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    Discovering the secret behind managing WEEE: deep learning method in industry 4.0
    (Springer, 2023) Shahidzadeh, Mohammad Hossein; Shokouhyar, Sajjad; Safari, Aida; Tirkolaee, Erfan Babaee; Shokoohyar, Sina
    A large volume of waste electrical and electronic equipment (WEEE) is generated worldwide every year. This consists of hazardous and precious metals and represents a significant portion of this stream. Governments must make the right decisions regarding the influential factors affecting consumers' participation in electronic waste recycling programs in Industry 4.0 era to minimize the devastating impacts of these devices on the environment and human health and to recover precious metals and resources. Using the decentralized consensus decision-making concept, the proposed framework in this study uses social media users' opinions to improve decision-making concerning the influential factors affecting consumers' participation through artificial intelligence (AI). Considering Industry 4.0 concept, 20,348,014 million posts are extracted from Twitter, Facebook (Meta), and Reddit platforms and are analyzed using AI techniques. Then, more than 100 papers are analyzed to list influential factors comprehensively. Finally, the aggregated factors are presented to the Delphi method for further analysis. The findings demonstrate that economic incentives are considered significant factors in developed and developing countries. Since the living conditions of developed and developing nations are different, their concerns are also different. Hence, socio-economic and socio-political issues are the main concerns of people in developing countries. However, proximity, ease of access, and other factors play a significant role in developed countries. This study is among a few studies developing a real-time decision-making system to improve decision-making using social media data and AI techniques.