Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

Adishesha, Amogh Subbakrishna; Jakielaszek, Lily; Azhar, Fariha; Zhang, Peixuan; Honavar, Vasant; Ma, Fenglong; Belani, Chandra P.; Mitra, Prasenjit; Huang, Sharon X.

doi:10.1109/jbhi.2023.3271580

Cited by 3 publications

(3 citation statements)

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“…This may help patients make decisions and enhance treatment. Google and Amazon are creating AI systems that "learn" and empathize with patients [46,47]. These innovations might improve patient participation, individualized assistance, and care experiences.…”

Section: Social Media Ai Gpt and Metaverse Models For Public Health I...mentioning

confidence: 99%

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

Awan,

Gonzalez,

Sharma

2024

Information

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Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today’s culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. This review highlights the substantial influence of social media in advancing public health objectives. The key themes explored encompass the utilization of social media to advance health initiatives, monitor diseases, track behaviors, and interact with communities. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings show how social media has been used as a tool for research, implementing health campaigns, and health promotion. Social media integration with artificial intelligence (AI) and Generative Pre-Trained Transformers (GPTs) can impact and offer an innovative approach to tackle the problems and difficulties in health informatics. The research shows how social media will keep growing and evolving and, if used effectively, has the potential to help close public health gaps across different cultures and improve population health.

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Section: Social Media Ai Gpt and Metaverse Models For Public Health I...mentioning

confidence: 99%

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

Awan,

Gonzalez,

Sharma

2024

Information

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show abstract

“…These studies underscore the importance of credibility and integrity in online contenst. Adishesha et al [14] and Patro et al [15] explored forecasting user interests through topic tag predictions in online health communities and a conscious cross-breed recommendation approach for electronic commerce systems. Their work emphasizes the role of predictive analytics in understanding user behavior and preferences on social media platforms.…”

Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

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In an era where social media platforms burgeon with diverse content, effective content moderation becomes imperative to filter harmful materials. Traditional methods often grapple with the dual challenges of accuracy and computational efficiency levels. These conventional approaches typically rely on either text-based or image-based analysis, neglecting the complex interplay of multimodal content prevalent in social media scenarios. This limitation leads to suboptimal content filtering, often missing contextually nuanced or visually deceptive harmful content sets. Addressing these challenges, the proposed work introduces an innovative, hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Transformers for nuanced visual content extraction from social media posts. CNNs adeptly handle image features, while Transformers enrich the analysis with contextual textual understanding, thereby enhancing accuracy in identifying harmful contents. Additionally, a Bi-directional Attention Mechanism (BAM) is employed for efficient text-visual fusion, dynamically prioritizing relevant information and reducing computational loads. The fusion process is further refined using Genetic Algorithms (GAs) for hyperparameter optimization, streamlining the model's performance levels. The model's robustness is demonstrated through its ability to discern complex intra and inter-modal relationships within social media content, facilitated by Graph Neural Networks (GNNs). Moreover, the model's scalability and deployment efficiency on social media platforms are ensured through quantization and pruning techniques, balancing accuracy with computational practicality levels. Empirical testing on datasets from Google, Facebook, and Kaggle reveals the model's superior performance over existing methods, with notable improvements in precision, accuracy, recall, AUC, specificity, and response delay in detecting harmful contents. The model also excels in preempting potential harmful content posters, offering enhanced pre-emption metrics.

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“…This may help patients make decisions and enhance treatment. Google and Amazon are creating AI systems that "learn" and empathize with patients [47,48]. These innovations might improve patient participation, individualized assistance, and care experiences.…”

Section: 6mentioning

confidence: 99%

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

Awan,

Gonzalez,

Sharma

2023

Preprint

View full text Add to dashboard Cite

Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today's culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings showed how social media has been used as a tool for research, designing and implementing health campaigns, messaging, and health promotion. In addition, social media integration with Artificial Intelligence (AI) and Generative Pre-Trained transformers (GPT) can have a big impact and offer an innovative approach to tackle the problems and difficulties in health informatics. These innovations might improve patient participation, individualized assistance, and care experiences. The research showed how likely social media will keep growing and evolving and, if used effectively, have the potential to help close public health gaps across different cultures and improve population health.

show abstract

Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

Cited by 3 publications

References 46 publications

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions

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