The Impact of Covid-19 on Online Discussions: the Case Study of the Sanctioned Suicide Forum

The COVID-19 pandemic has been at the center of the lives of many of us for at least a couple of years, during which periods of isolation and lockdowns were common. How all that affected our mental well-being, especially the ones' who were already in distress? To investigate the matter we analyse the online discussions on Sanctioned Suicide, a forum where users discuss suicide-related topics freely. We collected discussions starting from March 2018 (before pandemic) up to July 2022, for a total of 53K threads with 700K comments and 16K users. We investigate the impact of COVID-19 on the discussions in the forum. The data show that covid, while being present in the discussions, especially during the first lockdown, has not been the main reason why new users registered to the forum. However, covid appears to be indirectly connected to other causes of distress for the users, i.e. anxiety for the economy. © 2023 ACM.

The infinity vaccine war: linguistic regularities and audience engagement of vaccine debate on Twitter

Purpose: As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro- and anti-vaxxers' tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences' engagement. Design/methodology/approach: Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement. Findings: In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro- and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro- and anti-vaxxers separately. Among the pro-vaxxers' tweets, the topics of "Child protection” and "COVID-19 situation” are positively predicting audiences' engagement. For anti-vaxxers, the topics of "Supporting Trump,” "Injured children,” "COVID-19 situation,” "Media propaganda” and "Community building” are more appealing to audiences. Originality/value: This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery. Peer review: The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-03-2022-0186 © 2023, Emerald Publishing Limited.

I am what I am-Convergence Behaviors on Online Discussion about the Safety of COVID-19 Vaccines

Using data from an online discussion on the risk of getting blood clot from Johnson & Johnson vaccine moderated by the New York Times Facebook page, we investigated the presence of eleven convergence behaviors, and the interaction between them. While recent research focuses on misinformation or fake news as the object of analysis, we argue in this exploratory research that it is equally important to analyze who and, whenever possible, why people engage in information exchange given a particular crisis, hence their convergence behaviors. Mapping the types of postings to their authors would be an additional step to design, develop, implement, and possibly, regulate online discussions for a more effective and just civic engagement. As we witness a mass manipulation of public opinion, our findings suggest that the number of netizens that seek to correct misinformation is growing. If the society goal is to swiftly rebut as many conspiracy theories as possible, we advocate for a dual social media control strategy: restrain as much as possible the misinformation spreaders/manipulators and encourage correctors to help propagate countervailing facts. © 2022 IEEE Computer Society. All rights reserved.

Perception Analysis: Pro- and Anti- Vaccine Classification with NLP and Machine Learning

Online discussion of the ensuing pandemic exemplifies the extent and complexity of information required to understand human perception. Social media has proven to be a viable medium for identifying actionable data and analyzing public perception. As health sectors all over the world battled to obtain accurate information regarding COVID-19, this research focused on gauging public perceptions of the vaccine. The public reception of the vaccine can be determined by public perception. This study explores how to use machine learning to understand human perceptions in the context of the COVID-19 vaccine. Natural Language Processing (NLP) was employed to detect pro- and anti-vaccine tweets, while two machine learning classification models were used to study the patterns derived from the analysis. The study analyzed people's perceptions of the vaccine by presenting the results from a geographic region, while learning patterns that are likely to be associated with pro- or anti-vaccine perceptions. © 2022 IEEE Computer Society. All rights reserved.

Few-Shot (Dis)Agreement Identification in Online Discussions with Regularized and Augmented Meta-Learning

Online discussions are abundant with opinions towards a common topic, and identifying (dis)agreement between a pair of comments enables many opinion mining applications. Realizing the increasing needs to analyze opinions for emergent new topics that however tend to lack annotations, we present the first meta-learning approach for few-shot (dis)agreement identification that can be quickly applied to analyze opinions for new topics with few labeled instances. Furthermore, we enhance the meta-learner's domain generalization ability from two perspectives. The first is domain-invariant regularization, where we design a lexicon-based regularization loss to enable the meta-learner to learn domain-invariant cues. The second is domain-aware augmentation, where we propose domain-aware task augmentation for meta-training to learn domain-specific expressions. In addition to using an existing dataset, we also evaluate our approach on two very recent new topics, mask mandate and COVID vaccine, using our newly annotated datasets containing 1.5k and 1.4k SubReddits comment pairs respectively. Extensive experiments on three domains/topics demonstrate the effectiveness of our meta-learning approach. © 2022 Association for Computational Linguistics.

VRobotator: A Virtual Robot Facilitator of Small Group Discussions for K-12

The COVID-19 pandemic has stressed the importance of efficient and accommodating online educational experiences. In this contribution, we present a novel system for the facilitation of small group online discussions using an avatar during video conferencing. The avatar was programmed with group facilitation best practices, whereas the content for the activities was prepared by the classes' teachers. Groups of tenth grade students interacted with the system, where we compared activities facilitated by the avatar with activities without facilitation. Our results show that students reported the activity with the avatar to be significantly more efficient, more understandable and inducing more participation compared to activities without avatar facilitation. Students also spoke significantly more with avatar facilitation. This system shows promise in future online educational activities as a facilitator of discussions with K-12 students. © 2022 ACM.

Implementing Collaborative Online International Learning (COIL) in Regular Engineering Coursework

This Innovative Practice Full Paper is part of a collaboration between a US and a Chinese university, in which two different online engineering courses, situated at the US university, were taught to mixed classes of students from both. Course content remained the same as standard offerings, and assessments had both individual and group components, specifically instructor-selected mixed-university groups. The first course was run in the spring 2021 semester, followed by a different course in the fall 2021 semester.International education offerings have been hampered by disruptions due to Covid-19, in addition to the usual barriers. Alternate models like collaborative online international learning (COIL) provide an avenue to continue international partnerships and expand their modalities. The current project is based on the COIL model but differs in two ways: the courses were not specially developed for COIL but adapted to it without changing course objectives, and the courses were taught solely by the instructors at the US university though administrative support was provided by both universities.Data for this project was collected using student surveys, personal interviews, instructor reflections, and course assessments. Student survey data is qualitatively analyzed using thematic analysis, while personal interviews and instructor reflections are summarized by the authors. Student learning outcomes in both sections were compared to those in the sections offered only to on-campus students and found to be similar or slightly better. Student perceptions of the classes were positive despite substantial difficulties.Instructors and students in these classes faced significant challenges like time zone differences and scheduling issues, technological barriers, and cultural differences leading to miscommunication. However, the collaborative aspects of the courses made them attractive for both groups of students, and opportunities for easy communication during synchronous class meetings made things run smoothly.In addition to discussing the challenges and attractions, the authors make a series of recommendations on conducting a regular online course in STEM disciplines with students from two different education systems. © 2022 IEEE.

The Way to Better Learning Online: Using Online Discussions in College Classes During COVID-19

In recent years, a large body of evidence demonstrates that active learning through engaging college students in a discussion leads to increased learning outcomes. However, more empirical studies are needed to understand its effectiveness in online classes. In Experiment 1, students majoring in veterinary medicine were randomly assigned to three conditions: Lecture and Review, Lecture and Discussion, and Self-study and Discussion. Post-test results showed that the two discussion groups achieved significantly higher scores than the review group. Moreover, among the two discussion groups, the self-study group performed better than the lecture group. To reconsider the reliability and generalizability of our study we conducted Experiment 2 in which, we compared the performance of the two discussion conditions again for students majoring in social science. Our findings indicate that not only online discussions but active learning activities such as self-studying before the discussion is also crucial for students' learning. © ISLS.

Exploring Autonomous Language Learning Behaviors through Video Sharing and Online Discussions in Higher Education1

This study explores the way in which video sharing and online discussions foster learning autonomy in English language classes at a public university during the lockdown established by the Colombian government because of the coronavirus pandemic. This study was conducted with 127 engineering and administration undergraduates from a public university in the department of Boyaca. It was a qualitative case study that aimed to identify the extent to which video sharing and online discussions increase learner's autonomous behaviors in English language learning. The instruments for data collection were student journals and artifacts such as video recordings and online discussions, interviews, and questionnaires. An open coding analysis was performed, and the results revealed that the degrees of autonomous behaviors could be gradually fostered by the implementation of video sharing. The results also indicated that students are receptive to online writing discussions, and showed how motivation plays a vital role in learning English and the achievement of the learning goals set by the students themselves.

In-depth analysis of college students' online discussion interaction based on behavior sequence

In the context of the COVID-19 pandemic, the paper focuses on discussing this important teaching segment online in blended learning. Taking the junior as the research objects, the behavioral characteristics and interaction depth of different types of students are studied through Cluster Analysis and Lag Sequence Analysis. The purposes are to dig out the problems existing in the online discussion process and to propose teachers' intervention suggestions so as to improve the effect of the discussion. The research found that discussion behavior is mainly construction, and there is no discussion mechanism with communication and interaction as knowledge development. The behavior transformation is mainly based on the priming-single type and priming-arguing type, and there are fewer students who achieve the ideal state of the argumentative type. Most students integrate into the discussion atmosphere slowly, so teachers should take corresponding measures to help students enter the discussion quickly at the beginning of the discussion. © 2022 ACM.

Implementing Online Discussion and Mind Mapping to Investigate a Disease Outbreak.

The recent increase in online learning modalities due to coronavirus disease 2019 (COVID-19) has created a significant gap in real-time discussions on complex issues. This lack of enrichment from student discussions levies the concern of a deficiency in strong learning outcomes. This learning activity focused on mind mapping to facilitate small group discussions on the 2010 cholera outbreak in Haiti. Students learned about the disease triangle and cause-and-effect relationships on a large spatial and temporal scale. In this case, the three points of the triangle represented the pathogen (Vibrio cholerae), the environment (Haiti), and the hosts (Haitians). Each student in each small group was required to read a unique article to present to their group on the day of the activity. Using mind mapping, each group illustrated relationships that may have exacerbated the cholera outbreak. Learning outcomes were assessed through the evaluation of questions relevant to that week's exercise. Students were assessed on their ability to recognize relationships between the pathogen, environment, and hosts, as well as the ability to apply what they learned to the present-day COVID-19 pandemic. The disease triangle activity is readily accessible and can be easily implemented for identifying cause-and-effect relationships in large-scale systems. Importantly, this learning activity retained real-time discussion-based problem-solving for improving students' critical thinking skills and approaches to complex issues.

A topic modeling method for analysis of short-text data in social media networks

Currently, many short texts are published online, especially on social media platforms. High impact events, for example, are highly commented on by users. Understanding the subjects and patterns hidden in online discussions is a very important task for contexts such as elections, natural disasters or major sporting events. However, many works of this nature use techniques that, despite showing satisfactory results, are not the most suitable when it comes to the short texts on social media and may suffer a loss in their results. Therefore, this paper presents a text mining method for messages published on social media, with a data pre-processing step and topic modeling for short texts. For this paper, we created a data set from real world tweets related to COVID-19 that is openly available1 for research purposes. © 2022, EasyChair. All rights reserved.

Organizational Strategies of Online Discussion to Promote College Students Deep Learning under the Background of COVID-19

In literature review it is found that the research shows that online discussion can promote the development of students' higher-order thinking, but the current online discussion effect is not ideal mainly because teachers lack careful design of online discussion. Through the design and implementation of online discussion organization, the results show that the discussion topics put forward by students according to the given scope of teachers can stimulate the enthusiasm of discussion participation;Compared with class group discussion, students in panel discussion show higher enthusiasm and deeper interaction;Without a host in the discussion, students have higher enthusiasm for discussion and are easier to achieve higher-level goals. It can be seen from the research that when teachers design online discussions, it is more conducive to achieving the goal of online discussions by letting students put forward discussion topics for spontaneous panel discussions. © 2021 ACM.

Effects of Tangible Rewards on Student Learning Performance, Knowledge Construction, and Perception in Fully Online Gamified Learning

This study investigated the effects of tangible rewards on student learning performance, knowledge construction of online discussions, and perception in fully online gamified learning during the COVID-19 pandemic. We conducted a quasi-experiment study involving two classes: (a) A control group (N = 26) utilized gamification points as main intangible rewards, and (b) A treatment group (N = 26) utilized gamification points as main intangible rewards and high-quality assignment samples from a previous cohort as tangible rewards. Results suggest that the presence of tangible rewards had no impact on learning performance. However, tangible rewards motivated students to create more posts and replied more often to a peer's post in online discussions. According to the survey responses, students liked the learning experience where both tangible rewards and game elements as intangible rewards were used all together in a gamified class. Students also preferred the provision of course content closely related to learning material as rewards and suggested offering multiple types of rewards at regular intervals. Most students in the tangible rewards group often checked their peers' posts for gaining new knowledge and reflecting on their own work by comparison, which also showed students' active participation in knowledge construction of online discussions. © 2021 IEEE.

VROBOTATOR: A VIRTUAL ROBOT FACILITATOR OF SMALL GROUP DISCUSSIONS FOR K-12

The COVID-19 pandemic has stressed the importance of efficient and accommodating online educational experiences. In this contribution, we present work-in-progress on the development of a novel system for facilitation of small group online discussions using an avatar during video conferencing. Four groups of eighth and ninth grades' students interacted with the system, with and without avatar guidance. Our pilot study results show that students reported the avatar-guided learning to be more efficient, easier to follow, and inducing more engagement and active participation. Students also showed better understanding of the learned subject with the avatar guidance, as shown by the post-session questionnaires. This system shows promise in future online educational activities as a facilitator of discussions with K-12 students. © 2021 Virtual Simulation Innovation Workshop, SIW 2021. All rights reserved.

#Scamdemic, #Plandemic, or #Scaredemic: What Parler Social Media Platform Tells Us about COVID-19 Vaccine.

This study aims to understand public discussions regarding COVID-19 vaccine on Parler, a newer social media platform that recently gained in popularity. Through analyzing a random sample (n = 400) of Parler posts using the hashtags #COVID19Vaccine and #NoCovidVaccine, we use the concept of echo chambers to understand users' discussions through a text analytics approach. Thematic analysis reveals five key themes: reasons to refuse the COVID-19 vaccine (40%), side effects of the COVID-19 vaccine (28%), population control through the COVID-19 vaccine (23%), children getting vaccinated without parental consent (5%), and comparison of other health issues with COVID-19 (2%). Textual analysis shows that the most frequently used words in the corpus were: nocovidvaccine (348); vaccine (264); covid (184); covid19 (157); and vaccines (128). These findings suggest that users adopted different terms and hashtags to express their beliefs regarding the COVID-19 vaccine. Further, findings revealed that users used certain hashtags such as "echo" to encourage like-minded people to reinforce their existing beliefs on COVID-19 vaccine efficacy and vaccine acceptance. These findings have implications for public health communication in attempts to correct false narratives on social media platforms. Through widely sharing the scientific findings of COVID-19 vaccine-related studies can help individuals understand the COVID-19 vaccines efficacy accurately.