Wrong Information Research Articles

Knowledge and Attitude Regarding Social Media as a Source of Information among Farmers of District Kota, Rajasthan

The present study was undertaken to assess the knowledge and attitude of farmers regarding social media in the Kota district of Rajasthan, during the year 2021-22. The study used purposive sampling to select one Block, namely Khairabad and simple random sampling to select four villages from the block. In total four villages were selected for the study, and a total sample of 120 social media users was chosen using proportionate simple random sampling technique. The study revealed that the maximum respondent was middle-aged (42.5%), while farmers (50.8%) had small land holdings. The maximum number of respondents having a medium level of economic motivation, and social media exposure, and about fifty-three percent (52.5%) of the respondents have medium level of knowledge about social media. The majority of the farmers (65.8%) have maximum knowledge about social media are very useful but sometimes it helps in spreading wrong information, while less than fifty percent (49.2%) the of farmer have a medium-level attitude regarding social media. Age, Occupation, Educational qualification, Farm power, and Economic motivation are positively and significantly correlated at 0.01% level of probability and Family size, Landholding, social media exposure is positively and Family size, Landholding, social media exposure is significantly at 0.05% level with the knowledge toward use of social media. On the other hand, all the independent variables were significantly and positively correlated with attitude level at 0.01% and 0.05%.

Real or fake? Measuring the impact of protein annotation errors on estimates of domain gain and loss events.

Protein annotation errors can have significant consequences in a wide range of fields, ranging from protein structure and function prediction to biomedical research, drug discovery, and biotechnology. By comparing the domains of different proteins, scientists can identify common domains, classify proteins based on their domain architecture, and highlight proteins that have evolved differently in one or more species or clades. However, genome-wide identification of different protein domain architectures involves a complex error-prone pipeline that includes genome sequencing, prediction of gene exon/intron structures, and inference of protein sequences and domain annotations. Here we developed an automated fact-checking approach to distinguish true domain loss/gain events from false events caused by errors that occur during the annotation process. Using genome-wide ortholog sets and taking advantage of the high-quality human and Saccharomyces cerevisiae genome annotations, we analyzed the domain gain and loss events in the predicted proteomes of 9 non-human primates (NHP) and 20 non-S. cerevisiae fungi (NSF) as annotated in the Uniprot and Interpro databases. Our approach allowed us to quantify the impact of errors on estimates of protein domain gains and losses, and we show that domain losses are over-estimated ten-fold and three-fold in the NHP and NSF proteins respectively. This is in line with previous studies of gene-level losses, where issues with genome sequencing or gene annotation led to genes being falsely inferred as absent. In addition, we show that insistent protein domain annotations are a major factor contributing to the false events. For the first time, to our knowledge, we show that domain gains are also over-estimated by three-fold and two-fold respectively in NHP and NSF proteins. Based on our more accurate estimates, we infer that true domain losses and gains in NHP with respect to humans are observed at similar rates, while domain gains in the more divergent NSF are observed twice as frequently as domain losses with respect to S. cerevisiae. This study highlights the need to critically examine the scientific validity of protein annotations, and represents a significant step toward scalable computational fact-checking methods that may 1day mitigate the propagation of wrong information in protein databases.

A survey study on the Genetically Modified Organism (GMO) knowledge levels and attitudes of the consumers

This research was conducted in the form of a survey study to determine the knowledge levels and attitudes of consumers towards genetically modified organism (GMO). The study was carried out in a 4-month period between September and December 2022. As a data collection tool in the research, questions were asked to determine the knowledge level and attitudes of consumers towards GMO products. The findings of the study revealed that the participants were aware of genetically modified products and the majority of them thought that genetically modified foods were harmful. As a result of the data obtained, it is understood that consumers have some misconceptions about GMO and that important awareness trainings are needed to eliminate these misconceptions. In other words, it has been understood that consumers confuse issues such as GMO, pesticide and antibiotic residue from hormone application, and they have wrong information or prejudice on the subject. Another important issue that consumers will face in the future and somehow need to be informed about the features of the products offered for sale in the market.

Rights of the Women Worker of Readymade Garment Industry in Bangladesh: Gaps Between Law and Practice

Readymade garment industry is one of the most important sources of foreign currency of Bangladesh as well as this sector is the largest employment sector of women. There are more than 5000 garment factories in Bangladesh where 85% of the workers are women. There is legal framework to protect the rights of the women worker but in practice their rights are violated. The working condition, health and safety, minimum wages, working hours, maternity welfare facilities, difficulty to form trade union are the key areas where the rights of the women worker are violated most. Violation of the rights have thrown the women worker to vulnerable situation. This paper attempt to find out the rights of women worker and how the practice of violation of rights projected them to vulnerability. It also seeks to find out the cause of violation of the women workers rights and finally concludes with recommendations to implement the law in practice. This study has suffered from some limitations. Simple Random Sampling method used in this study may cause the problem of getting data from one part beside others which may have an effect on getting accurate results. Beside this, many women worker provide wrong information without understanding the fact.

Scene Change aware Inter-Frame Forgeries Detection Technique for Surveillance Videos Based on Similarities Analysis

Surveillance videos provide security and increases work efficiency in places of work and homes. as the most acceptable form of evidence, surveillance videos are now tampered to hide actions or convey wrong information. Researchers have proposed ways to mitigate the effect of activities of the attackers through checking the authenticity of the video. The proposed schemes suffer performance degradation in the presence of scene changes. Recently a scheme that addresses the effects of scene change on inter-frame forgery detection was developed where it detects scene changes and divides multiple scenes in to shots. The scheme improves the overall performance of the inter-frame forgery detection at the expense of high average computational time. In this research, a video scene change aware forgery detection scheme is proposed to mitigate the effect of scene change on inter-frame forgery detection with low average computational time. The proposed scheme utilizes the luminance level within frame region which is a more efficient feature to detect scene change. The experimental results show that the scheme has 57% decreases in computational average time and increased in accuracy to 99.03%.

Trust evaluation of health websites by eliminating phishing websites and using similarity techniques

SummaryEvery user uses a search engine to find health information from websites. Content‐rich health websites are considered in our research as wrong information in these websites can threaten life. Search engines give a list of URLs related to their search keyword. Generally, the user follows the top websites displayed by the search engine. Newly constructed websites do not have ratings, hit counts, and reviews. The search engine does not display newly constructed websites in their top rank. In such a case, the newly constructed website with the same content as the website displayed at the top of the search engine loses the user's trust. Another problem is; the phishing website URLs are also displayed by the Google Search engine, which appear similar to the genuine websites. To solve the problem and enhance the trust of health websites which is not at the top of the search engine among users, we have proposed an approach that extracts all URLs based on the keyword. It identifies all legitimate URLs using a Machine Learning classifier. Address bar features, Domain name features, HTML, and JavaScript features were identified for the dataset of getting legitimate URLs. Three classifiers (Decision Tree, Random Forest, and Support Vector Machine) were trained and evaluated. Decision Tree has the highest training accuracy, 94.125, testing accuracy, 92.75, and precision score of 96.97. The cross‐validation score of all three models is almost 93. Therefore, Decision tree is used to identify legitimate websites. After getting the list of legitimate URLs, all the content of the legitimate website is extracted. A Semantic Similarity between top‐rank legitimate website content and legitimate websites is found using Natural language processing techniques. Then the websites are ranked based on similarity and the value of the trust is assigned from highly trustable to less trustable. We have compared and correlated our results with the Web of Trust, a reputation tool for trust analysis, and have achieved a positive correlation. Thus, our approach removes phishing websites and enhances the trust in other websites that are not at the top of the search engine.

BERPIKIR KRITIS SISWA BERKEPRIBADIAN SENSING, INTUITION, THINKING DAN FEELING DALAM MENYELESAIKAN MASALAH DENGAN INFORMASI KONTRADIKSI

Critical thinking skill is necessary to be learned by every students, because by thinking critically students are able to understand other mathematical skills. One of the problems that can improve critical thinking skills is Problem With Contradiction Information (PWCI), which means problem that contains wrong information. Every student has their own characteristics, it also influences their critical thinking. One of the ways to identify a type of personality people is by using the Myers Briggs Type Indicator (MBTI), which classifies humans based on several perspectives, such as how to understanding and receiving information (Sennsing-Intuition), also how to make a conclusion and decision (Thinking-Feeling). The purpose of this study is to describe the critical thinking skills of students with sensing, intuition, thinking and feeling personalities. This research is a descriptive study with a qualitative approach. This research was based on SMAN Arjasa, and the subject was 8 students consisting of 2 students each with sensing, intuition, thinking and feeling personality. Data collection methods used are test and interview. The analysis technique used is the analysis of test results, interviews and triangulation. The results of this research revealed that students with sensing, intuition, thinking and feeling personality types have their own characteristics when solving the problem. The results of the critical thinking test found that there were no students who were able to fulfill critical thinking skills but there were students who were able to find contradictory information in the question

Rajaswala Paricharya- An Essential Approach Towards Female Through the Lens of Ayurveda Science

Ayurveda recognizes menstruation as a physiological and self-purifying process; it is also governed by the actions of the Doshas. Ayurveda has prescribed a mode of life to be adopted by menstruating women - a series of Do’s and Don’ts - called as Rajaswala Paricharya, which aims to protect the health of the menstruating woman and prevent any health defects in the child, in case any conception happens. Menstrual Dysfunctions Risk Factors are conventionally divided into social and medico-biological. Unfortunately, it has been observed that the Rajaswala Paricharya is neither being publicized nor followed by the women in modern era. Also, the wrong information spread by media and manufacturers of sanitary napkins. WHO, UNICEF, NHM-GOI all are emphasizing on improving the reproductive health of adolescent girls and supports the accessible WASH facilities. But, they do not emphasize certain do’s and don'ts during menstrual days. So, Ayurveda physician has to promote this Paricharya for enhancement of reproductive health of female. With little effort, women naturally increased observance of the restrictions as they practiced them and experienced the benefits. Aim of this is to avoid vitiation of Vata, Pitta and Kapha, prevent formation of Ama and restore the Bala and nurture the Agni of the female. Thus, developing a correct and a positive outlook towards menstruation i.e., Rajaswala Paricharya is as important as practicing them. Girls may be benefited from it by responding appropriately to physical and psychological changes that occur during menstruation as well as by experiencing fewer symptoms.

Abstract P1-08-03: EXPLORATORY ANALYSIS OF A MOBILE APP THAT ADDRESSES RADIOTHERAPY INFORMATIONAL NEEDS FOR BREAST CANCER PATIENTS

Abstract INTRODUCTION Radiotherapy is an area of medicine that is little known by patients and that requires a long time for the doctor to be able to explain and resolve the doubts that arise in the first consultation, which may cause anxiety and low adherence to treatment recommendations. A mobile app called Canswer was developed to address this problem in a novel way to help patients learn about radiation oncology in their free time. OBJECTIVE Developed a mobile app that provides oncology radiotherapy information to patients. MATERIAL AND METHODS The patients who attended the consultation for the first time were provided with information and obtained verbal consent, later they were provided with the Canswer application on their mobile device, and at the end, a Likert-type perception survey was applied and the Cancer Treatment Survey (CATS), which analyzed the information needs of patients. Anxiety and depression were analyzed through the Hospital Anxiety and Depression Scale (HADS). RESULTS Of the total number of patients recruited, 17 patients from northeast Mexico with breast cancer agreed that the use of the application would be useful to them and were interested in receiving additional information after their first-time radiotherapy consultation from March to June 2022. Average age of respondents was 45 years. A total of 7 patients (41%) had doubts about radiotherapy after consultation. When analyzing their knowledge, only 1 patient identified that radiotherapy works by DNA damage, the rest had wrong information, 14 patients thought that this treatment works by heating the cells; 2 thought that radiation therapy causes cells to burst. 8 patients (47%) understood how teletherapy works and 5 how brachytherapy works (19%). A question was included about the best skin care during breast radiotherapy in which 76% correctly answered that the skin should be washed daily with water and neutral soap, but 24% answered that hydrating creams should be applied just before stepping into the machine. Regarding the duration of the consultation, a total of 10 patients (60%) thought it was too short. 47% felt the limited time of the consultation did not allow an adequate evaluation. 100% of the respondents were satisfied with the information provided about the type of treatment, its benefits, and adverse effects, however, 47% persisted with doubts at the end of the consultation. 47% of patients (n=8) had some level of anxiety with 29% mild, 11% moderate and 6% severe. Incidence of depression was 24% with 18% having mild form and 6% severe. Regarding the information provided by the doctor during the consultation, 8 patients (47%) would like their doctor to have told them more about anxiety and depression, 7 about sexuality concerns (41%), and 6 (35%) about alternative medicine. 79% of patients wanted to know more about the side effects the treatment might cause after their visit, and 71% reported wanting to know more about how to prevent side effects. 66% also reported that they would like more information about how the treatment feels when it begins. DISCUSSION AND CONCLUSION This analysis revealed that most patients have doubts about radiotherapy, even after the information provided in their first consultation. This may be multifactorial but a key factor may be work overload at radiotherapy centers and a consultation of short duration. This application seems to be an effective, easily accessible, free tool that provides the necessary information, even on topics that are difficult to explain in a standard consultation. Important aspects that the app needs to focus on in future updates include side effects and their management, sexuality, nutrition, depression, and alternative medicine. This application also revealed misconceptions that should be routinely addressed, such as skin care during treatment. Link to app: https://www.canswer.info/?page_id=393 Citation Format: Jose F. Muñoz Lozano, Diana Cristina Pérez Ibave, Estefania Abundis Marquez, Fernando Alcorta Nuñez, Celia B. Gonzalez Alcorta, Carlos Salazar Mejia, Maria Fernanda Noriega, Omar Zayas Villanueva, Victor Oyervides Juarez, Larisa M. Renteria Garcia, Adelina Alcorta Garza, Juan Francisco Gonzalez Guerrero, Valeria Luna, David Hernandez, Rafael Piñeiro Retif, Oscar Vidal Gutiérrez. EXPLORATORY ANALYSIS OF A MOBILE APP THAT ADDRESSES RADIOTHERAPY INFORMATIONAL NEEDS FOR BREAST CANCER PATIENTS [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-08-03.

Lessons from the COVID-19 Pandemic: Strengthening our Commitment to Eye Care

Lessons from the COVID-19 Pandemic: Strengthening our Commitment to Eye Care

Feature Selection Model using Naive Bayes ML Algorithm for WSN Intrusion Detection System

Intrusion detection models using machine-learning algorithms are used for Intrusion prediction and prevention purposes. Wireless sensor network has a possibility of being attacked by various kinds of threats that will de-promote the performance of any network. These WSN are also affected by the sensor networks that send wrong information because of some environmental causes in- built disturbances misaligned management of the sensors in creating intrusion to the wireless sensor networks. Even though signified routing protocols cannot assure the required security in wireless sensor networks. The idea system provides a key solution for this kind of problem that arises in the network and predicts the abnormal behavior of the sensor nodes as well. But built model by the proposed system various approaches in detecting these kinds of intrusions in any wireless sensor networks in the past few years. The proposed system methodology gives a phenomenon control over the wireless sensor network in detecting the inclusions in its early stages itself. The Data set pre-processing is done by a method of applying the minimum number of features for intrusion detection systems using a machine learning algorithm. The main scope of this article is to improve the prediction of intrusion in a wireless sensor network using AI- based algorithms. This also includes the finest feature selection methodologies to increase the performance of the built model using the selected classifier, which is the Bayes category algorithm. Performance accuracy in the prediction of different attacks in wireless sensor networks is attained at nearly 95.8% for six selected attributes, a Precision level of 0.958, and the receiver operating characteristics or the area under the curve is equal to 0.989.

A solution to invalid data in TMR-based electromagnetic tomography

Electrical tomography (ET) is a method to reconstruct the distribution of electrical parameters based on boundary measurement data. Electromagnetic tomography (EMT) based on tunneling magnetoresistance (TMR) is a new type of ET technology. More measurement information can be obtained by arranging more TMR detectors. Due to the system error, normalization is necessary before image reconstruction. However, it is observed that the amplitudes of some normalized measurement data are much larger than that of other data and contain wrong information, making the permeability distribution unable to be reconstructed. They are named invalid data. Through theoretical analysis, the reason for the invalid data is that the measuring point is exactly at the zero point of the radial magnetic induction intensity when the measurement data is normalized. Through simulation and experiment, the existence of invalid data and its harmful influence are verified, and the effectiveness of the solution of removing invalid data is also confirmed.

The relationship between depression, anxiety, e-health literacy, and health-promoting behavior in nursing students during COVID-19.

The purpose of this study was to identify the correlation between depression, anxiety, e-health literacy (eHL), and health-promoting behaviors among nursing students during the coronavirus disease 2019 (COVID-19) pandemic, and to identify the factors affecting health-promoting behaviors. A cross-sectional study was conducted online, recruiting 301 nursing students recruited from 4 universities in the Chungcheng Province and Daejeon Metropolitan City in South Korea between May 28 and June 30, 2021. Data were analyzed using SPSS WIN 27.0. The general characteristics of the study participants were analyzed by frequency and percentage, and the degree of depression, anxiety, eHL, and health-promoting behaviors were calculated as averages and standard deviations. Differences in health-promoting behaviors according to general characteristics were analyzed using independent t tests and analysis of variance, and a post hoc Scheffe test was conducted. Correlations between depression, anxiety, eHL, and health-promoting behaviors were measured using Pearson correlation matrices. Stepwise multiple regression was performed to identify factors affecting health-promoting behaviors. During the COVID-19 pandemic, the average level of depression and anxiety was reported to be 3.93 (4.71) and 3.40 (4.30), respectively; 33.2% of nursing students experienced more than mild depression and 29.2% experienced anxiety. The average eHL level was 3.91 (0.56), and the average of health-promoting behaviors was 2.43 (0.45). Depression was positively correlated with anxiety (R = 0.734, P < .001) and negatively correlated with health-promoting behaviors (r = -0.198, P = .001), whereas eHL had a positive correlation with health-promoting behaviors (R = 0.347, P < .001). The factors affecting health-promoting behaviors were religion (β = -0.160, standard error [SE] = 0.048), current health status (β = -0.097, SE = 0.032), frequency of searching the Internet for health-related information in a week (β = -0.070, SE = 0.026), interest in health (β = -0.191, SE = 0.039), and critical eHL (β = 0.243, SE = 0.040); the explanatory power was 27.4%. Results demonstrate that during the COVID-19 pandemic, higher depression and anxiety among nursing students decreased health-promoting behaviors, while higher eHL increased health-promoting behaviors, and eHL was a major factor affecting health-promoting behaviors. These results contribute to the provision of basic data for the development of nursing intervention programs and educational strategies that can establish correct health-promoting behaviors by managing depression and anxiety among nursing students and improving eHL.

Satellite Navigation Spoofing Interference Detection and Direction Finding Based on Array Antenna.

Satellite navigation signals are feeble when they reach the ground, so they are vulnerable to attacks from outside interference signals. By emitting spoofing interference signals similar to real satellite signals, spoofing interference can make receivers give wrong navigation, position, and time information, and it is challenging to detect. This seriously affects the safe use of GNSS; therefore, it is essential to identify spoofing interference signals quickly and accurately. In our study, we proposed a novel six-array spoofing-interference-monitoring array antenna, which achieved the detection and identification of spoofing interference sources by monitoring the relevant peaks and combining an airspace-trapping algorithm. Moreover, we quickly accomplished our search for the whole circumferential ambiguity by using long- and short-baseline algorithms, which can realize the high-precision detection of spoofing interference sources. To verify this method's accuracy, we conducted outdoor real experiments using a special spoofing interference source, and our experimental results show that our proposed array antenna's directional accuracy for spoofing interference signals is kept within 2°, showing high spoofing interference direction-finding capability.

Development of Android-based health media applications as promotional media in improving COVID-19 preventive behavior in the community

Corona Virus Disease 2019 (COVID-19) caused by SARS-CoV-2 became a worldwide pandemic. The spread of wrong information causes people to misperceive the information. The massive spread of the virus shows the lack of public knowledge about preventing the transmission of COVID-19. Intrapersonal factors, namely changes in individual behavior, are the main focus of breaking the chain of the spread of COVID-19. This study aims to develop an Android-based health education application for COVID-19 prevention behavior in the community. This research method used an exploratory design with an R&amp;D approach which consists of 2 stages. The first stage was making a prototype application through 1) Evaluation of public knowledge and attitudes about COVID-19 prevention with a questionnaire in the form of a google form to find strategic issues; 2) Development and mapping of prototype content through Focus Group Discussions with nurses and expert consultations. The evaluation used a simple random sampling technique on 193 respondents. The second stage was socialization and application feasibility testing on 193 respondents. The results of the first stage were an overview of the implementation of health education that had not been maximized due to the risk of transmission, sufficient knowledge (52.8%), and negative attitudes (63.2%) toward the prevention of COVID-19, as well as a prototype system of health education in applications that include prevention COVID-19 prevention. It was followed by the second stage of socialization and application feasibility testing for users. This study concludes that the development of an android-based application supports effective health promotion efforts in conveying information to increase knowledge and attitudes toward COVID-19 prevention.

Opinion on Distribution Nature of NEPSE Return: A Qualitative Analysis

The paper conducted a qualitative analysis among the Nepalese econophysicist-cum-investors regarding the opinions on the investment environment of the Nepalese stock market (NEPSE) and the challenges they have been facing while going for an investment decision. The paper has also gotten opinions on the screening process from the NEPSE from the interviewees and their opinion on the distribution nature of the return from the market. The paper followed the grounded theory to come with certain theories from the opinions. The paper found that the strong fundamentals of the listed companies were a concern for the interviewees. Similarly, wrong information and trading procedures were seen as concerns among interviewees. The paper found from interviews that the published financial reports of the listed companies helped in screening the shares to make a better portfolio among the Nepalese econophysicist-cum-investors. Finally, in the context of the distribution nature of NEPSE return, most interviewees viewed that the return follows the normal distribution. The interviewees viewed that the market return follows the power-law when the bull-run continues, backed by the extreme magnitude of trade volume as well as the perfect political and economic condition of the nation. They also linked the nature of return distribution with the behavioral factors of the investment decision process.

Audit‐based correction mechanism for malicious statistics information of data plane

SummaryIn software‐defined networking (SDN), the controller relies on the information collected from the data plane for route planning, load balancing, and other functions. Statistics information is the most important kind of information among them, so the correctness of statistics information is the key to the proper operation of the network. Most of the current research on data plane focuses on policy consistency, rule redundancy, forwarding anomalies, and so on, and little attention is paid to whether the statistics information uploaded by the switches to the controller is correct. However, incorrect statistics information inevitably leads the controller to make wrong decisions. Therefore, this paper proposes an audit‐based malicious information correction mechanism to address the problem of wrong statistics information uploaded by the switches. This mechanism audits the statistics information and locates malicious switches before uploading the statistics information to the controller. It identifies and corrects the statistics information errors by combining flow path and statistics information. We have performed simulations on Nsfnet, Abilene, and Fat‐Tree, and the results show that our method can correct about 70% of the statistical information errors with less computational cost. To the best of our knowledge, this paper is the first malicious statistics information correction scheme for wildcard rules.

Evaluation of accuracy, reliability, quality, and readability of online patient information materials on coccyx injury.

The aim of this research is to evaluate the websites containing "coccydynia, coccyx trauma or fracture" in terms of readability, reliability, accuracy, and quality. Searches for "coccydynia, coccyx trauma, coccyx fracture" were carried out in the 3 most used search engines in the USA: Google, Yahoo, and Bing in February 2022. A total of 141 websites were rated by 2 different neurosurgeons for the "Global Quality Score" and "Alexa Popularity Rank." 97.2% of the sites examined include the definition of the disease, 66% include the importance of the disease, 92.9% include the symptoms of the disease, 88.7% include the treatment of the disease, 78% include the signs of the disease, 77.3% include the mechanism of the disease It has been determined that the websites examined within the scope of the research have high global quality score (GQS) and APR and are enriched with images to a large extent.

Commodification of Viral Information in the New Media Era (Case Study on Cek Fakta Page of Tempo.Co)

The development of internet technology resulted in drastic changes in the flow of information globally. Now, people can make their own choice of information that they like. But on the other hand, they also have the potential to receive wrong information. Social media, where people interact a lot, also has a lot of false information or hoaxes. Meanwhile, the mass media, which used to be the gatekeepers of information, are no longer the main source of readers. As a way of defending itself from change, the media began to adapt to the flow of information on social media. They seize the opportunity by seeing the circulation of viral information that tends to be hoaxes. This study describes the adaptation of Tempo media to current conditions. This study uses an interpretive or constructivist paradigm. The research methodology rests on the foundation of ontological and epistemological assumptions. The author uses a media political economy approach in this study. In the media's political economy, there is commodification, converting use value into exchange value. This research shows the existence of the Cek Fakta web page on Tempo.co as a way to promote media name branding. Tempo has calculated the business on the Cek Fakta page at Tempo.co. Profits are not only material or money but also in terms of branding. Cek Facts is a tool for branding Tempo. From that point of view, in terms of traffic, the Cek Fakta page's existence contributes to many readers.

ORTHO AI : The Dawn Of A New Era: Artificial Intelligence In Orthopaedics

ORTHO AI : The Dawn Of A New Era: Artificial Intelligence In Orthopaedics