Predicting continuous conflict perception with Bayesian Gaussian processes

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception

Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

© 2019, Copyright © 2017 Taylor & Francis Group, LLC. Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations

Research Diary Visual Mapping : a reflective methodological tool for process and strategy-as-practice studies

Balogun, Huff and Johnson (2003) highlight the growing paradox for researchers who must focus on context and details while favouring general lines of research. These authors focus their reflection around the collection of qualitative data, particularly those of discussion groups, collaborative research and of research journal redaction techniques. We propose, in the context of collaborative research, a new utilisation of the personal diary, fuelled by our doctoral experiences in collaborative research. While the personal diary in its usual form increases the level of reflectivity on an intervening process, it is nevertheless difficult to exploit for the work of interpreting and legitimizing research. We therefore propose personal diary mapping. In addition to the advantages of personal diary mapping as a methodological tool for viewing the phenomenon, it allows a process to be described by highlighting specifics that are not obvious in reading a text. Moreover, the process of personal diary mapping provides a contribution to the epistemic work in a constructivist reference because it helps make the relationship between knowledge and empirical information explicit (Martinet 2007). After a summary bringing process studies closer to SaP and a review of the modalities of action research and their implications in terms of ethics and researcher responsibility, we present the origins, principles and benefits of visual mapping as regards the researcher's responsibility. In a second step, we illustrate the normative elements of this approach through a case study on strategic competence development based on personal diary mapping.Research Diary ; Visual Mapping ; methodological tool ; process ; strategy-as-practice

A typology of conflict resolution strategies in e-mail communication

E-mail is used extensively to share ideas, discuss issues and to collaborate in the management of projects. However, it is often considered to be a lean medium of communication, epistolary in style, and lacking in both the verbal and non-verbal cues found in face-to-face communication. These limitations can predispose the message to misunderstandings between interlocutors leading to tensions and the use of aggressive tactics. Ensuing conflicts, if badly managed, can be both destructive and costly. The main premise for this research is that conflict resolution strategies, similar to those found in interpersonal interactions, are used in e-mail communication. The purpose of this study is to identify in group projects the features inherent in the language of e-mail that show the interlocutors' use of these strategies within their written exchanges. The analysis of the data is derived from the e-mail text of three separate project teams working in European Universities. The problem of identifying these strategies is approached from the perspective of Pragmatics. The methodology used is Discourse Analysis. The study is divided into two analytical phases; the first, employs the use of Speech Acts to analyse the written utterances; the second, utilises Sillars' Typology of Conflict Resolution Strategies as a template for identifying the types of conflict used in e-mail communication. The results of this study confirm the use of three kinds of conflict resolution strategies in the e-mail; this allows a comparative analysis of the three groups to be undertaken. These findings are considered to have important implications within the field of Computer-Mediated Communication, particularly for the understanding of expressions of conflict within e-mail contexts as well as their consequences for sender/receiver interaction in project group

Building rapport and a sense of communal identity through play in a second language classroom

Many teachers would recognize that a certain amount of laughter and play in a classroom is one of the signs of a socially cohesive and contented group of learners. However, on the face of it, language play in a multinational second language classroom would seem to be highly constrained by an apparent lack of common cultural reference points and, at the lower end of the proficiency spectrum, by the linguistic abilities of the learners. This paper features an investigation into language play consisting of a teacher and two low-proficiency adult learners from different professional fields and nationalities, enrolled on an intensive Business English course. The analysis is informed by Goffman’s concept of frame, by Bakhtin’s ideas about the heteroglossic and dialogical nature of language, and by Bauman and Briggs’s notion of recontextualization. It shows how the learners build a common pool of prior talk and reference points, alluding to them humorously. The data consists of a series of short episodes which together trace the development of one such shared reference point. Over two days, the learners transform an incident which highlights their shortcomings in the language into a celebratory resource that they playfully use to build rapport and to help in the construction of a shared sense of identity and culture. I argue in this paper that the language play found in the featured data is very similar in kind to that in native speaker interactions

Group Interaction Frontiers in Technology

Over the last decade, the study of group behavior for multimodal interaction technologies has increased. However, we believe that despite its potential benefits on society, there could be more activity in this area. The aim of this workshop is create a forum for more interdisciplinary dialogue on this topic to enable the acceleration of growth. The workshop has been very successful in attracting submissions addressing important facets in the context of technologies for analyzing and aiding groups. This paper provides a summary of the activities of the workshop and the accepted papers

Multimodal Human Group Behavior Analysis

Human behaviors in a group setting involve a complex mixture of multiple modalities: audio, visual, linguistic, and human interactions. With the rapid progress of AI, automatic prediction and understanding of these behaviors is no longer a dream. In a negotiation, discovering human relationships and identifying the dominant person can be useful for decision making. In security settings, detecting nervous behaviors can help law enforcement agents spot suspicious people. In adversarial settings such as national elections and court defense, identifying persuasive speakers is a critical task. It is beneficial to build accurate machine learning (ML) models to predict such human group behaviors. There are two elements for successful prediction of group behaviors. The first is to design domain-specific features for each modality. Social and Psychological studies have uncovered various factors including both individual cues and group interactions, which inspire us to extract relevant features computationally. In particular, the group interaction modality plays an important role, since human behaviors influence each other through interactions in a group. Second, effective multimodal ML models are needed to align and integrate the different modalities for accurate predictions. However, most previous work ignored the group interaction modality. Moreover, they only adopt early fusion or late fusion to combine different modalities, which is not optimal. This thesis presents methods to train models taking multimodal inputs in group interaction videos, and to predict human group behaviors. First, we develop an ML algorithm to automatically predict human interactions from videos, which is the basis to extract interaction features and model group behaviors. Second, we propose a multimodal method to identify dominant people in videos from multiple modalities. Third, we study the nervousness in human behavior by a developing hybrid method: group interaction feature engineering combined with individual facial embedding learning. Last, we introduce a multimodal fusion framework that enables us to predict how persuasive speakers are. Overall, we develop one algorithm to extract group interactions and build three multimodal models to identify three kinds of human behavior in videos: dominance, nervousness and persuasion. The experiments demonstrate the efficacy of the methods and analyze the modality-wise contributions