With the rise in popularity of online conferencing platforms such as Zoom and Teams, there is an increasing need to understand the dynamics of virtual meetings to enhance productivity. One key aspect that can contribute to productive online meetings is conversational involvement. Personality traits influence the behaviour of participants within a group and therefore, have the potential to influence conversational involvement. This research paper aims to explore the impact of personality traits on conversational involvement in online meetings. To interpret personalities, the HEXACO model has been employed. Through a systematic investigation, this study provides valuable insights into how individual personalities influence engagement levels in a group during virtual meetings. The results of this study shed light on the relationship between personality traits and conversational involvement, thus contributing to the existing body of knowledge surrounding effective online meeting practices.

Involvement in an online setting has become increasingly more important in a rapidly digitizing world. Previous research has suggested that lexical alignment is an effective method for increasing interpersonal involvement. Multi-modal systems exist for analysing involvement, but the lexical alignment modality is not commonly used in these. This study examines the lexical alignment modality as a predictive measure of involvement in online group meetings. By analyzing participation indicators in virtual meetings, the aim is to identify moments of high involvement. Results indicate that lexical alignment does not correlate with moments of high involvement. The findings contribute to the scientific understanding of leveraging the verbal modality for analyzing and organizing online meetings.

The importance of understanding how to maximize involvement in virtual meetings has been greatly increased due to the rapid rise of video conferencing tools during the COVID-19 pandemic. This research builds upon the data collected by the MEMO Corpus, specifically interview footage and annotated data, and will aim to explore the various factors that influence group involvement in virtual meetings. The research will be conducted in three steps; data annotation, data exploration, and data analysis. The goal of this paper is to investigate the influence of factors such as likeability, listening, and audience engagement in how conversational ability is perceived, given that the scales provided by the MEMOCorpus are valid and the three measures are independent, and how conversational ability influences group involvement, by exploring the existence of correlations. The results of the analysis will provide insight into the dynamics of conversational involvement in virtual meetings and improve virtual communication practices.

Since the recent rise and advancement of video conferencing platforms such as Zoom, it has become important to interpret the logistics of remote online meetings. Analysing verbal and non-verbal cues (such as body language) between members of these virtual forums can provide additional information regarding the level of conversational involvement of each party. This research focuses on age, gender, demographics and virtual background differences in the context of group conversational discussions. It argues that groups formed of younger adults have a higher level of involvement compared to the older groups. Similarly, this study found that groups with a higher ratio of male participants score better in virtual conversational engagement compared to women preponderant groups. To better understand the influence of these inter- and intra-personal characteristics, a corpus formed of 45 online meetings on the topic of Covid-19 was used. This set of data consists of questionnaires with measurements (demographics and other personal values), as well as detailed annotations of conversational signals, which provide valuable insights into the research topic of conversational involvement. This study includes an experiment to investigate the involvement of individual backgrounds in the prediction of group conversational engagement. Four predictive models were used, namely the Decision Tree, Random Forest, Linear Regression and Generalized Linear Mixed Effects Models. While the Generalized Linear Mixed Effects Model provides more meaningful observations on the statistical effect of these factors, the Random Forest ultimately proved to give the best performance accuracy. The purpose of this research is to improve the connection between humans and technology by studying how inter- and intra- characteristics of individuals impact the involvement of a group in virtual interaction.