Inoculating the Public Against Misinformation: Testing The Effectiveness of “Pre-bunking” Techniques in the Context of Mental Illness and Violence

The publics might be misinformed about mental illness due to frequently mentioned violence and inaccurate notions in news coverage that underlie people who have a history of mental illness are prone to be dangerous or violent. Drawing from the theories of inoculation and the psychological reactance, the author seeks to investigate the effects of using preemptive corrective message techniques (labeling, appeal to consensus, and provision of an alternative explanation) as on the method of promoting resistance to misinformation linking mental illness and violence, which is a harmful and unsubstantiated stereotype. To accomplish this research aim, the author conducted two experiments to examine the efficacy of inoculation messages and how these messages function in combating stigma and false beliefs in misinformation on the condition of source credibility. The findings show that labeling was the most effective among three inoculation interventions to reduce stigmatizing attitude, perceived credibility of misinformation, negative word of mouth, and increase intentions to debunk stereotypes and misconceptions after controlling preexisting positions and mental health knowledge. The direct and indirect effects of inoculation interventions on the outcomes were illustrated in PROCESS and SEM models. Theoretical contributions and implications for practitioners and future research are discussed

Public Perceptions Of Genetically Modified Food On Social Media: A Content Analysis Of Youtube Comments On Videos

Controversy about genetically modified (GM) food prevails on social media. Discussion about GM food includes the implementation of mandatory labeling as well as public concerns about potential health hazards posed by GM food. Previous studies mainly focus on traditional press and broadcast media, few investigate such controversial topics on social media. Interested in public opinion about this issue and possible influences of social media on public opinion, this study uses quantitative content analysis to examine the characteristics of user comments on a specific social media platform, YouTube. The purpose of the study is to investigate YouTube comments from several aspects, encompassing attributes, valence, sources cited to support opinions, motivation of commenting, along with other characteristics (uncertainty, interactivity, and hostility) embedded in these comments. In addition, the study also examines whether there are relationships between some of the variables mentioned above. Findings in this study showed that the most discussed issue related to GM food among YouTube users was informative education, and viewers were prone to comment in a negative tone. Interestingly, uncertainty manifested in the comments was associated with interactivity among commenters. Additionally, hostility toward GM food and mass media were highly associated with interactivity among commenters

Cervical Cell Image Classification-Based Knowledge Distillation

Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge distillation. This new method not only transfers common features between different source domain data, but also realizes model-to-model knowledge transfer using the unnormalized probability output between models as knowledge. A multi-exit classification network is then introduced as the student network, where a global context module is embedded in each exit branch. A self-distillation method is then proposed to fuse contextual information; deep classifiers in the student network guide shallow classifiers to learn, and multiple classifier outputs are fused using an average integration strategy to form a classifier with strong generalization performance. The experimental results show that the developed method achieves good results using the SIPaKMeD dataset. The accuracy, sensitivity, specificity, and F-measure of the five classifications are 98.52%, 98.53%, 98.68%, 98.59%, respectively. The effectiveness of the method is further verified on a natural image dataset

Cervical Cell/Clumps Detection in Cytology Images Using Transfer Learning

Cervical cancer is one of the most common and deadliest cancers among women and poses a serious health risk. Automated screening and diagnosis of cervical cancer will help improve the accuracy of cervical cell screening. In recent years, there have been many studies conducted using deep learning methods for automatic cervical cancer screening and diagnosis. Deep-learning-based Convolutional Neural Network (CNN) models require large amounts of data for training, but large cervical cell datasets with annotations are difficult to obtain. Some studies have used transfer learning approaches to handle this problem. However, such studies used the same transfer learning method that is the backbone network initialization by the ImageNet pre-trained model in two different types of tasks, the detection and classification of cervical cell/clumps. Considering the differences between detection and classification tasks, this study proposes the use of COCO pre-trained models when using deep learning methods for cervical cell/clumps detection tasks to better handle limited data set problem at training time. To further improve the model detection performance, based on transfer learning, we conducted multi-scale training according to the actual situation of the dataset. Considering the effect of bounding box loss on the precision of cervical cell/clumps detection, we analyzed the effects of different bounding box losses on the detection performance of the model and demonstrated that using a loss function consistent with the type of pre-trained model can help improve the model performance. We analyzed the effect of mean and std of different datasets on the performance of the model. It was demonstrated that the detection performance was optimal when using the mean and std of the cervical cell dataset used in the current study. Ultimately, based on backbone Resnet50, the mean Average Precision (mAP) of the network model is 61.6% and Average Recall (AR) is 87.7%. Compared to the current values of 48.8% and 64.0% in the used dataset, the model detection performance is significantly improved by 12.8% and 23.7%, respectively

Multiple-Stage Knowledge Distillation

Knowledge distillation (KD) is a method in which a teacher network guides the learning of a student network, thereby resulting in an improvement in the performance of the student network. Recent research in this area has concentrated on developing effective definitions of knowledge and efficient methods of knowledge transfer while ignoring the learning ability of the student network. To fully utilize this potential learning ability and improve learning efficiency, this study proposes a multiple-stage KD (MSKD) method that allows students to learn the knowledge delivered by the teacher network in multiple stages. The student network in this method consists of a multi-exit architecture, and the students imitate the output of the teacher network at each exit. The final classification by the student network is achieved through ensemble learning. However, because this results in an unreasonable gap between the number of parameters in the student branch network and those in the teacher branch network, as well as a mismatch in learning capacity between these two networks, we extend the MSKD method to a one-to-one multiple-stage KD method. The experimental results reveal that the proposed method applied to the CIFAR100 and Tiny ImageNet datasets exhibits good performance gain. The proposed method of enhancing KD by changing the style of student learning provides new insight into KD

What Shapes Environmental Responsibility Perceptions? Measuring Value Orientations as a Predictor of Situational Motivations and Communicative Action

This study investigates individuals' cognitive, motivational, and communication responses regarding an environmental CSR issue using arguments from the situational theory of problem-solving (STOPS) with a cross-situational factor as an antecedent. Survey results provide empirical support for the application of the STOPS in a CSR communication context and suggest that a collectivistic orientation predicts individuals' situational perceptions and cognitive reactions toward organizations' environmental CSR efforts. Theoretical and practical implications for strategic communicators are discussed

iTRAQ-Based Proteomics Analysis of Serum Proteins in Wistar Rats Treated with Sodium Fluoride: Insight into the Potential Mechanism and Candidate Biomarkers of Fluorosis

Fluorosis induced by exposure to high level fluoride is quite widespread in the world. The manifestations of fluorosis include dental mottling, bone damage, and impaired malfunction of soft tissues. However, the molecular mechanism of fluorosis has not been clarified until now. To explore the underlying mechanisms of fluorosis and screen out serum biomarkers, we carried out a quantitative proteomics study to identify differentially expressed serum proteins in Wistar rats treated with sodium fluoride (NaF) by using a proteomics approach of isobaric tagging for relative and absolute quantitation (iTRAQ). We fed Wistar rats drinking water that had 50, 150, and 250 mg/L of dissolved NaF for 24 weeks. For the experimental duration, each rat was given an examination of the lower incisors to check for the condition of dental fluorosis (DF). By the end of the treatment, fluoride ion concentration in serum and lower incisors were detected. The results showed that NaF treatment can induce rat fluorosis. By iTRAQ analysis, a total of 37 differentially expressed serum proteins were identified between NaF-treated and control rats. These proteins were further analyzed by bioinformatics, out of which two proteins were validated by enzyme-linked immunoadsorbent assays (ELISA). The major proteins were involved in complement and coagulation cascade, inflammatory response, complement activation, defense response, and wound response, suggesting that inflammation and immune reactions may play a key role in fluorosis pathogenesis. These proteins may contribute to the understanding of the mechanism of fluoride toxicity, and may serve as potential biomarkers for fluorosis