Over the past 30 years, the Kingdom of Saudi Arabia (KSA) has recorded rapid economic growth and development rates matched by corresponding levels of consumption. This has led to an exponential increase in the volume of municipal solid waste. Even if the bulk share of this waste is recyclable, the propensity of households to engage in the recycling of waste has so far produced a negligible effect. Arguably, in the context of the pervasive digitalization of public and private spaces, accompanied by the developments in information and communication technology (ICT), best conceptualized by reference to the concepts of smart cities and circular economy, it is natural to expect ICT and policy objectives to converge in order to encourage and enable greater engagement with waste segregation and recycling amongst households. Against this backdrop, this paper explores the feasibility and ways of increasing households’ participation in recyclable waste collection, segregation, and disposal in a context devoid of a top-down city-level waste management policy. To this end, the design thinking approach was employed to identify factors inhibiting/facilitating households’ engaging in recycling. A variety of ideation techniques were adopted in subsequent stages of the design thinking process, including interviews with stakeholders from the government and industry sectors, surveys with Jeddah households (N = 446), an eight week-long recyclable waste collection experiment, and feedback surveys (N = 46). The results suggest that applying relatively simple and resource-savvy tools aimed at promoting household recyclable waste collection has substantial potential. This, in turn, not only feeds into the debate on smart cities but also into the research of public policy.

Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.

Deaf-mute people have much potential to contribute to society. However, communication between deaf-mutes and non-deaf-mutes is a problem that isolates deaf-mutes from society and prevents them from interacting with others. In this study, an information technology intervention, intelligent gloves (IG), a prototype of a two-way communication glove, was developed to facilitate communication between deaf-mutes and non-deaf-mutes. IG consists of a pair of gloves, flex sensors, an Arduino nano, a screen with a built-in microphone, a speaker, and an SD card module. To facilitate communication from the deaf-mutes to the non-deaf-mutes, the flex sensors sense the hand gestures and connected wires, and then transmit the hand movement signals to the Arduino nano where they are translated into words and sentences. The output is displayed on a small screen attached to the gloves, and it is also issued as voice from the speakers attached to the gloves. For communication from the non-deaf-mutes to the deaf-mute, the built-in microphone in the screen senses the voice, which is then transmitted to the Arduino nano to translate it to sentences and sign language, which are displayed on the screen using a 3D avatar. A unit testing of IG has shown that it performed as expected without errors. In addition, IG was tested on ten participants, and it has been shown to be both usable and accepted by the target users.

Reliance on social networks has become an integral part of modern daily activities. Social networks are crowded with vast numbers of comments, opinions, and beliefs about different aspects of people’s daily lives. Opinions can directly express positive or negative perspectives toward a specific entity or its aspects, or they can be comparative opinions that present the differences or similarities between two or more entities, such as “product A is better than product B.’’ Comparative opinions have significant potential to offer new opportunities across various fields such as marketing, education, and e-commerce. Thus, analyzing comparative opinions is essential. Indeed, comparative opinion mining has been applied in several languages, including Arabic. Nevertheless, comparative opinion mining in Arabic is still in its early stages and requires more contributions and investigations. Therefore, this work introduces the ACOM approach for Arabic Comparative Opinion Mining. First, we collected the ACOM corpus in Arabic from the X platform, focusing on comparative opinions in the technology domain. This paper provides a benchmark comparison of several deep-learning models by employing different approaches, including Long Short-Term Memory (LSTM), Bidirectional LSTMs (BiLSTM), and Convolutional Neural Networks (CNN). In the deep learning model, the generated representation vector was used to construct the embedding layer as the first layer. To this end, we adopted two pretrained word embedding techniques to investigate their effectiveness on the ACOM corpus: Word2vec and Bidirectional Encoder Representations from Transformers (BERT). The main highlight of this paper is leveraging the most advanced innovation in AI, the GPT-3 model, as a classifier model. Our experimental results show that BERT and BiLSTM achieved impressive performance on the ACOM task with 91% accuracy and a 90% F1-score. Additionally, this paper discusses the impact of fine-tuning specific hyperparameters by providing a comprehensive examination.

In the context of the debate on the need to place citizens at the center of the technological revolution, this paper makes a case for a natural language processing (NLP) crowdsourcing platform that ensures inclusion and diversity, thus making the research outcome relevant and applicable across issues and domains. This paper also makes the case that by enabling participation for a wide variety of stakeholders, this NLP crowdsourcing platform might ultimately prove useful in the decision- and policy-making processes at city, community, and country levels. Against the backdrop of the debates on artificial intelligence (AI) and NLP research, and considering substantial differentiation specific to the Arab language, this paper introduces and evaluates an Arab language-sensitive NLP crowdsourcing platform. The value of the platform and its accuracy are measured via the System Usability Scale (SUS), where it scores 72.5, i.e., above the accepted usability average. These findings are crucial for NLP research and the research community in general. They are equally promising in view of the practical application of the research findings.