Thread Reconstruction in Conversational Data using Neural Coherence Models

Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.Comment: Neu-IR: Workshop on Neural Information Retrieval 201

On terrorist attacks in Nigeria: Stance and engagement in conversations on Nairaland

Terrorist attacks in Nigeria have generated a huge body of conversations and debates on the Internet. This study investigates the contents of these online conversations on Nairaland and how such conversations exhibit stance and civic engagement in response to the attacks. Nairaland is an online community and public space that serves as a meeting place for Nigerians at home and in the Diaspora, who constantly follow-up on the events in Nigeria and participate in political debates about the country. This study argues that the frequent negative evaluations of Boko Haram and the attribution of the activities to Islam and the consistent constructions of northern Nigeria as ‘violent people’ and Islam as an ‘evil’ religion in Nairaland are potential to further worsen religious and ethnic relations in Nigeri

Computers and composition communities: Solidarity as a research paradigm

After a brief history of composition studies demonstrating a community realizing the need for more inclusive research practices, this thesis shows composition struggling with its identity as an academically legitimized discipline

Graph-Based Conversation Analysis in Social Media

Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users' intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities

The Effect of Online Discussion Forums on Student Learning and Student Perception of Learning in a Science Course at the Community College Level

Institutions of higher education are feeling the pressure to offer a greater number of courses through alternative methods of instructional delivery including hybrid and online courses in an attempt to meet the needs of their students. Among institutions of higher education, community colleges have become a forerunner in online education, in many cases incorporating the development of online education into the institution’s strategic plan. To some educators, hybrid course offerings provide the best of face-to-face education with electronic transfer of information. One of the greatest challenges which exists in the development of a hybrid course is the development of instructional methodologies which utilize cooperative and active learning. All learning management systems utilized by institutions of higher education have some form of online discussion forum as a key component. Online discussion forums have been suggested as an effective pedagogical tool which requires both cooperative interaction amongst students while simultaneously requiring individual active reflection of knowledge. However, current studies have focused on the effectiveness of online discussion forums at the undergraduate and graduate levels. The aim of the current study was to determine the effectiveness of online discussion forums in an upper level science course at the community college level in terms of student satisfaction and student achievement. Analysis of the data acquired from this study determined that the incorporation of online discussion forums as well as individual written reflections as a post-reflective assignment effectively improved student achievement and understanding of scientific topics and concepts related to Microbiology. In addition, it was determined that the students’ attitudes towards the online discussion forum as a cooperative learning experience were somewhat positive. Thus, it can be concluded that the incorporation of online discussion forums into courses at the community college level can be considered as an alternative pedagogical tool which can effectively improve student learning