Social media analytics and their applications to evaluate an activity in online health interventions using CRITIC and TOPSIS techniques.

The usage of social media is increasing by leaps and bounds in our day-to-day lives. It affects daily routines and brings a lot of change in different departments like health and education systems during the COVID-19 pandemic. Healthcare research and practice have been significantly impacted by the astounding growth of social media. Social media are changing health information management in several ways, from offering appropriate ways to enhance healthcare professional contact and share health-related knowledge and experience to facilitating the development of innovative medical research and wisdom. Social media analytics (SMAs) are efficient and effective interaction instruments that can be useful for both patients and clinicians in health interventions. Moreover, a significant portion of those involved in clinical practices (such as clinicians, professional colleges, and departments of health) are unaware of the importance of social media, its potential applications in their daily lives, as well as the possible consequences and how these will be handled. In the presented study, we proposed MCDM-based approaches known as "Criteria Importance Through Inter Correlation" (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in order to identify the effective alternative among several options and make a better decision. After extracting features from the literature review, we choose six significant and relevant features and assign weights to them using CRITIC techniques while utilizing the TOPSIS technique to rank the alternatives based on their performance values. After the implementation of both methods and evaluation procedure, it is determined that the alternative with the highest score is placed at the top and called the "best alternative," while the alternative with the lowest score is placed at the bottom and called the worst alternative. Finally, we suggest a variety of research initiatives and new research areas where the aforementioned procedures become extremely useful in evaluating SMAs and their uses in online health interventions.

Using Social Media to Detect Fake News Information Related to Product Marketing: The FakeAds Corpus

Nowadays, an increasing portion of our lives is spent interacting online through social media platforms, thanks to the widespread adoption of the latest technology and the proliferation of smartphones. Obtaining news from social media platforms is fast, easy, and less expensive compared with other traditional media platforms, e.g., television and newspapers. Therefore, social media is now being exploited to disseminate fake news and false information. This research aims to build the FakeAds corpus, which consists of tweets for product advertisements. The aim of the FakeAds corpus is to study the impact of fake news and false information in advertising and marketing materials for specific products and which types of products (i.e., cosmetics, health, fashion, or electronics) are targeted most on Twitter to draw the attention of consumers. The corpus is unique and novel, in terms of the very specific topic (i.e., the role of Twitter in disseminating fake news related to production promotion and advertisement) and also in terms of its fine-grained annotations. The annotation guidelines were designed with guidance by a domain expert, and the annotation is performed by two domain experts, resulting in a high-quality annotation, with agreement rate F-scores as high as 0.815.

The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Platforms?

The purpose of this study is to investigate students’ intention to continue using online learning platforms during face-to-face traditional classes in a way that is parallel to their usage during online virtual classes (during the pandemic). This investigation of students’ intention is based on a conceptual model that uses newly used external factors in addition to the technology acceptance model (TAM) contrasts;hence, it takes into consideration users’ satisfaction, the external factor of information richness (IR) and the quality of the educational system and information disseminated. The participants were 768 university students who have experienced the teaching environments of both traditional face-to-face classes and online classes during the pandemic. A structural equation modelling (SEM) test was conducted to analyse the independent variables, including the users’ situation awareness (SA), perceived ease of use, perceived usefulness, satisfaction, IR, education system quality and information quality. An online questionnaire was used to explore students’ perceptions of their intention to use online platforms accessibly in a face-to-face learning environment. The results showed that (a) students prefer online platforms that have a higher level of content richness, to be able to implement the three dimensions of users’ situation awareness (perception, comprehension and projection);(b) there were significant effects of TAM constructs on students’ satisfaction and acceptance;(c) students are in favour of using a learning platform that is characterised by a high level of educational system quality and information quality and (d) students with a higher level of satisfaction have a more positive attitude in their willingness to use the online learning system.