Social Media News in Crisis? Popularity Analysis of the Top Nine Facebook Pages of Bangladeshi News Media

In the current digital era, the topics of media literacy and the trust placed in social media news are among the most pertinent research subjects due to their significant impacts on individuals. This study aims to examine the relationship between media literacy levels and trust in social media news among Generation Z, considered digital natives. Conducted with Journalism Department students at Uşak University Faculty of Communication, this study obtained 311 usable responses. It was found that students used social media for more than seven hours daily, with WhatsApp being the most frequently used platform. Both the stu-dents' media literacy levels and their trust in social media news were found to be high. No statistically significant difference was found between the students' descriptive characteris-tics such as age, gender and average social media usage and their media literacy and trust level in social media news (p>0.05). However, a positive, significant, and moderate rela-tionship (r: 0.382) was found between media literacy and trust in social media news (p<0.05). Regression analysis results showed that media literacy had a 14.6% effect on students' trust level in social media news (p<0.01). This result could be interpreted as the younger generation placing more trust in social media news due to reasons such as the traditional media's low levels of press freedom in Turkey and biased press reporting.

To enhance the detection of fake news in social media using artificial intelligence techniques and its performance is compared with apriori algorithm. Materials and Methods: The performance analysis has been done with the sample of (N=10) and compared with apriori (N=10), results were compared based on accuracy of both the algorithms. The significance level for support vector machine algorithms and Apriori algorithms having the values (p<0.05) with better performance. Result: The fake news in social media is detected by using the SVM algorithm and have a better accuracy of 91.87% than apriori algorithm with an accuracy of 31.76%. Conclusion: The implementation of this project shows the enhanced detection with support vector machine algorithm which is significantly higher than the apriori algorithm.

Sharing news in social media has influence on individuals as well as society and has become a global phenomenon. However, little empirical research has been conducted to understand why people share news in social media. Adopting the uses and gratifications theory, we investigate the gratification factors influencing news sharing intention on social media. A regression analysis was employed to analyze the data collected from 203 undergraduate and graduate students. The results show that informativeness was the strongest motivation in predicting news sharing intention, followed by socializing and status seeking. However, entertainment/escapism was not a significant predictor in contrast to prior work. Implications and opportunities for future work are also discussed.

The spread of medical fake news in social media – The pilot quantitative study

The study focused on issues relating to fake political news in social media andyouth participation in politics in South-East Nigeria. Specifically, it determinedyouths’ frequency of use of social media to gather political news, their majorsources of political news online, extent to which they verify credibility andauthenticity of political news and their sources, their level of exposure to fakepolitical news online, and ways fake news in social media influence theirparticipation in politics. Survey research design was adopted. Population of studywas made up of undergraduate students (youths) in South-East state universities,aged 18 to 29 years. Questionnaire was used for data collection. Data wereanalysed using frequency, percentages and mean. Findings reveal that manyyouths (56%) frequently use social media to gather political news especially fromfamily members, friends, special groups, influencers and opinion leaders’ pagesonline. They however, rarely verify quality of such political news and their varioussources. The findings further reveal that youths have been exposed to fake politicalnews online and this has negatively affected their political engagements,orientation, perception, interest and trust in elected government. In line with these,the researcher recommends continual use of social media platforms to disseminateverified, authentic and credible political news, introduction of compulsory courseon ‘how-to-spot-fake-news’ in the universities and development of updated factchecking apps that can verify political news emanating from short tweets, hashtags, posts, comments, unverified sources, and illogical stories.etc. There shouldalso be information websites created and handled by non-partisan persons whereyouths can access correct political information.

The essence of this publication is an attempt to characterize the term of fake news in the social media environment. Social media platforms are commercial entities that are difficult to control and focused on user-generated content. Therefore, they constitute an effective space for spreading dis-information, both intentional and unintentional. The definitions of fake news, their types and exam-ples were analyzed, and a case study was conducted in relation to two important events – both geo-politically and media-wise – the war in Ukraine and the presidential elections in the USA. The last chapter is a proposal of activities that will help you differentiate truth from false information and develop your own media competencies.

Misinformation becomes rampant in the digital age and social media provide people with the opportunities for engaging more actively in society. The objectives of the study are: (i) to ascertain the extent to which residents of Kano have been exposed to digital images on Covid-19 that often accompany fake news in social media; (ii) to establish the extent to which residents of Kano are influenced by fake news on Covid-19 with images; (iii) to find out the factors that often lead to the influence of fake news with digital images on Covid-19 among social media users in Kano; (iv) to ascertain the social media platforms mostly used in spreading fake news about Covid-19 in the state. Theoretically, Technological Determinism and Perception theories were adopted to analyze these issues. A Positivist approach to data generation and analysis was adopted using the survey method. Two local governments were selected for the study: Tarauni and Kano Municipal. Tarauni local government area had the highest number of the Covid-19 cases, while Kano Municipal had the lowest number. The population of the study consist of 593,087 with a sample size of 400 respondents derived from Taro Yamane’s sample size prediction table. The respondents were reached through cluster sampling. A total of 400 copies of questionnaires were administered to respondents in Tarauni and Kano Municipal. However, only 385 copies, which represent (96%), were retrieved and found usable for the study as the remaining 15 were not returned. The study found that Kano residents were significantly exposed to digital images that often accompany fake news in social media. They read news online every day, prefer news accompanied by images, share and like news online. The study also found that Kano residents are influenced by fake news with digital images on Covid-19 to a very great extent, especially on Facebook. Factors responsible for proliferation of fake news on social media include: perception or instinct, eagerness to be the first to share images and lack of knowledge about image verification tools. The study concludes that ignorance and the old belief that pictures do not lie were responsible for this.

Social media has become the primary source for rumor spreading, and information quality is an increasingly important issue in this context. In the last years, many researchers have been working on methods to improve the rumor classification, especially on the identification of fake news in social media, with good results. However, due to the complexity of natural language, this task presents difficult challenges, and many research opportunities. This survey analyzes 87 distinct publications, which were systematically selected out of 1333 candidates. This work covers eight years of research on fake news applied in social media and presents the main methods, text and user features, and datasets used in literature.

Since the outbreak of COVID-19, social media plays an important role to circulate pandemic news around the world. Some malevolent users may take an advantage of this and spread fake news to attract people for business and research purposes. In this paper, we take an approach by applying existing machine learning algorithms to detect fake news in social media and show a comparison of their performances. In our study, the support vector classifier (SVC) outperforms the rest of the classifiers based on different statistical metrics. Therefore, the SVC classifier has been considered as our proposed classifier model to identify fake COVID-19 news in social media. Two word clouds have also been generated based on the appearance of words in the news that shows an insignificant difference between true and fake news.

News sharing in social media: The effect of gratifications and prior experience

The fundamental purpose of the work is to detect the Fake News in Social Media with the use of Machine Learning Algorithms.TRUE and FAKE dataset is used to detect false news. This dataset carries the record of data i.e. TRUE or FAKE news. Fake News detection is accomplished via Logistic Regression and Naive Bayes classifier. Naive-Bayes algorithm is a simple approach mainly used for classification. Sample size has been determined to be 20 for both the groups using G Power 80%. Logistic Regression algorithm provides mean accuracy of 97.5% when compared to Naive Bayes algorithm with mean accuracy of 89.43%. Statistical significance value is obtained as 0.002 (p&lt;0.05). Logistic Regression has extensively higher accuracy than Naive Bayes algorithm.

News in social media, such as Twitter, has been generated in high volume and speed. However, very few of them are labeled (as fake or true news) by professionals in near real time. In order to achieve timely detection of fake news in social media, a novel framework of two-path deep semisupervised learning (SSL) is proposed where one path is for supervised learning and the other is for unsupervised learning. The supervised learning path learns on the limited amount of labeled data, while the unsupervised learning path is able to learn on a huge amount of unlabeled data. Furthermore, these two paths implemented with convolutional neural networks (CNNs) are jointly optimized to complete SSL. In addition, we build a shared CNN to extract the low-level features on both labeled data and unlabeled data to feed them into these two paths. To verify this framework, we implement a Word CNN-based SSL model and test it on two data sets: LIAR and PHEME. Experimental results demonstrate that the model built on the proposed framework can recognize fake news effectively with very few labeled data.

Combining Crowd and Machine Intelligence to Detect False News in Social Media

Through the advent of social media, news achieves a life of its own online. The media organisations partly lose control over the diffusion process, and simultaneously individuals gain power over the process, and become opinion leaders for others. This study focusses on news sharers and news shared (or rather, interacted), and has three RQ:s: 1) What characterises the people who share news in social media, 2) Have the characteristics of interacted news changed over time? and 3) Are there differences between news content interacted by ordinary people and news highlighted by media organisations? Two different studies have been conducted: A representative survey and a quantitative content analysis. &#x0D; The main results are that the opinion leaders differ from the majority by being younger, with a greater political interest, single and more digital in their general lifestyle, both concerning news consumption and other aspects. The content analysis shows that the most interacted news on social media follow the traditional news values rather well, with a few exceptions. Most apparent is that interacted news is more positive over time and compared to print front-page news. Accidents and crime dominate print front-pages, while politics is more prominent in interacted news.

Fake news (fake-news) existed long before the advent of the Internet and spread rather quickly via all possible means of communication as it is an effective tool for influencing public opinion. Currently, there are many definitions of fake news, but the professional community cannot fully agree on a single definition, which creates a big problem for its detection. Many large IT companies, such as Google and Facebook, are developing their own algorithms to protect the public from the falsification of information. At the same time, the lack of a common understanding regarding the essence of fake news makes the solution to this issue ideologically impossible. Consequently, experts and digital humanists specializing in different fields must study this problem intensively. This research analyzes the mechanisms for publishing and distributing fake-news according to the classification, structure and algorithm of the construction. Conclusions are then made on the methods for identifying this type of news in social media using systems with elements of artificial intelligence and machine learning.