“What’s happening?” Assessing the Sustainability of Virtual Professional Learning Communities on Social Media: A Quantitative Study of ‘Sense of Community’

While research has highlighted the multifaceted benefits of Twitter as an informal professional learning resource, there remains a lack of literature that adequately teases apart the dynamic underpinnings of these types of informal professional learning communities (Thacker, 2017; Visser et al., 2014). Greenhow & Gleason (2012) posited that there is a need to better understand Twitter’s place within the education profession, as well as “how participants understand their experiences and place within the Twitter community and beyond” (p. 473). Grounded in ‘sense of community’ theory, this study examined ‘sense of community’ as a construct supporting the #SSChat community’s sustainability. Additionally, I endeavored to determine whether a statistically significant correlation existed between perceived SOC and sustainability of #SSChat community participants, and whether statistically significant correlations existed between each of the four independent SOC tenets and sustainability. Findings from this study produced implications to inform future strategic planning efforts to strengthen the #SSChat community on Twitter. Moreover, they support the #SSChat as a viable form of social studies education professional development and have implications for similar social media-based informal professional learning communities, as well as the field of social studies education in general

Graph-based Cluster Analysis to Identify Similar Questions: A Design Science Approach

Social question answering (SQA) services allow users to clarify their queries by asking questions and obtaining answers from other users. To enhance the responsiveness of such services, one can identify similar questions and, thereafter, return the answers available. However, identifying similar questions is difficult because of the complex language structure of user-generated questions. For this reason, we developed an approach to cluster similar questions based on a web of social relationships among the questions, the answers, the askers, and the answerers. To do so, we designed a graph-based cluster analysis using design science research guidelines. In evaluating the results, we found that the proposed graph-based cluster analysis is more promising than baseline methods

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development