementia Friends session and (4) identified future learning needs.

CONCLUSIONS

Adolescents had generally positive opinions about Dementia Friends, particularly the interactive nature of the session. Whilst they felt participating in Dementia Friends improved their attitudes and knowledge, they were often left wanting to learn more. Future research needs to empirically evaluate the extent to which Dementia Friends may improve attitudes and knowledge of dementia.