Purpose: The explosion of social media use such as Facebook among higher education students is deemed to have great potential in widely disseminating environmental sustainability awareness. The paper aims to capture, summarise, synthesise and comment on the role of social media to garner interest of students and staff on environmental sustainability issues.Design/methodology/approach: Systematic literature review technique is adopted, and three selected online databases were searched for relevant papers for review. Specific data were extracted from each paper, and the discussion section was based on the developed research questions.Findings: Higher education needs to fully leverage the ubiquity of social media to extend how environmental sustainability is viewed by the students and staff. Sustainability practices conducted at the university level such as recycling, reduction of electricity and water consumptions and paper reduction in classroom and used to engage students on environmental matters. For staff, social media can be leveraged as to convey the university policy and assist in their quest to become full-fledged green universities.Research limitations/implications: Some of the limitations of this research include the lack of keyword search using synonyms or related terms equivalent to the term “awareness”, lack of forward and backward searches, and the papers were searched until end of 2013 only. Future research needs to take advantage of the current limitations to investigate this topic and be empirically supported by theories using quantitative, qualitative or mixed-method-based research. Future studies could also develop relevant frameworks to propose an effective use of social media for creating environmental sustainability awareness in higher education.Practical implications: Propagations of environmental sustainability awareness in higher education would be more effective with the use of social media.Social implications: In principle, the increase of environmental awareness level would increase the students’ good behavioural conduct on environmental sustainability.Originality/value: While the broad topic of environmental sustainability in higher education is widely discussed, the role of social media in shaping environmental sustainability awareness is still under researched. This situation provides a significant potential for exploration by sustainability researchers to craft their investigation on the effective role of social media in creating environmental awareness in higher education.

Advancements in information and communication technology, and online web users have given attention to the virtual representation of each user, which is crucial for effective service personalization. Meeting users need and preferences is an ongoing challenge in service personalization. This issue can be addressed through the building of a comprehensive user profile. A user profile is the summary of the user’s interests, characteristics, behaviours, and preferences, while user profiling is the system of collecting, organizing and inferring the user profile information. Many reviews on user profiling have been conducted but none focused on the effective profile modeling process. Hence, this article aims to provide a review of the recent state-of-the-art approach to user profiling. These include methods, description, characteristics, and taxonomy of the user profile. The study of the existing user profiling modeling in the aspect of data acquisition, feature extraction, profiling techniques, and profiling approaches (with the identification of their strengths and weaknesses) and the performance measures are also provided. In addition, the research challenges were also discussed with a focus on privacy, datasets, cold start issues, trust issues, and computational complexity. Moreover, the article identified an open research direction that serves as solutions to the identified challenges and motivation for further researchers in advancing user profiling. The findings showed that an effective modeling process enhances the construction of accurate user profile for service personalization.

Sarcasm is a complicated linguistic term commonly found in e-commerce and social media sites. Failure to identify sarcastic utterances in Natural Language Processing applications such as sentiment analysis and opinion mining will confuse classification algorithms and generate false results. Several studies on sarcasm detection have utilised different learning algorithms. However, most of these learning models have always focused on the contents of expression only, leaving the contextual information in isolation. As a result, they failed to capture the semantics and contextual information in the sarcastic expression. Secondly, many deep learning methods in NLP uses a word embedding learning algorithm as a standard approach for feature vector representation, which ignores the sentiment polarity of the words in the sarcastic expression. This study proposes a context-based feature technique for sarcasm Identification using the deep learning model, BERT model, and conventional machine learning to address the issues mentioned above. Two Twitter and Internet Argument Corpus, version two (IAC-v2) benchmark datasets were utilised for the classification using the three learning models. The first model uses embedding-based representation via deep learning model with bidirectional long short term memory (Bi-LSTM), a variant of Recurrent Neural Network (RNN), by applying Global Vector representation (GloVe) for the construction of word embedding and context learning. The second model is based on Transformer using a pre-trained Bidirectional Encoder representation and Transformer (BERT). In contrast, the third model is based on feature fusion that comprised BERT feature, sentiment related, syntactic, and GloVe embedding feature with conventional machine learning. The effectiveness of this technique is tested with various evaluation experiments. However, the technique’s evaluation on two Twitter benchmark datasets attained 98.5% and 98.0% highest precision, respectively. The IAC-v2 dataset, on the other hand, achieved the highest precision of 81.2%, which shows the significance of the proposed technique over the baseline approaches for sarcasm analysis.

Trust is a concept that lies amidst the rhetoric of empowering users by using blockchain. Bitcoin’s eminence hyped as “trust-free,” poignantly made trust an indispensable central element associated with the complexities and uncertainties of online transactions abound in the use and development of blockchain – leaving many users unacquainted with its use. Studies that focus on assessing the trustworthiness of trustees using various characteristics are scarce. By mapping the characteristics that constitute the basis of the formation of trust from the viewpoint of the trustor, this study explored the characteristics of blockchain. Semi-structured interviews were conducted with 14 individuals (aged 20-40 years, from technical and/or business prospects), using the blockchain. Three overlapping procedures; data reduction, data display, and conclusion drawing and verification, were used to identify categories and themes. The findings show that the characteristics of blockchain that influence trust are Reliability, Integrity, Tamper-proofing and Immutability, Versatility, Transparency, and Privacy. The Blockchain is a functionally versatile, tamper-proof, immutable, asset database on a decentralized network. Its reliable technical features, which ensure data integrity, and provide a transparent privacy protected environment, have meant that users are willing to trust it to generate value.

This study examines the efficacy of robots as assistive technology (AT) learning tools for children with autism spectrum disorder (ASD). The study attempts to find answers to whether robots as assistive tools can (i) profoundly improve achievement in learning and (ii) provide valuable learning experiences among this group of children. Using LEGO Mindstorms EV3, a robot was built and programmed to teach the basic concept of place value in mathematics. Eight children with ASD, specifically, four females and four males, participated in the single case study, and six special education teachers took part in the interviews. The children participated in both traditional and robotic intervention lessons and were assessed at the end of each session. The results indicate a positive increase in content knowledge and an improved disposition toward learning, thus demonstrating the potential utilization of robots as AT tools for harnessing classroom learning. Data from the interviews with teachers highlighted four valuable learning experiences that occurred in the classrooms as a result of the robotic interventions; namely, with respect to young children with ASD, the AT (1) promoted interest and engagement, (2) increased attention and focus, (3) triggered interactions and communication, and (4) created a happy and fun learning environment.