Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

BACKGROUND

As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area.

METHODS

This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used.

RESULTS

A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data.

CONCLUSION

The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.

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.

The Stigma Toward Dementia on Twitter: A Sentiment Analysis of Dutch Language Tweets

People living with dementia are often faced with attitudes indicating stigma. Social media platforms, such as Twitter, can allow for self-expression and support, but can also be used to disseminate misinformation, which can reinforce existing stigma. In the present study, we explore whether the stigma toward dementia is present in Dutch language tweets. In total, 969 tweets containing dementia-related keywords were collected during a period of five months in 2019 and 2020. These were analyzed by means of a sentiment analysis, which we approached as a classification task. The tweets were coded into seven dimensions, i.e., information, joke, metaphor, organization, personal experience, politics, and ridicule, using a semi-automatic machine learning approach. The emerging correlations with our use of Linguistic Inquiry and Word Count software for sentiment analysis validate our approach. In the present study, 9.29% of tweets contain ridicule, propagating stigmatic attitudes on Twitter.

Clinician Communication in Hospice: Constructions of Reality Throughout the End-of-Life Process

The current study examined qualitative data from hospice clinicians' perspectives on language, surrounding end-of-life (EOL), to understand challenges and opportunities for constructing a trajectory of communication leading towards a good death experience. Findings from two focus groups with nine clinicians' and 12 individual interviews, four of which were follow up interviews after the focus groups, were guided by framework analysis and revealed three themes, constructing language choices, roles and responsibilities, and socio-cultural considerations. We used the Opportunity Model for Presence during the End-of-Life Process (OMP-EOLP) to make sense of the findings and discuss implications for language use throughout the EOL process. We argue additional efforts should be made in recognizing the value of presence checks, re-constructing advance care planning, and utilizing different forms of media as an educational tool and connection mechanism for clinicians with patients and families to achieve a timely engagement of EOL conversations for all healthcare participants.

They May not Work! An Evaluation of Eleven Sentiment Analysis Tools on Seven Social Media Datasets

OBJECTIVE

Sentiment analysis is an important method for understanding emotions and opinions expressed through social media exchanges. Little work has been done to evaluate the performance of existing sentiment analysis tools on social media datasets, particularly those related to health, healthcare, or public health. This study aims to address the gap.

MATERIAL AND METHODS

We evaluated 11 commonly used sentiment analysis tools on five health-related social media datasets curated in previously published studies. These datasets include Human Papillomavirus Vaccine, Health Care Reform, COVID-19 Masking, Vitals.com Physician Reviews, and the Breast Cancer Forum from MedHelp.org. For comparison, we also analyzed two non-health datasets based on movie reviews and generic tweets. We conducted a qualitative error analysis on the social media posts that were incorrectly classified by all tools.

RESULTS

The existing sentiment analysis tools performed poorly with an average weighted F1 score below 0.6. The inter-tool agreement was also low; the average Fleiss Kappa score is 0.066. The qualitative error analysis identified two major causes for misclassification: (1) correct sentiment but on wrong subject(s) and (2) failure to properly interpret inexplicit/indirect sentiment expressions.

DISCUSSION

and Conclusion: The performance of the existing sentiment analysis tools is insufficient to generate accurate sentiment classification results. The low inter-tool agreement suggests that the conclusion of a study could be entirely driven by the idiosyncrasies of the tool selected, rather than by the data. This is very concerning especially if the results may be used to inform important policy decisions such as mask or vaccination mandates.

Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study

Background

Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns.

Objective

This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions.

Methods

Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time.

Results

A random forest model best identified misconceptions with an accuracy of 82% from blind validation and found that 37% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions.

Conclusions

Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time.

Examining the COVID-19 Impact on People with Dementia from the Perspective of Family and Friends

Background

The COVID-19 pandemic is taking a serious toll on people with dementia. Given the rapidly evolving COVID-19 context, policymakers and practitioners require timely, evidence-informed research to address the changing needs and challenges of people with dementia and their family care partners.

Objective

Using Twitter data, the objective of this study was to examine the COVID-19 impact on people with dementia from the perspective of their family members and friends.

Methods

Using the Twint application in Python, we collected 6243 relevant tweets over a 15-month time frame. The tweets were divided among 11 coders and analyzed using a 6-step thematic analysis process.

Results

Based on our analysis, 3 main themes were identified: (1) frustration and structural inequities (eg, denied dignity and inadequate supports), (2) despair due to loss (eg, isolation, decline, and death), and (3) resiliency, survival, and hope for the future.

Conclusions

As the COVID-19 pandemic persists and new variants emerge, people with dementia and their family care partners are facing complex challenges that require timely interventions. More specifically, tackling COVID-19 challenges requires revisiting pandemic policies and protocols to ensure equitable access to health and support services, recognizing the essential role of family care partners, and providing financial assistance and resources to help support people with dementia in the pandemic. Revaluating COVID-19 policies is critical to mitigating the pandemic’s impact on people with dementia and their family care partners.

Using Twitter to understand perspectives and experiences of dementia and caregiving at the beginning of the COVID-19 pandemic

The COVID-19 pandemic has placed a tremendous burden on all of society, particularly among vulnerable populations such as people living with dementia and their caregivers. Efforts to understand the impact of the COVID-19 pandemic on those living with dementia are crucial towards addressing needs during the pandemic and beyond. This qualitative descriptive study includes a thematic analysis of 6938 tweets from March 17–24, 2020, that included direct or indirect references to COVID-19 and at least one of the following terms/hashtags: Alzheimer, #Alzheimer, dementia, and #dementia. Five themes were identified: continuing care, finding support, preventing spread of COVID-19, maintaining human rights, and the impact of the pandemic on the daily lives of people living with dementia. People living with dementia and their families faced unique challenges related to caregiving, maintaining social connectedness while trying to follow public health guidelines, and navigating the convergence of COVID-19 and dementia-related stigma. Data from Twitter can be an effective means to understand the impacts of public health emergencies among those living with dementia and how to address their needs moving forward by highlighting gaps in practice, services, and research.

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.

Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo

Background

The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience’s attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited.

Objective

This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics.

Methods

A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide.

Results

In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide (χ²₁=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow (χ²₁=28.9; P<.001), false representation (χ²₁=144.4; P<.001), weak and pathetic (χ²₁=20.4; P<.001), glorified and normalized (χ²₁=177.6; P<.001), and immoral (χ²₁=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85.

Conclusions

The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide.

Improving the health equity and the human rights of Canadians with dementia through a social determinants approach: a call to action in the COVID-19 pandemic

In 2019, the Canadian Government released a national dementia strategy that identified the need to address the health inequity (e.g., avoidable, unfair, and unjust differences in health outcomes) and improve the human rights of people living with dementia. However, the novel coronavirus disease 2019 (COVID-19) pandemic is having an inequitable impact on people with dementia in terms of mortality and human rights violations. As the new Omicron COVID-19 variant approaches its peak, our commentary highlights the need for urgent action to support people living with dementia and their care partners. More specifically, we argue that reducing COVID-19 inequities requires addressing underlying population-level factors known as the social determinants of health. Health disparities cannot be rectified merely by looking at mortality rates of people with dementia. Thus, we believe that improving the COVID-19 outcomes of people with dementia requires addressing key determinants such as where people live, their social supports, and having equitable access to healthcare services. Drawing on Canadian-based examples, we conclude that COVID-19 policy responses to the pandemic must be informed by evidence-informed research and collaborative partnerships that embrace the lived experience of diverse people living with dementia and their care partners.

The use of digital technologies by people with mild-to-moderate dementia during the COVID-19 pandemic: A positive technology perspective

A growing body of research has shown that people with dementia are using digital technologies to enhance lived experience. The COVID-19 pandemic has brought new digital opportunities and challenges and so provides a unique opportunity to understand how people with dementia have adapted to this new digital landscape. Semi-structured interviews were conducted with 19 people with dementia and analysed thematically. We generated five themes, showing how participants used digital means to combat the stresses of the pandemic by facilitating social connection, self-actualisation, enhanced well-being and by assisting with activities of daily life. These technologies helped to reduce isolation, provide access to support groups, create opportunities for cognitive stimulation and self-development, and engendered a sense of identity at a time of perceived loss. Despite these benefits, participants also reported challenges regarding cognitive fatigue and usability issues. We recommend that training on how to use digital technologies is co-produced with people with dementia and designers engage with the voices of people with dementia throughout the design process. In turn, this could promote the social connectedness, well-being and self-worth of people with dementia.

Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning

Stigma has negative effects on people with mental health problems by making them less likely to seek help. We develop a proof of principle service user supervised machine learning pipeline to identify stigmatising tweets reliably and understand the prevalence of public schizophrenia stigma on Twitter. A service user group advised on the machine learning model evaluation metric (fewest false negatives) and features for machine learning. We collected 13,313 public tweets on schizophrenia between January and May 2018. Two service user researchers manually identified stigma in 746 English tweets; 80% were used to train eight models, and 20% for testing. The two models with fewest false negatives were compared in two service user validation exercises, and the best model used to classify all extracted public English tweets. Tweets classed as stigmatising by service users were more negative in sentiment (t (744) = 12.02, p < 0.001 [95% CI: 0.196–0.273]). Our linear Support Vector Machine was the best performing model with fewest false negatives and higher service user validation. This model identified public stigma in 47% of English tweets (n5,676) which were more negative in sentiment (t (12,143) = 64.38, p < 0.001 [95% CI: 0.29–0.31]). Machine learning can identify stigmatising tweets at large scale, with service user involvement. Given the prevalence of stigma, there is an urgent need for education and online campaigns to reduce it. Machine learning can provide a real time metric on their success.

Investigation of Carers’ Perspectives of Dementia Misconceptions on Twitter: Focus Group Study

Background

Dementia misconceptions on social media are common, with negative effects on people with the condition, their carers, and those who know them. This study codeveloped a thematic framework with carers to understand the forms these misconceptions take on Twitter.

Objective

The aim of this study is to identify and analyze types of dementia conversations on Twitter using participatory methods.

Methods

A total of 3 focus groups with dementia carers were held to develop a framework of dementia misconceptions based on their experiences. Dementia-related tweets were collected from Twitter’s official application programming interface using neutral and negative search terms defined by the literature and by carers (N=48,211). A sample of these tweets was selected with equal numbers of neutral and negative words (n=1497), which was validated in individual ratings by carers. We then used the framework to analyze, in detail, a sample of carer-rated negative tweets (n=863).

Results

A total of 25.94% (12,507/48,211) of our tweet corpus contained negative search terms about dementia. The carers’ framework had 3 negative and 3 neutral categories. Our thematic analysis of carer-rated negative tweets found 9 themes, including the use of weaponizing language to insult politicians (469/863, 54.3%), using dehumanizing or outdated words or statements about members of the public (n=143, 16.6%), unfounded claims about the cures or causes of dementia (n=11, 1.3%), or providing armchair diagnoses of dementia (n=21, 2.4%).

Conclusions

This is the first study to use participatory methods to develop a framework that identifies dementia misconceptions on Twitter. We show that misconceptions and stigmatizing language are not rare. They manifest through minimizing and underestimating language. Web-based campaigns aiming to reduce discrimination and stigma about dementia could target those who use negative vocabulary and reduce the misconceptions that are being propagated, thus improving general awareness.

Infodemiological Examination of Personal and Commercial Tweets About Cannabidiol: Term and Sentiment Analysis

BACKGROUND

In the absence of official clinical trial information, data from social networks can be used by public health and medical researchers to assess public claims about loosely regulated substances such as cannabidiol (CBD). For example, this can be achieved by comparing the medical conditions targeted by those selling CBD against the medical conditions patients commonly treat with CBD.

OBJECTIVE

The objective of this study was to provide a framework for public health and medical researchers to use for identifying and analyzing the consumption and marketing of unregulated substances. Specifically, we examined CBD, which is a substance that is often presented to the public as medication despite complete evidence of efficacy and safety.

METHODS

We collected 567,850 tweets by searching Twitter with the Tweepy Python package using the terms "CBD" and "cannabidiol." We trained two binary text classifiers to create two corpora of 167,755 personal use and 143,322 commercial/sales tweets. Using medical, standard, and slang dictionaries, we identified and compared the most frequently occurring medical conditions, symptoms, side effects, body parts, and other substances referenced in both corpora. In addition, to assess popular claims about the efficacy of CBD as a medical treatment circulating on Twitter, we performed sentiment analysis via the VADER (Valence Aware Dictionary for Sentiment Reasoning) model on the personal CBD tweets.

RESULTS

We found references to medically relevant terms that were unique to either personal or commercial CBD tweet classes, as well as medically relevant terms that were common to both classes. When we calculated the average sentiment scores for both personal and commercial CBD tweets referencing at least one of 17 medical conditions/symptoms terms, an overall positive sentiment was observed in both personal and commercial CBD tweets. We observed instances of negative sentiment conveyed in personal CBD tweets referencing autism, whereas CBD was also marketed multiple times as a treatment for autism within commercial tweets.

CONCLUSIONS

Our proposed framework provides a tool for public health and medical researchers to analyze the consumption and marketing of unregulated substances on social networks. Our analysis showed that most users of CBD are satisfied with it in regard to the condition that it is being advertised for, with the exception of autism.

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.

From Text to Thought: How Analyzing Language Can Advance Psychological Science

Humans have been using language for millennia but have only just begun to scratch the surface of what natural language can reveal about the mind. Here we propose that language offers a unique window into psychology. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—natural-language processing and comparative linguistics—are contributing to how we understand topics as diverse as emotion, creativity, and religion and overcoming obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods and highlight the best way to combine language analysis with more traditional psychological paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

A Twitter discourse analysis of negative feelings and stigma related to NAFLD, NASH and obesity

BACKGROUND

People with non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are stigmatized, partly since 'non-alcoholic' is in the name, but also because of obesity, which is a common condition in this group. Stigma is pervasive in social media and can contribute to poorer health outcomes. We examine how stigma and negative feelings concerning NAFLD/NASH and obesity manifest on Twitter.

METHODS

Using a self-developed search terms index, we collected NAFLD/NASH tweets from May to October 2019 (Phase I). Because stigmatizing NAFLD/NASH tweets were limited, Phase II focused on obesity (November-December 2019). Via sentiment analysis, >5000 tweets were annotated as positive, neutral or negative and used to train machine learning-based Natural Language Processing software, applied to 193 747 randomly sampled tweets. All tweets collected were analysed.

RESULTS

In Phase I, 16 835 tweets for NAFLD and 2376 for NASH were retrieved. Of the annotated NAFLD/NASH tweets, 97/1130 (8.6%) and 63/535 (11.8%), respectively, related to obesity and 13/1130 (1.2%) and 5/535 (0.9%), to stigma; they primarily focused on scientific discourse and unverified information. Of the 193 747 non-annotated obesity tweets (Phase II), the algorithm classified 40.0% as related to obesity, of which 85.2% were negative, 1.0% positive and 13.7% neutral.

CONCLUSIONS

NAFLD/NASH tweets mostly indicated an unmet information need and showed no clear signs of stigma. However, the negative content of obesity tweets was recurrent. As obesity-related stigma is associated with reduced care engagement and lifestyle modification, the main NAFLD/NASH treatment, stigma-reducing interventions in social media should be included in the liver health agenda.

A Thematic Analysis of Weibo Topics (Chinese Twitter Hashtags) Regarding Older Adults During the COVID-19 Outbreak

Objectives

We explored the portrayal of older adults and the public response to topics concerning older adults during the COVID-19 pandemic in Chinese social media (Weibo topics, equivalent to hashtags on Twitter). We also explored the temporal trends of dominant themes to identify changes over time.

Method

Topics related to older adults were searched in the Weibo topic search engine between January 20 and April 28, 2020. Overall, 241 topics and their view frequency and comment frequency were collected. Inductive thematic analysis was conducted to classify the topics into themes. The popularity of each theme was also analyzed. In addition, the frequency with which each theme appeared during the three major stages of the pandemic (outbreak, turnover, post-peak) was reported.

Results

Six main themes were identified. “Older adults contributing to the community” was the most prominent theme with the highest average comment frequency per topic. It was also the most dominant theme in the first stage of the pandemic. “Older patients in hospitals” was the second most prominent theme, and the most dominant theme in the second and third stages of the pandemic. The percentage of topics with the themes “Care recipients” and “Older adults caring for the young” increased over time.

Discussion

The portrayal of older people as being warm, competent, and actively exercising their agency is prevalent on Weibo. The Weibo-viewing public shows signs of interest in intergenerational solidarity during the pandemic in China. These findings are different from findings reported by studies conducted in the West.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

[Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019]

Objetivo.

Informar sobre la oposición a las vacunas y la información errónea fomentadas en Twitter, destacando las cuentas de Twitter que dirigen estas conversaciones.

Métodos.

Utilizamos el aprendizaje automático supervisado para codificar todos los mensajes publicados en Twitter. En primer lugar, identificamos manualmente los códigos y los temas mediante un enfoque teórico fundamentado y, a continuación, los aplicamos a todo el conjunto de datos de forma algorítmica. Identificamos a los 50 autores más importantes un mes tras otro para determinar las fuentes influyentes de información relacionadas con la oposición a las vacunas.

Resultados.

El período de recopilación de datos fue del 1 de junio al 1 de diciembre del 2019, lo que dio lugar a 356 594 mensajes opuestos a las vacunas. Un total de 129 autores de Twitter reunieron los criterios de autor principal durante al menos un mes. Los autores principales fueron responsables del 59,5% de los mensajes opuestos a las vacunas y detectamos diez temas de conversación. Los temas se distribuyeron de forma similar entre los autores principales y todos los demás autores que declararon su oposición a las vacunas. Los autores principales parecían estar muy coordinados en su promoción de la información errónea sobre cada tema.

Conclusiones.

La salud pública se ha esforzado por responder a la información errónea sobre las vacunas. Los resultados indican que las fuentes de información errónea sobre las vacunas no son tan heterogéneas ni están tan distribuidas como podría parecer a primera vista, dado el volumen de mensajes. Existen fuentes identificables de información errónea, lo que puede ayudar a contrarrestar los mensajes y a fortalecer la vigilancia de la salud pública.

Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning

Objectives

This study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse.

Methods

Twitter data (N=82,893) related to both older adults and COVID-19 and dated from January 23 to May 20, 2020, were analyzed. We used a combination of data science methods (including Linguistic Inquiry and Word Count, supervised machine learning, topic modeling, and sentiment analysis), qualitative thematic analysis, and conventional statistics.

Results

The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). The daily average of ageist content was 18%, with the highest of 52.8% on March 11, 2020. Specifically, more than one in ten (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” Also, 14 themes were extracted, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic.

Discussion

Ageist content in the context of COVID-19 was prevalent on Twitter. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.

The use of Twitter by people with young-onset dementia: A qualitative analysis of narratives and identity formation in the age of social media

A diagnosis of dementia in midlife can be challenging, causing losses or changes in a person’s identity. Narrative provides a means of reconstructing identity and can be communicated on social media. There has been initial evidence on the value of Twitter for people with dementia, but researchers have not yet directly engaged with users’ perspectives. We employed a narrative model of identity to examine why people with dementia use Twitter and what challenges they face. Interviews were conducted with 11 younger people with dementia and analysed thematically. Participants used Twitter to counter a loss of identity through community membership and by regaining a sense of purpose. They sought to redefine dementia identities by challenging stigma and campaigning for social change. The character limit of tweets facilitated narrative through which participants preserved their identities. These findings suggest that Twitter could be an important source of post-diagnostic support for people with young-onset dementia. However, there are some risks as Twitter was sometimes a hostile environment for individuals who did not present in a ‘typical’ manner, or faced technical difficulties because of their symptoms. In the future, platform developers could work with people with dementia to make Twitter more accessible for this group.

The mediating effect of media usage on the relationship between anxiety/fear and physician-patient trust during the COVID-19 pandemic

OBJECTIVE

Our study explored whether and how media usage can mediate the path from anxiety and fear to physician-patient trust.

DESIGN

Study 1 was a population-based, longitudinal study using nationally representative data from 29 provinces in mainland China. The baseline sample (N = 3233) was obtained from February 1 to 9, 2020. Follow-up (N = 1380) took place during March 17 to 24, 2020. Study 2 was a machine learning-based sentiment analysis in which data were captured from Sina Weibo, a Chinese microblogging website, among the most popular official, unofficial, and health-related media accounts. The screened blogs from November to December 2019 and February to March 2020 were scored by Google APIs for positivity and magnitude.

MAIN OUTCOME MEASURES

Physician-patient trust.

RESULTS

Study 1 showed fear and anxiety affected changes in physician-patient trust through media usage, the indirect effect of which was 0.14 (0.03) and the 95% CI was [0.08, 0.19]. Study 2 indicated a more positive image of physicians after the outbreak compared to before [F (2, 3537) = 3.646, p = 0.026, partial η²=0.002].

CONCLUSION

The negative impact of anxiety and fear on physician-patient trust was mediated by media use, which can be explained by the more positive media image during the pandemic.

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers' opinions about products assists to maintain their competitive conditions. We intend to analyze the users' opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words' relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F-score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

Negative stereotypes, fear and social distance: a systematic review of depictions of dementia in popular culture in the context of stigma

BACKGROUND

Literature, film and news media reflect and shape social perceptions of dementia which in turn impact on dementia stigma. The aim of this paper is to systematically review papers on the depiction and frames for dementia in literature, film, mass media and social media in order to better understand cultural stigma related to dementia.

METHODS

A systematic search of electronic databases was undertaken combining phrases relating to dementia, popular culture and representations, and phrases relating to dementia and stigma. We searched for scientific English language papers which included original analysis on the representation or depiction of dementia in popular culture (i.e. in film and television, literature, news, social media and language). Articles published between 1989-2018 were included. The search was conducted in December 2017 and updated in January 2019. Inductive thematic synthesis was undertaken.

RESULTS

A total of 60 articles were included from an initial sample of 37022. Dementia was almost always depicted in conjunction with ageing, and often equated with Alzheimer's disease. Common frames for dementia were biomedical - dementia involves the deterioration of the brain for which there is no current cure; natural disaster or epidemic - dementia is a force of nature which will overwhelm mankind; and living dead - people with dementia lose their brains, memories, minds and consequently their personhood and human rights. There were examples of more positive depictions of dementia including expressing love and individual agency and experiencing personal growth. Feelings commonly associated with dementia were fear, shame, compassion and guilt, and depictions often resulted in a sense of social distance.

CONCLUSIONS

Depictions of dementia in popular culture are associated with negative images and feelings, and social distance between people with dementia and those without. These correspond to dementia stigma in the public and as experienced by people with dementia. Further research is needed into the impact of literature, news and social media on dementia stigma and these cultural mediums might be used to reduce stigma.

Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019

Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation.Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition.Results. The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes.Conclusions. Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in countermessaging and public health surveillance.

Coronavirus, Ageism, and Twitter: An Evaluation of Tweets about Older Adults and COVID-19

OBJECTIVES

In March 2020, the World Health Organization declared coronavirus disease 2019 (COVID‐19) a pandemic. High morbidity and mortality rates of COVID‐19 have been observed among older adults and widely reported in both mainstream and social media. The objective of this study was to analyze tweets related to COVID‐19 and older adults, and to identify ageist content.

DESIGN

We obtained a representative sample of original tweets containing the keywords “elderly,” “older,” and/or “boomer” plus the hashtags “#COVID19” and/or “#coronavirus.”

SETTING

Tweets posted between March 12 and March 21, 2020.

MEASUREMENTS

We identified the type of user and number of followers for each account. Tweets were classified by three raters as (1) informative, (2) personal accounts, (3) personal opinions, (4) advice seeking, (5) jokes, and (6) miscellaneous. Potentially offensive content, as well as that downplaying the severity of COVID‐19 because it mostly affects older adults, was identified.

RESULTS

A total of 18,128 tweets were obtained, of which a random sample of 351 was analyzed. Most accounts (91.7%) belonged to individuals. The most common types of tweets were personal opinions (31.9%), followed by informative tweets (29.6%), jokes/ridicule (14.3%), and personal accounts (13.4%). Overall, 72 tweets (21.9%) likely intended to ridicule or offend someone and 21.1% had content implying that the life of older adults was less valuable or downplayed the relevance of COVID‐19.

CONCLUSION

Most tweets related to COVID‐19 and older adults contained personal opinions, personal accounts, and jokes. Almost one‐quarter of analyzed tweets had ageist or potentially offensive content toward older adults. J Am Geriatr Soc 68:1661‐1665, 2020.

Overcoming Alzheimer's Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies

Alzheimer's disease (AD) imposes a considerable burden on those diagnosed. Faced with a neurodegenerative decline for which there is no effective cure or prevention method, sufferers of the disease are subject to judgement, both self-imposed and otherwise, that can have a great deal of effect on their lives. The burden of this stigma is more than just psychological, as reluctance to face an AD diagnosis can lead people to avoid early diagnosis, treatment, and research opportunities that may be beneficial to them, and that may help progress towards fighting AD and its progression. In this review, we discuss how recent advents in information technology may be employed to help fight this stigma. Using artificial intelligence (AI) technologies, specifically natural language processing (NLP), to classify the sentiment and tone of texts, such as those of online posts on various social media sites, has proven to be an effective tool for assessing the opinions of the general public on certain topics. These tools can be used to analyze the public stigma surrounding AD. Additionally, there is much concern among individuals that an AD diagnosis, or evidence of pre-clinical AD such as a biomarker or imaging test results, may wind up unintentionally disclosed to an entity that may discriminate against them. The lackluster security record of many medical institutions justifies this fear to an extent. Adopting more secure and decentralized methods of data transfer and storage, and giving patients enhanced ability to control their own data, such as a blockchain-based method, may help to alleviate some of these fears.

Sentiment Analysis in Health and Well-Being: Systematic Review

Background

Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.”

Objective

This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals.

Methods

Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation.

Results

The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes.

Conclusions

SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

Electronic Cigarette Themes on Twitter: Dissemination Patterns and Relations with Online News and Search Engine Queries in South Korea

This study examines multiple aspects of e-cigarette mentions on different online media channels during the announcement and implementation of a cigarette tax increase policy in South Korea. It consists of three parts. First, a Naive Bayes classifier was used to sort 59,147 tweets about e-cigarettes into five pre-designated themes - promotion, health, policy, product evaluation, and price - and to determine their relative prevalence. Second, social network analysis was used to identify the themes' dissemination patterns. Third, the themes were examined for their associations with e-cigarette mentions in two other media channels - online news articles (n = 580) and search engine queries (64 weeks of Google Trends data). Results show that the most prevalent tweet theme was product evaluation, and the theme with the largest increase during the data collection period was promotion. Promotion-themed tweets were the least connected with tweets about the other five themes, while health-themed tweets were the most connected. Finally, product evaluation-themed tweets exhibited the strongest correlation with search engine query count and had the largest explanatory power.

How Do General-Purpose Sentiment Analyzers Perform when Applied to Health-Related Online Social Media Data?

Sentiment analysis has been increasingly used to analyze online social media data such as tweets and health forum posts. However, previous studies often adopted existing, general-purpose sentiment analyzers developed in non-healthcare domains, without assessing their validity and without customizing them for the specific study context. In this work, we empirically evaluated three general-purpose sentiment analyzers popularly used in previous studies (Stanford Core NLP Sentiment Analysis, TextBlob, and VADER), based on two online health datasets and a general-purpose dataset as the baseline. We illustrate that none of these general-purpose sentiment analyzers were able to produce satisfactory classifications of sentiment polarity. Further, these sentiment analyzers generated inconsistent results when applied to the same dataset, and their performance varies to a great extent across the two health datasets. Significant future work is therefore needed to develop context-specific sentiment analysis tools for analyzing online health data.

Interdisciplinary optimism? Sentiment analysis of Twitter data

Interdisciplinary research has faced many challenges, including institutional, cultural and practical ones, while it has also been reported as a 'career risk' and even 'career suicide' for researchers pursuing such an education and approach. Yet, the propagation of challenges and risks can easily lead to a feeling of anxiety and disempowerment in researchers, which we think is counterproductive to improving interdisciplinarity in practice. Therefore, in the search of 'bright spots', which are examples of cases in which people have had positive experiences with interdisciplinarity, this study assesses the perceptions of researchers on interdisciplinarity on the social media platform Twitter. The results of this study show researchers' many positive experiences and successes of interdisciplinarity, and, as such, document examples of bright spots. These bright spots can give reason for optimistic thinking, which can potentially have many benefits for researchers' well-being, creativity and innovation, and may also inspire and empower researchers to strive for and pursue interdisciplinarity in the future.

A systematic literature review of machine learning in online personal health data

OBJECTIVE

User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.

MATERIALS AND METHODS

We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review.

RESULTS

We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support.

CONCLUSIONS

The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.

Considering the Impact of Social Media on Contemporary Improvement of Australian Aboriginal Health: Scoping Review

BACKGROUND

Social media may have a significant role in influencing the present and future health implications among Australian Aboriginal and Torres Strait Islander people, yet there has been no review of the role of social media in improving health.

OBJECTIVE

This study aims to examine the extent of health initiatives using social media that aimed to improve the health of Australian Aboriginal communities.

METHODS

A scoping review was conducted by systematically searching databases CINAHL Plus; PubMed; Scopus; Web of Science, and Ovid MEDLINE in June 2017 using the terms and their synonyms "Aboriginal" and "Social media." In addition, reference lists of included studies and the Indigenous HealthInfonet gray literature were searched. Key information about the social media intervention and its impacts on health were extracted and data synthesized using narrative summaries.

RESULTS

Five papers met inclusion criteria. All included studies were published in the past 5 years and involved urban, rural, and remote Aboriginal or Torres Strait Islander people aged 12-60 years. No studies reported objective impacts on health. Three papers found that social media provided greater space for sharing health messages in a 2-way exchange. The negative portrayal of Aboriginal people and negative health impacts of social media were described in 2 papers.

CONCLUSIONS

Social media may be a useful strategy to provide health messages and sharing of content among Aboriginal people, but objective impacts on health remain unknown. More research is necessary on social media as a way to connect, communicate, and improve Aboriginal health with particular emphasis on community control, self-empowerment, and decolonization.

Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets

Background

Dementia is a prevalent disorder among adults and often subjects an individual and his or her family. Social media websites may serve as a platform to raise awareness for dementia and allow researchers to explore health-related data.

Objective

The objective of this study was to utilize Twitter, a social media website, to examine the content and location of tweets containing the keyword “dementia” to better understand the reasons why individuals discuss dementia. We adopted an approach that analyzed user location, user category, and tweet content subcategories to classify large publicly available datasets.

Methods

A total of 398 tweets were collected using the Twitter search application programming interface with the keyword “dementia,” circulated between January and February 2018. Twitter users were categorized into 4 categories: general public, health care field, advocacy organization, and public broadcasting. Tweets posted by “general public” users were further subcategorized into 5 categories: mental health advocate, affected persons, stigmatization, marketing, and other. Placement into the categories was done through thematic analysis.

Results

A total of 398 tweets were written by 359 different screen names from 28 different countries. The largest number of Twitter users were from the United States and the United Kingdom. Within the United States, the largest number of users were from California and Texas. The majority (281/398, 70.6%) of Twitter users were categorized into the “general public” category. Content analysis of tweets from the “general public” category revealed stigmatization (113/281, 40.2%) and mental health advocacy (102/281, 36.3%) as the most common themes. Among tweets from California and Texas, California had more stigmatization tweets, while Texas had more mental health advocacy tweets.

Conclusions

Themes from the content of tweets highlight the mixture of the political climate and the supportive network present on Twitter. The ability to use Twitter to combat stigma and raise awareness of mental health indicates the benefits that can potentially be facilitated via the platform, but negative stigmatizing tweets may interfere with the effectiveness of this social support.

Detecting Temporal Cognition in Text: Comparison of Judgements by Self, Expert and Machine

Background: There is a growing research focus on temporal cognition, due to its importance in memory and planning, and links with psychological wellbeing. Researchers are increasingly using diary studies, experience sampling and social media data to study temporal thought. However, it remains unclear whether such reports can be accurately interpreted for temporal orientation. In this study, temporal orientation judgements about text reports of thoughts were compared across human coding, automatic text mining, and participant self-report.

Methods: 214 participants responded to randomly timed text message prompts, categorically reporting the temporal direction of their thoughts and describing the content of their thoughts, producing a corpus of 2505 brief (1–358, M = 43 characters) descriptions. Two researchers independently, blindly coded temporal orientation of the descriptions. Four approaches to automated coding used tense to establish temporal category for each description. Concordance between temporal orientation assessments by self-report, human coding, and automatic text mining was evaluated.

Results: Human coding more closely matched self-reported coding than automated methods. Accuracy for human (79.93% correct) and automated (57.44% correct) coding was diminished when multiple guesses at ambiguous temporal categories (ties) were allowed in coding (reduction to 74.95% correct for human, 49.05% automated).

Conclusion: Ambiguous tense poses a challenge for both human and automated coding protocols that attempt to infer temporal orientation from text describing momentary thought. While methods can be applied to minimize bias, this study demonstrates that researchers need to be wary about attributing temporal orientation to text-reported thought processes, and emphasize the importance of eliciting self-reported judgements.

Identifying people with dementia on Twitter

There is a growing body of research on the use of Twitter by people with health conditions, but it does not include people with dementia. In this brief report, we aim to: (1) determine whether people with dementia are using Twitter; (2) provide an estimate of the number of Twitter account holders who identify as having a diagnosis of dementia; and (3) examine the demographic characteristics of these account holders. Tweetcatcher was used to identify tweets containing the search terms ‘dementia’ or ‘Alzheimer’. These data were systematically searched to locate account holders who identified themselves as having a diagnosis of dementia, and a content analysis was conducted of these account holders’ profiles. Thirty account holders self-identified as having a diagnosis of dementia. The average age of account holders was 59 years and the majority were located in North America or the UK. Although the majority of account holders reported having Alzheimer’s disease or did not specify a type of dementia, some rare forms of dementia were also evident. The sample consisted of relatively young account holders and contained more men, which might suggest that other groups are under-represented on Twitter. The majority of account holders considered themselves a dementia activist or were affiliated with a dementia organisation. The findings suggest that people with dementia, with varying demographic characteristics and a range of diagnoses, are active on Twitter. These account holders are more frequently male, relatively young, and dementia activists.

Population Neuroscience: Dementia Epidemiology Serving Precision Medicine and Population Health

Over recent decades, epidemiology has made significant contributions to our understanding of dementia, translating scientific discoveries into population health. Here, we propose reframing dementia epidemiology as "population neuroscience," blending techniques and models from contemporary neuroscience with those of epidemiology and biostatistics. On the basis of emerging evidence and newer paradigms and methods, population neuroscience will minimize the bias typical of traditional clinical research, identify the relatively homogenous subgroups that comprise the general population, and investigate broader and denser phenotypes of dementia and cognitive impairment. Long-term follow-up of sufficiently large study cohorts will allow the identification of cohort effects and critical windows of exposure. Molecular epidemiology and omics will allow us to unravel the key distinctions within and among subgroups and better understand individuals' risk profiles. Interventional epidemiology will allow us to identify the different subgroups that respond to different treatment/prevention strategies. These strategies will inform precision medicine. In addition, insights into interactions between disease biology, personal and environmental factors, and social determinants of health will allow us to measure and track disease in communities and improve population health. By placing neuroscience within a real-world context, population neuroscience can fulfill its potential to serve both precision medicine and population health.

Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research

PURPOSE OF REVIEW

We present a review of emerging technologies and how these can transform personal air pollution exposure assessment and subsequent health research.

RECENT FINDINGS

Estimating personal air pollution exposures is currently split broadly into methods for modeling exposures for large populations versus measuring exposures for small populations. Air pollution sensors, smartphones, and air pollution models capitalizing on big/new data sources offer tremendous opportunity for unifying these approaches and improving long-term personal exposure prediction at scales needed for population-based research. A multi-disciplinary approach is needed to combine these technologies to not only estimate personal exposures for epidemiological research but also determine drivers of these exposures and new prevention opportunities. While available technologies can revolutionize air pollution exposure research, ethical, privacy, logistical, and data science challenges must be met before widespread implementations occur. Available technologies and related advances in data science can improve long-term personal air pollution exposure estimates at scales needed for population-based research. This will advance our ability to evaluate the impacts of air pollution on human health and develop effective prevention strategies.