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Book ; Online: Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support

Loh, Siyuan Brandon / Raamkumar, Aravind Sesagiri

2023

Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various information-seeking and reasoning tasks. These computational systems drive state-of-the-art dialogue systems, such as ChatGPT and Bard. They also carry substantial ... ...

Abstract	Large Language Models (LLMs) have demonstrated remarkable performance across various information-seeking and reasoning tasks. These computational systems drive state-of-the-art dialogue systems, such as ChatGPT and Bard. They also carry substantial promise in meeting the growing demands of mental health care, albeit relatively unexplored. As such, this study sought to examine LLMs' capability to generate empathetic responses in conversations that emulate those in a mental health counselling setting. We selected five LLMs: version 3.5 and version 4 of the Generative Pre-training (GPT), Vicuna FastChat-T5, Pathways Language Model (PaLM) version 2, and Falcon-7B-Instruct. Based on a simple instructional prompt, these models responded to utterances derived from the EmpatheticDialogues (ED) dataset. Using three empathy-related metrics, we compared their responses to those from traditional response generation dialogue systems, which were fine-tuned on the ED dataset, along with human-generated responses. Notably, we discovered that responses from the LLMs were remarkably more empathetic in most scenarios. We position our findings in light of catapulting advancements in creating empathetic conversational systems. Comment: 7 pages, 1 figure
Keywords	Computer Science - Computation and Language ; I.2
Subject code	121
Publishing date	2023-10-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Users' Beliefs Toward Physical Distancing in Facebook Pages of Public Health Authorities During COVID-19 Pandemic in Early 2020.

Tan, Soon Guan / Sesagiri Raamkumar, Aravind / Wee, Hwee Lin

Health education & behavior : the official publication of the Society for Public Health Education

2021 Volume 48, Issue 4, Page(s) 404–411

Abstract: This study aims to describe Facebook users' beliefs toward physical distancing measures implemented during the Coronavirus disease (COVID-19) pandemic using the key constructs of the health belief model. A combination of rule-based filtering and manual ... ...

Abstract	This study aims to describe Facebook users' beliefs toward physical distancing measures implemented during the Coronavirus disease (COVID-19) pandemic using the key constructs of the health belief model. A combination of rule-based filtering and manual classification methods was used to classify user comments on COVID-19 Facebook posts of three public health authorities: Centers for Disease Control and Prevention of the United States, Public Health England, and Ministry of Health, Singapore. A total of 104,304 comments were analyzed for posts published between 1 January, 2020, and 31 March, 2020, along with COVID-19 cases and deaths count data from the three countries. Findings indicate that the perceived benefits of physical distancing measures (
MeSH term(s)	COVID-19 ; Humans ; Pandemics/prevention & control ; Physical Distancing ; Public Health ; SARS-CoV-2 ; Social Media ; United States
Language	English
Publishing date	2021-05-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1362906-2
ISSN	1552-6127 ; 1090-1981
ISSN (online)	1552-6127
ISSN	1090-1981
DOI	10.1177/10901981211014428
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Book ; Online: Empathetic Conversational Systems

Raamkumar, Aravind Sesagiri / Yang, Yinping

A Review of Current Advances, Gaps, and Opportunities

2022

Abstract: Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to ... ...

Abstract	Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features. Comment: 20 pages, 3 figures, 4 tables
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; I.2
Subject code	004
Publishing date	2022-05-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Digital Support for Renal Patients Before and During the COVID-19 Pandemic: Examining the Efforts of Singapore Social Service Agencies in Facebook.

Tan, Junjie / Sesagiri Raamkumar, Aravind / Wee, Hwee Lin

Frontiers in big data

2021 Volume 4, Page(s) 737507

Abstract: During the coronavirus disease 2019 (COVID-19) pandemic, social service agencies (SSAs) play a crucial role in supporting renal patients, who are particularly vulnerable to infections. Social media platforms such as Facebook, serves as an effective ... ...

Abstract	During the coronavirus disease 2019 (COVID-19) pandemic, social service agencies (SSAs) play a crucial role in supporting renal patients, who are particularly vulnerable to infections. Social media platforms such as Facebook, serves as an effective medium for these SSAs to disseminate information. Content analysis of the SSAs' Facebook posts can provide insights on whether Facebook has been adequately utilized during the COVID-19 pandemic and enable SSAs to improve their social media use in future pandemics. This study aimed to compare renal-related SSAs' Facebook post content before and during the COVID-19 pandemic. Facebook posts of three SSAs National Kidney Foundation (NKF), Kidney Dialysis Foundation (KDF), and Muslim Kidney Action Association (MKAC), posted during the pre-COVID-19 period (January 23, 2019 to June 2, 2019) and the peri-COVID-19 period (January 23, 2020 to June 1, 2020) were extracted. A classification scheme was developed by two coders with themes derived inductively and deductively. Each Facebook post was assigned with a theme. Quantitative analyses indicate that the number of Facebook posts increased from 115 in the pre-COVID-19 period to 293 in the peri-COVID-19 period. During peri-COVID-19, posts regarding lifestyle changes, donations and infectious disease surfaced. While the proportion of posts about encouraging kindness increased from one to 77 posts, the proportion of posts about community-based events and psychosocial support decreased from 44 to 15 posts and 17 to 10 posts respectively during the two periods. Facebook was found to be well-utilized by two of the three renal SSAs in engaging their beneficiaries during the pandemic. During future pandemics, renal SSAs should place emphasis on posts related to psychosocial support and encouraging kindness. Further studies are required to ascertain the impact of COVID-19 from the perspective of renal patients and also to validate the classification scheme which was developed in this study. The study's methodology and classification scheme can be used to guide future studies for evaluating the social media outreach performance of renal health support groups.
Language	English
Publishing date	2021-09-14
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-909X
ISSN (online)	2624-909X
DOI	10.3389/fdata.2021.737507
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Impact of COVID-19 on the outreach strategy of cancer social service agencies in Singapore: A pre-post analysis with Facebook data.

Ethan Tan, Kieran / Sesagiri Raamkumar, Aravind / Wee, Hwee Lin

Journal of biomedical informatics

2021 Volume 118, Page(s) 103798

Abstract: The Singapore government implemented multiple restrictive measures as the novel coronavirus infection (COVID-19) spread through the community, thereby affecting the support service of cancer-related social service agencies (cancer-SSAs). We are ... ...

Abstract	The Singapore government implemented multiple restrictive measures as the novel coronavirus infection (COVID-19) spread through the community, thereby affecting the support service of cancer-related social service agencies (cancer-SSAs). We are interested to understand how Singapore's cancer-SSAs utilized the social media platform Facebook to overcome the restrictions which were introduced due to COVID-19. Facebook posts from two prominent cancer-SSAs 365 Cancer Prevention Society (365CPS) and Singapore Cancer Society (SCS) between comparable periods in 2019 and 2020 were extracted. These posts were categorized using a classification scheme which was specifically developed by two coders with adequate intercoder reliability. Statistical analyses were performed to determine if there was a significant difference in the frequency of posts between 2019 and 2020, and across three specific periods in 2020. Results indicate that 365CPS appears to have adapted to the pandemic by increasing their posting frequency on Facebook in 2020, but the same was not evident for SCS. However, both SSAs tweaked their social media outreach strategy in line with social distancing measures, publishing posts detailing activities that beneficiaries can participate from home such as healthy recipes and virtual events. SSAs can scale up their efforts to achieve a higher level of health promotion and support for their beneficiaries. Further qualitative studies are required to ascertain the impact of COVID-19 from the perspective of beneficiaries and validate the classification scheme which was developed in this study. The classification scheme could be used in ascertaining the quality of social media communication between public/private support groups and patients.
MeSH term(s)	COVID-19 ; Humans ; Neoplasms/prevention & control ; Reproducibility of Results ; Singapore ; Social Media ; Social Work/organization & administration
Language	English
Publishing date	2021-05-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2021.103798
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Article ; Online: Use of Health Belief Model-Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study.

Sesagiri Raamkumar, Aravind / Tan, Soon Guan / Wee, Hwee Lin

JMIR public health and surveillance

2020 Volume 6, Issue 3, Page(s) e20493

Abstract: Background: Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions ... ...

Abstract	Background: Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public. Objective: This study is aimed at developing and evaluating deep learning-based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study. Methods: The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19-related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit-based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study. Results: The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study. Conclusions: The deep learning-based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.
MeSH term(s)	COVID-19 ; Coronavirus Infections/prevention & control ; Deep Learning ; Health Knowledge, Attitudes, Practice ; Humans ; Models, Psychological ; Pandemics/prevention & control ; Physical Distancing ; Pneumonia, Viral/prevention & control ; Social Media/statistics & numerical data
Keywords	covid19
Language	English
Publishing date	2020-07-14
Publishing country	Canada
Document type	Journal Article
ISSN	2369-2960
ISSN (online)	2369-2960
DOI	10.2196/20493
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook During the COVID-19 Pandemic in Early 2020: Cross-Country Comparison.

Sesagiri Raamkumar, Aravind / Tan, Soon Guan / Wee, Hwee Lin

Journal of medical Internet research

2020 Volume 22, Issue 5, Page(s) e19334

Abstract: Background: The coronavirus disease (COVID-19) pandemic presents one of the most challenging global crises at the dawn of a new decade. Public health authorities (PHAs) are increasingly adopting the use of social media such as Facebook to rapidly ... ...

Abstract	Background: The coronavirus disease (COVID-19) pandemic presents one of the most challenging global crises at the dawn of a new decade. Public health authorities (PHAs) are increasingly adopting the use of social media such as Facebook to rapidly communicate and disseminate pandemic response measures to the public. Understanding of communication strategies across different PHAs and examining the public response on the social media landscapes can help improve practices for disseminating information to the public. Objective: This study aims to examine COVID-19-related outreach efforts of PHAs in Singapore, the United States, and England, and the corresponding public response to these outreach efforts on Facebook. Methods: Posts and comments from the Facebook pages of the Ministry of Health (MOH) in Singapore, the Centers for Disease Control and Prevention (CDC) in the United States, and Public Health England (PHE) in England were extracted from January 1, 2019, to March 18, 2020. Posts published before January 1, 2020, were categorized as pre-COVID-19, while the remaining posts were categorized as peri-COVID-19 posts. COVID-19-related posts were identified and classified into themes. Metrics used for measuring outreach and engagement were frequency, mean posts per day (PPD), mean reactions per post, mean shares per post, and mean comments per post. Responses to the COVID-19 posts were measured using frequency, mean sentiment polarity, positive to negative sentiments ratio (PNSR), and positive to negative emotions ratio (PNER). Toxicity in comments were identified and analyzed using frequency, mean likes per toxic comment, and mean replies per toxic comment. Trend analysis was performed to examine how the metrics varied with key events such as when COVID-19 was declared a pandemic. Results: The MOH published more COVID-19 posts (n=271; mean PPD 5.0) compared to the CDC (n=94; mean PPD 2.2) and PHE (n=45; mean PPD 1.4). The mean number of comments per COVID-19 post was highest for the CDC (mean CPP 255.3) compared to the MOH (mean CPP 15.6) and PHE (mean CPP 12.5). Six major themes were identified, with posts about prevention and safety measures and situation updates being prevalent across the three PHAs. The themes of the MOH's posts were diverse, while the CDC and PHE posts focused on a few themes. Overall, response sentiments for the MOH posts (PNSR 0.94) were more favorable compared to response sentiments for the CDC (PNSR 0.57) and PHE (PNSR 0.55) posts. Toxic comments were rare (0.01%) across all PHAs. Conclusions: PHAs' extent of Facebook use for outreach purposes during the COVID-19 pandemic varied among the three PHAs, highlighting the strategies and approaches that other PHAs can potentially adopt. Our study showed that social media analysis was capable of providing insights about the communication strategies of PHAs during disease outbreaks.
MeSH term(s)	Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; England/epidemiology ; Humans ; Information Dissemination ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Public Health ; SARS-CoV-2 ; Singapore/epidemiology ; Social Media ; United States/epidemiology
Keywords	covid19
Language	English
Publishing date	2020-05-19
Publishing country	Canada
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/19334
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Zika reveals India's risk communication challenges and needs.

Vijaykumar, Santosh / Raamkumar, Aravind Sesagiri

Indian journal of medical ethics

2018 Volume 3, Issue 3, Page(s) 240–244

Abstract: India's approach to disseminating information about the first three cases of the Zika virus was criticised nationally and internationally after the issue came to light in May 2017 through a World Health Organization news release. We analyse the incident ... ...

Abstract	India's approach to disseminating information about the first three cases of the Zika virus was criticised nationally and internationally after the issue came to light in May 2017 through a World Health Organization news release. We analyse the incident from a risk communication perspective. This commentary recaps the events and synthesises key arguments put forth by the news media and public health stakeholders. We use Peter Sandman's risk = hazard + outrage framework - also adopted by India's risk communication planners - to analyse India's risk communication response and contextualise it against the mandate of the National Risk Communication Plan and Integrated Disease Surveillance Programme. We conclude with recommendations for India's risk communication policymakers, including the need to develop capacity for risk communication research and scholarship in the country.
MeSH term(s)	Communication ; Disease Outbreaks ; Humans ; India ; Mass Media ; Public Health ; Risk ; Zika Virus ; Zika Virus Infection/virology
Language	English
Publishing date	2018-04-12
Publishing country	India
Document type	Journal Article
ISSN	0975-5691
ISSN (online)	0975-5691
DOI	:10.20529/IJME.2018.027
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Article ; Online: Themes, communities and influencers of online probiotics chatter: A retrospective analysis from 2009-2017.

Vijaykumar, Santosh / Raamkumar, Aravind Sesagiri / McCarty, Kristofor / Mutumbwa, Cuthbert / Mustafa, Jawwad / Au, Cyndy

PloS one

2021 Volume 16, Issue 10, Page(s) e0258098

Abstract: We build on recent examinations questioning the quality of online information about probiotic products by studying the themes of content, detecting virtual communities and identifying key influencers in social media using data science techniques. We ... ...

Abstract	We build on recent examinations questioning the quality of online information about probiotic products by studying the themes of content, detecting virtual communities and identifying key influencers in social media using data science techniques. We conducted topic modelling (n = 36,715 tweets) and longitudinal social network analysis (n = 17,834 tweets) of probiotic chatter on Twitter from 2009-17. We used Latent Dirichlet Allocation (LDA) to build the topic models and network analysis tool Gephi for building yearly graphs. We identified the top 10 topics of probiotics-related communication on Twitter and a constant rise in communication activity. However the number of communities grew consistently to peak in 2014 before dipping and levelling off by 2017. While several probiotics industry actors appeared and disappeared during this period, the influence of one specific actor rose from a hub initially to an authority in the latter years. With multi-brand advertising and probiotics promotions mostly occupying the Twitter chatter, scientists, journalists, or policymakers exerted minimal influence in these communities. Consistent with previous research, we find that probiotics-related content on social media veers towards promotions and benefits. Probiotic industry actors maintain consistent presence on Twitter while transitioning from hubs to authorities over time; scientific entities assume an authoritative role without much engagement. The involvement of scientific, journalistic or regulatory stakeholders will help create a balanced informational environment surrounding probiotic products.
MeSH term(s)	Advertising/methods ; Humans ; Longitudinal Studies ; Probiotics/administration & dosage ; Retrospective Studies ; Social Media/statistics & numerical data
Language	English
Publishing date	2021-10-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0258098
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Impact of COVID-19 on the outreach strategy of cancer social service agencies in Singapore: A pre-post analysis with Facebook data

Tan, Kieran Ethan / Sesagiri Raamkumar, Aravind / Wee, Hwee Lin

medRxiv

Abstract	The Singapore government implemented multiple restrictive measures as the novel coronavirus infection (COVID-19) spread through the community, thereby affecting the support service of cancer-related social service agencies (cancer-SSAs). We are interested to understand how Singapore9s cancer-SSAs utilized the social media platform Facebook to overcome the restrictions which were introduced due to COVID-19. Facebook posts from cancer-SSAs 365 Cancer Prevention Society (365CPS) and Singapore Cancer Society (SCS) between comparable periods in 2019 and 2020 were extracted. These posts were categorized using a classification scheme specifically developed for this study. Statistical analyses were performed to determine if there was a significant difference in the frequency of posts between 2019 and 2020, and across three specific periods in 2020. Results indicate that 365CPS appears to have adapted to the pandemic by increasing their posting frequency on Facebook in 2020, but the same was not evident for SCS. However, both SSAs tweaked their social media outreach strategy in line with social distancing measures, publishing posts detailing activities that beneficiaries can participate from home such as healthy recipes and virtual events. SSAs can scale up their efforts to achieve a higher level of health promotion and support for their beneficiaries.
Keywords	covid19
Language	English
Publishing date	2020-11-13
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.11.11.20229740
Database	COVID19

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