SIG SM Digest January 2023
Publications (Conference/Journal Papers)
We are excited to share the latest publications from the SIG SM community for the SIG SM community.
Thank you Amara Malik, Lu An, Farwa Taqi, Abdul Jabbar, Catherine Dumas, Amir Karami, and Margaret Zimmerman for sharing your research.
Journal Title: Health information seeking and sharing behavior of young adults on social media in Pakistan
Social media such as Facebook, YouTube, WhatsApp, and Twitter have radically enhanced the public access to health information. Still, studies have unexplored the factors that contribute toward such behaviors especially in developing countries. Therefore, this study identifies the factors that contribute to the likelihood of young adults’ health information seeking and sharing on social media. Specifically, drawing upon health belief model (HBM), the study attempts to understand how health belief and e-Health literacy affects health information seeking and sharing on social media. The study collected data from 413 young adults through Google Forms on a random basis. The results generated applying structural equation modeling confirmed that HBM related factors such as perceived susceptibility, perceived severity and perceived benefits positively while perceived barriers negatively influence young adults’ health information seeking and sharing intentions on social media. Furthermore, e-Health literacy was positively associated with health information seeking and sharing intentions on social media. This study is amongst a first few studies in the context of developing world to investigate the young adults’ intentions of seeking and sharing health information on social media based on HBM.
Malik, A., Islam, T. Ahmad, M. & Mahmood, K. (2022). Health information seeking and sharing behavior of
young adults on social media in Pakistan. Journal of Librarianship & Information Science: https://doi.org/10.1108/ITP-12-2021-0991
Title: Severity assessment and the early warning mechanism of public events based on the comparison of microblogging characteristics
The purpose of the study is to evaluate the severity of public events in real time from the perspective of social media and to construct the early warning mechanism of public events. This study constructed the severity assessment system of public events from the dimensions of the netizens' role, the Internet media's role, the spread of public events and the attitudes and feelings of netizens. The method of analyzing the influence tendency of the public event severity indicators was proposed. A total of 1,107,308 microblogging entries regarding four public events were investigated. The severity of public events was divided into four levels. It is found that serious public events have higher indicator values than medium level events on the microblogging platform. A quantitative severity classification standard for public events was established and the early warning mechanism of public events was built. Microblogging and other social media platforms provide rich clues for the real-time study and judgment of public events. This study only investigated the Weibo platform as the data source. Other social media platforms can also be considered in future. Different from the ex-post evaluation method of judging the severity of public events based on their physical loss, this study constructed a quantitative method to dynamically determine the severity of public events according to the clues reflected by social media. The results can help the emergency management departments judge the severity of public events objectively and reduce the subjective negligence and misjudgment.
Chen, M.M., An, L., Li, G., Yu, C.M.(2022). Severity assessment and the early warning mechanism of public
events based on the comparison of microblogging characteristics. Information Technology & People. https://doi.org/10.1108/ITP-12-2021-0991
Title: Are Public Libraries Promoting Social Justice? A Case of Quaid-e-Azam Library of Pakistan.
The Libraries as a social institution must reflect diversity and equity. These serviceoriented institutions should design and offer inclusive services to all members of the society (Berthoud & Finn, 2019). To incorporate social justice work into public libraries’ practices, equitable access should be provided to all marginalized user groups. To do this, public libraries’ mission statements, infrastructure, policies and practices must be redesigned (Harrington et. al., 2020). Public libraries can use a social justice lens through collections, displays, instructions, reading groups etc. (Scherlen, 2020). There is a dearth of national and international literature promoting equitable educational opportunities using public libraries’ platform. National literature highlights some issues of Visually impaired students and teachers (Zia & Fatima, 2016; Ali, Bashir, Fatima & Babar, 2016; Awais & Ameen, 2015; Khan, Idress, Ashgar & Aziz, 2018; Ahmed & Naveed, 2021) in addition to physically impaired students and library services (Iqbal & Shahzad, 2021). Therefore, there is a significant need to highlight case studies, stories, personal experiences, current status and challenges of marginalized library user groups. For the present study, marginalized users group include people who are not in mainstream such as minorities i.e. physically disable. Such empirical studies may promote the noble cause of equity and may create awareness among library communities. However, less studies connected social justice with the role of public libraries
Batool, Syeda Hina, Taqi, Farwa, & Aslam, Tabassum. (2022, June 16). Are Public Libraries Promoting Social
Justice? A Case of Quaid-e-Azam Library of Pakistan. https://doi.org/10.5281/zenodo.6651705
Title: Dark Clouds with Silver Linings: Use of Mobile Technology during COVID-19 among University Students
This study aims to explore the academic and leisure use of mobile technologies among students during the COVID-19 pandemic. It also identified the challenges they faced while using mobile technologies. It was a quantitative study based on a questionnaire survey. An online questionnaire was used to collect the data from university students to understand their mobile technologies’ use during COVID-19 epidemic. The data depicted that university students used mobile technologies both for leisure and academic purposes to get their everyday information. They used it to share their academic documents, to keep in touch with teachers and fellows, and to save documents. It was noted that they use mobile technologies to engage in academic activities as well as to keep in touch with their loved ones during the period of confinement. The majority of them were users of smartphones. However, they were facing challenges while using mobile technologies such as the high cost of the devices, limited storage facilities, compatibility issues, data insecurity, and low battery life of the devices. The study would help understand the use of mobile technologies in relieving students from stress through their use for leisure purposes. Most importantly, it would articulate the effectiveness of online learning in comparison to traditional learning specifically in the time of confinement.
Irfan, M., Jabbar, A., & Fatima Warraich, N. (2022). Dark Clouds with Silver Linings: Use of Mobile Technology during COVID-19 among University Students. Journal of Development and Social Sciences, 3(2), 591–605. https://doi.org/10.47205/jdss.2022(3-II)54
Title: Transforming e-participation: VR-dialogue – building and evaluating an AI-supported framework for next-gen VR-enabled e-participation research
The purpose of this study is to explore whether immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles that hinder effective communication and collaboration. Immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles hindering effective communication and collaboration. VR technologies boost discussion participants' sense of presence and immersion; however, studying emerging VR technologies for their applicability to e-participation is challenging because of the lack of affordable and accessible infrastructures. In this paper, the authors present a novel framework for analyzing serious social VR engagements in the context of e-participation.
The authors propose a novel approach for artificial intelligence (AI)-supported, data-driven analysis of group engagements in immersive VR environments as an enabler for next-gen e-participation research. The authors propose a machine-learning-based VR interactions log analytics infrastructure to identify behavioral patterns. This paper includes features engineering to classify VR collaboration scenarios in four simulated e-participation engagements and a quantitative evaluation of the proposed approach performance.
The authors link theoretical dimensions of e-participation online interactions with specific user-behavioral patterns in VR engagements. The AI-powered immersive VR analytics infrastructure demonstrated good performance in automatically classifying behavioral scenarios in simulated e-participation engagements and the authors showed novel insights into the importance of specific features to perform this classification. The authors argue that our framework can be extended with more features and can cover additional patterns to enable future e-participation immersive VR research.
This research emphasizes technical means of supporting future e-participation research with a focus on immersive VR technologies as an enabler. This is the very first use-case for using this AI and data-driven infrastructure for real-time analytics in e-participation, and the authors plan to conduct more comprehensive studies using the same infrastructure.
The authors’ platform is ready to be used by researchers around the world. The authors have already received interest from researchers in the USA (Harvard University) and Israel and run collaborative online sessions.
The authors enable easy cloud access and simultaneous research session hosting 24/7 anywhere in the world at a very limited cost to e-participation researchers.
To the best of the authors’ knowledge, this is the very first attempt at building a dedicated AI-driven VR analytics infrastructure to study online e-participation engagements.
Porwol, L., Garcia Pereira, A., & Dumas, C. (2022). Transforming e-participation: VR-dialogue–building and evaluating an AI-supported framework for next-gen VR-enabled e-participation research. Transforming Government: People, Process and Policy. https://doi.org/10.1108/TG-12-2021-0205
Title: Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design
Natural language processing techniques have increased the volume and variety of text data that can be analyzed. The aim of this study was to identify the positive and negative topical sentiments among diet, diabetes, exercise, and obesity tweets. Using a sequential explanatory mixed-method design for our analytical framework, we analyzed a data corpus of 1.7 million diet, diabetes, exercise, and obesity (DDEO)-related tweets collected over 12 months. Sentiment analysis and topic modeling were used to analyze the data. The results show that overall, 29% of the tweets were positive, and 17% were negative. Using sentiment analysis and latent Dirichlet allocation (LDA) topic modeling, we analyzed 800 positive and negative DDEO topics. From the 800 LDA topics—after the qualitative and computational removal of incoherent topics—473 topics were characterized as coherent. Obesity was the only query health topic with a higher percentage of negative tweets. The use of social media by public health practitioners should focus not only on the dissemination of health information based on the topics discovered but also consider what they can do for the health consumer as a result of the interaction in digital spaces such as social media. Future studies will benefit from using multiclass sentiment analysis methods associated with other novel topic modeling approaches.
Shaw Jr, G., Zimmerman, M., Vasquez-Huot, L., & Karami, A. (2022, November). Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design. In Healthcare, 10(11), 2320 . https://doi.org/10.3390/healthcare10112320