Author: SocialXYZ

My colleagures and I recently presented and published research at EDULEARN 2022 titled, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”. The paper presents on differences in online interaction before and during COVID-19 lockdowns with an emphasis on sentiment analysis. Below is the tag cloud, abstract and full citation of the research.

Abstract
In early 2020, there was a drastic shift in the teaching modality across institutions of higher education as colleges and universities adapted to federal guidelines in the face of COVID-19 restrictions. While most, if not all institutions already had mature learning management systems (LMS), alternative approaches using social components proved invaluable during a time when face-to-face social interaction was largely restricted. More so, a large concern in the shift to remote instruction during COVID-19 lockdowns was the toll on student well-being as traditional learning management systems typically lack social networking capabilities. In this paper, we explore the use of online social networking (OSN) software to facilitate more positive interactions. More specifically, we analyse the sentiment within online discussions to show key differences between users using traditional LMS software and those using modified OSN software. Findings show that students participating during the pandemic, yielded higher levels of social presence and posted more positively to the discussion.

Reference
Thoms, E. Eryilmaz, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”, 14th Annual International Conference on Education and New Learning Technologies (EDULEARN 22), July 5, 2022.

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The following abstract is from a student co-authored paper, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” presented by Dr. Jason Isaacs at the 8th International Conference on Data Mining and Applications (DMA 2022), presented on May 28, 2022.

Abstract
Classifier algorithms are a subfield of data mining and play an integral role in finding patterns and relationships within large datasets. In recent years, fake news detection has become a popular area of data mining for several important reasons, including its negative impact on decision-making and its virality within social networks. In the past, traditional fake news detection has relied primarily on information context, while modern approaches rely on auxiliary information to classify content. Modelling with machine learning and natural language processing can aid in distinguishing between fake and real news. In this research, we mine data from Reddit, the popular online discussion forum and social news aggregator, and measure machine learning classifiers in order to evaluate each algorithm’s accuracy in detecting fake news using only a minimal subset of data.

Reference
M. Shariff, B. Thoms, J. Isaacs, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” Accepted for inclusion in 8th International Conference on Data Mining and Applications (DMA 2022), May 28-29, 2022.

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My colleagues and I recently published in the prestigious Journal of Management Information Systems (JMIS) titled, “Promoting learning community formation using recommender systems and learning analytics within a computer-supported collaborative learning software.” The paper provides an interesting deep dive into how collaborative recommendation systems facilitate knowledge communities. Below is the tag cloud, abstract, and full citation to the research.

Abstract
This paper explores the formation of a learning community facilitated by a specialized computer-supported collaborative learning asynchronous online discussion (AOD) tool. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed methods field study involving an AOD consisting of 259 messages posted by 50 participants. We applied degree and closeness centrality to cluster participants into three clusters corresponding to the central, intermediate, and peripheral community layers. The findings show that the intermediate cluster had the opportunity to influence others’ thinking within discourse, but it was the central cluster, which re-constructed the raw ideas affected by others’ feedback. The results further provide evidence that message lexical complexity does not correlate to the stages of knowledge building. Finally, results offer an initial insight into which ideas reached to higher stages of knowledge building among the clusters. Theoretical and practical implications are discussed.

Reference
E. Eryilmaz, B. Thoms, Z. Ahmed, KH Lee. “Promoting learning community formation using recommender systems and learning analytics within a computer-supported collaborative learning,” Journal of Management Information Systems, April 2022. (Impact Factor 7.84)

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My colleagues and I recently published an interesting paper for HICSS 2022 titled, “Determining Link Relevancy in Tweets Related to Multiple Myeloma Using Natural Language Processing.” The paper focused on analyzing Twitter data for links to multiple myeloma. Below is the tag cloud, abstract, and full citation to the research.

Abstract

Social m`edia platforms continue to play a leading role in the evolution of how people share and consume information. Information is no longer limited to updates from a user’s immediate social network but have expanded to an abstract network of feeds from across the global internet. Within the health domain, users rely on social media as a means for researching symptoms of illnesses and the myriad of therapies posted by others with similar implications. Whereas in the past, a single user may have received information from a limited number of local sources, now a user can subscribe to information feeds from around the globe and receive real-time updates on information important to their health. Yet how do users know that the information they are receiving is relevant or not? In this age of fake news and widespread disinformation the global domain of medical knowledge can be tough to navigate. Both legitimate and illegitimate practitioners leverage social media to spread information outside of their immediate network in order to reach, sway, and enlist a larger audience. In this research, we develop a system for determining the relevancy of linked webpages using a combination of web mining through Twitter hashtags and natural language processing (NLP).

Reference
S.V. Hoven, B. Thoms, N. Botts, “Determining Link Relevancy in Tweets Related to Multiple Myeloma Using Natural Language Processing,” Proceedings of Hawaiian International Conference on System Sciences (HICSS 55), January 3-7, 2022.

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My colleagues and I recently published and presented our article, “Developing a Mobile Social Learning Application for Interdisciplinary Technology Courses,” at the 2021 IADIS Conference on Information Systems. I published at IADIS back in 2007 and won Best Research Paper. My how time has flown. Below is the word cloud of the paper, abstract and citation.

Abstract
Smartphone adoption has grown steadily and now represents roughly 50% of all Internet traffic. Advances in mobile application development along with its widespread adoption offers unique opportunities within computing education to quickly offer knowledge and information directly into the hands of the learner. Adopting a mobile-first approach, this paper introduces the construction of a mobile social learning platform, built on top of existing social learning software and measures its adoption in interdisciplinary computing courses. The goal of the mobile app is to complement the browser-based system and provide access to course notifications, gradebook and discussion posts. Initial results found 50% of students adopting the smartphone app regularly, while most students preferred to use the browser-based system. Students adopting the mobile application indicated that they liked the ability to view course notifications, assignments, grades and instructor feedback from within the mobile app.

Citation
B. Thoms, E. Eryilmaz, “Developing a Mobile Social Learning Application for Interdisciplinary Technology Courses,” Proceedings of IADIS Conference on Information Systems, March 3-5, 2021.

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