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The architectural design of sentiment analysis model, where x is the text and y is the sentiment. BERT: Bidirectional Encoder Representations from Transformers; ReLU: Rectified Linear Unit.
Source publication
Background:
Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analy...
Contexts in source publication
Context 1
... ReLu is more computationally efficient than other popular activation functions and mitigates the vanishing gradient problem. The architectural design for this model is illustrated in Figure 3. ...
Citations
... The introduction of a new COVID-19 Twitter dataset is a practical contribution, enabling researchers and public health professionals to explore and analyze pandemic-related discussions comprehensively. The development of a web application prototype further underscores the practical applicability of the research, providing a tangible tool for real-time monitoring and analysis of health-related content on Twitter [92,93]. ...
The COVID-19 pandemic has sparked widespread health-related discussions on social media platforms like Twitter (now named ‘X’). However, the lack of labeled Twitter data poses significant challenges for theme-based classification and tweet aggregation. To address this gap, we developed a machine learning-based web application that automatically classifies COVID-19 discourses into five categories: health risks, prevention, symptoms, transmission, and treatment. We collected and labeled 6,667 COVID-19-related tweets using the Twitter API, and applied various feature extraction methods to extract relevant features. We then compared the performance of seven classical machine learning algorithms (Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbor, Logistic Regression, and Linear SVC) and four deep learning techniques (LSTM, CNN, RNN, and BERT) for classification. Our results show that the CNN achieved the highest precision (90.41%), recall (90.4%), F1 score (90.4%), and accuracy (90.4%). The Linear SVC algorithm exhibited the highest precision (85.71%), recall (86.94%), and F1 score (86.13%) among classical machine learning approaches. Our study advances the field of health-related data analysis and classification, and offers a publicly accessible web-based tool for public health researchers and practitioners. This tool has the potential to support addressing public health challenges and enhancing awareness during pandemics. The dataset and application are accessible at https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.
... The use of electronic platforms for behavioral monitoring allows for real-time assessment of human behavior and can trigger an alert if measured behavior deviates from healthy norms [14,15]. Additionally, these platforms enable the collection of large amounts of high-frequency, high-dimensional continuous data, which can be used to identify typical multidimensional behavior features over an extended period based on naturalistic situations [16,17]. The growing body of literature leveraging behavioral monitoring for depression prediction has gained traction, spurred by the profound shifts in lifestyle behavior patterns, especially during the COVID-19 pandemic [18][19][20]. ...
Background
Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device–based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings.
Objective
The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression.
Methods
A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression.
Results
The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64).
Conclusions
This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression.
... However, from the perspective of experts in the field of CCs, it becomes apparent that merely knowing what information is required is insufficient. Instead, a comprehensive process of data storage, projection, and visualization, as highlighted by [96], is necessary. ...
There is a great challenge in the business sector to adopt new technologies that boost companies to break into Industry 4.0, especially to obtain the capacity to adopt and develop complex systems based on: artificial intelligence, Big Data, Data Mining, and Cyber Physical Systems. However, efforts tend to be more of an empirical process, rather than a prior analysis, that allows companies to identify the complexity of the situation and trigger a viable implementation. For this reason, this research carried out a systematic review to identify and analyze, from the Systems Science approach, the proposed and most used models to face these organizational problems. In total, 42 of the 3800 documents were filtered for discussion using a systems approach. In addition, one of the models was tested by interviews with Mexican managers to understand how it promotes the abstraction of complexity necessary for a viable system change. The findings at the end of the work were to determine the lack of systemic properties in the current proposals, especially in the efforts to adopt artificial intelligence and the need to have a suitable model for the context of technology.
... The CSV file data set was uploaded and processed to enhance the quality of the tweets for subsequent analysis [30]. Initially, duplicate tweets were identified and eliminated, resulting in a data set containing 21,169 unique tweets. ...
... To determine the underlying social issues surrounding the spread of fluoride-free content on Twitter, we applied topic modeling to identify patterns based on the frequency of keywords [29,30]. A higher coherence score is associated with better data quality and simplified output interpretation. ...
BACKGROUND
Online misinformation concerning the side effects of fluoridated oral care products and tap water contributes to the onset and propagation of untrue beliefs that culminate in anti-fluoridation movements.
OBJECTIVE
This study aimed to analyze the fluoride-related misinformation on Twitter automatically.
METHODS
21,169 tweets published in English between May 2016 and May 2022 that included the keyword “fluoride-free” were retrieved by Twitter API. Latent Dirichlet Allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. The total count of misinformation records for each topic and their relevance over time were determined.
RESULTS
Utilizing a coherence score of 0.542, a total of 3 distinctly distributed salient topics emerged from the LDA topic modeling analysis. Results show that fluoride-related misinformation on Twitter was mainly associated with people’s perception of a healthy lifestyle, followed by the consumption of natural and organic oral care products and recommendations of fluoride-free products and measures. Interest in false content decreased between 2016 and 2019 and increased again after 2020.
CONCLUSIONS
Fluoride misinformation found on Twitter related to a healthy lifestyle. This misleading content probably contributed to the popularization of fluoride-free oral care products and the suspension of community water fluoridation programs. Strategies are needed to address and limit the spread of misinformation on social media.
Public health crises, such as pandemics, natural disasters, and environmental emergencies, disproportionately affect vulnerable populations, exacerbating existing health disparities. Effective crisis communication requires an interdisciplinary approach that integrates public health strategies, social work interventions, and marketing-based behavioral insights to ensure accessibility, cultural competence, and engagement. This chapter explores the intersection of social work and social marketing in crisis communication, highlighting the role of social workers in advocating for equity, combating misinformation, and fostering trust within communities. Social marketing principles—such as audience segmentation, behavioral insights, and tailored messaging—are examined as tools to enhance public health outreach and promote behavior change. The study underscores the necessity of a holistic framework that combines social determinants of health, digital engagement strategies, and interdisciplinary collaboration to optimize crisis communication outcomes. The findings contribute to the development of inclusive, evidence-based public health interventions aligned with Sustainable Development Goals (SDGs), particularly in reducing inequalities and strengthening partnerships for global health resilience.
Background:
Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis-driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests.
Objective:
This study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time.
Methods:
A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software.
Results:
We identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward.
Conclusions:
Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population.
