A neural computing approach to the construction of information credibility assessments for online social networks

Predicting image credibility in fake news over social media using multi-modal approach

Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks.

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19’s communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This article presents a large-scale study based on data mined from Twitter . Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users’ profiles, meta-data and tweets’ context. The study noted various interesting conclusions including the critical impact in term of reach level of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.

System dynamic analysis of construction waste recycling industry chain in China

With the acceleration of China's urbanization process, construction activities have led to a substantial increase in construction waste. However, as China's construction waste recycling rate is low, it is difficult to convert construction waste into valuable products for the sustainable development of waste resources. The biggest reason for this is the construction waste industrial chain, which is imperfect and has many problems. In this study, a simulation model is established using system dynamics software Vensim PLE. The analogue simulation shows that (1) China generates a large amount of construction waste every year. If construction waste source reduction measures are implemented, it will inhibit 50% of the waste generated. Upstream enterprises in the industry chain should actively develop waste reduction technology and improve waste reduction management; (2) marginal effect exists in the single waste reduction measure, but the government should implement more portfolio reduction measures to enhance the reduction effect, which is often three times that of the single policy; (3) the middle stream recycling enterprises in the industry chain cannot solely rely on government subsidies to reduce the cost of waste recycling. It is necessary to create synergistic linkages with more responsible entities upstream and downstream of the industry chain, thus creating a new model in construction waste management. (4) Government policies tend to drive the purchasing power of building material market. The government should actively exert its subjective initiative, supervise and provide policies to help the downstream enterprises that supply recycled building materials in the industry chain, and change the rigid understanding of contractors and owners about recycled building materials. The research conclusions can provide theoretical guidance for industry chain-related enterprises and government management departments in policy formulation, so that construction waste is sustainably developed.