Multiparticipant chat analysis: A survey

Uthus, David; Aha, David W.

doi:10.1016/j.artint.2013.02.004

Cited by 49 publications

(14 citation statements)

References 17 publications

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“…Separating overlapping conversations in chat data is challenging, and there are many techniques for doing so [29]. However, in our case overlapping conversations were rare due to low chat activity (µ = 8.2 messages per hour), permitting trivial segmentation of conversations based on a pause between conversations of at least 1 hour.…”

Section: Chat Conversationsmentioning

confidence: 96%

Chatrooms in MOOCs

Coetzee

Fox

Hearst

et al. 2014

Proceedings of the First ACM Conference on Learning @ Scale Conference

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We study effects of introducing a real-time chatroom into a massive open online course with several thousand students, supplementing an existing forum. The chatroom was supported by teaching assistants, and generated thousands of lines of discussion by 28% of 681 consenting chat condition participants, mostly on-topic. Despite this, chat activity remained low (µ = 8.2 messages per hour) and we could find no significant effect of chat use on objective or subjective dependent variables such as grades, retention, forum participation, or students' sense of community. Further investigation reveals that only 12% of chat participants have substantive interactions, while the remainder are either passive or have trivial interactions that are unlikely to result in learning.We also find that pervasive, highly visible chat interfaces are highly effective in encouraging both active and substantive participation in chat. When compared to chat interfaces that are restricted to a single webpage, the pervasive interface exhibits 2.8 times as many users with substantive interactions.

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Section: Chat Conversationsmentioning

confidence: 96%

Chatrooms in MOOCs

Coetzee

Fox

Hearst

et al. 2014

Proceedings of the First ACM Conference on Learning @ Scale Conference

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“…As people are most familiar to use natural language for human communication, this mode is also accepted in human computer interaction as a powerful way for interacting with computers by using spoken or written language or natural body language [46]. In their analysis of the current state of multiparticipant chat research, Uthus and Aha [42] identify the participation of artificial agents in chat conversations, such as bots in chat rooms or non-player characters in video games as an underexplored area of research. They particularly stress the importance of learning to design CAs that can participate in chat conversations, and analyzing conversations, with some non human users.…”

Section: Conversational Agentsmentioning

confidence: 99%

Where is the Bot in our Team? Toward a Taxonomy of Design Option Combinations for Conversational Agents in Collaborative Work

Bittner¹,

Oeste-Reiß²,

Leimeister³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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With rapid progress in machine learning, language technologies and artificial intelligence, conversational agents (CAs) gain rising attention in research and practice as potential non-human teammates, facilitators or experts in collaborative work. However, designers of CAs in collaboration still struggle with a lack of comprehensive understanding of the vast variety of design options in the dynamic field. We address this gap with a taxonomy to help researchers and designers understand the design space and the interrelations of different design options and recognize useful design option combinations for their CAs. We present the iterative development of a taxonomy for the design of CAs grounded in state of the art literature and validated with domain experts. We identify recurring design option combinations and white spots from the classified objects that will inform further research and development efforts.

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“…Rounding off our studies in this section, Uthus and Aha [30] surveyed research on the analysis of multi-participant chat. The authors conclude that chat data is difficult to analyse due to its unique characteristics due to the many problems the medium presents (e.g.…”

Section: E Studies Related To Text Segmentationmentioning

confidence: 99%

Hello & Goodbye: Conversation Boundary Identification Using Text Classification

Dunne

Malone

2018

2018 29th Irish Signals and Systems Conference (ISSC)

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One of the main challenges in discourse analysis is the process of segmenting text into meaningful topic segments. While this problem has been studied over the past thirty years, previous topic segmentation studies ignore crucial elements of a conversation: an opening and closing remark. Our motivation to revisit this problem space is the rise of instant message usage. We consider the problem of topic segmentation as a machine learning classification one. Using both enterprise and open source datasets, we address the question as to whether a machine learning algorithm can be trained to identify salutations and valedictions within multi-party real-time chat conversations. Our results show that both Naïve Bayes (NB) and Support Vector Machine (SVM) algorithms provide a reasonable degree of precision(mean F1 score: 0.58).

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Multiparticipant chat analysis: A survey

Cited by 49 publications

References 17 publications

Chatrooms in MOOCs

Chatrooms in MOOCs

Where is the Bot in our Team? Toward a Taxonomy of Design Option Combinations for Conversational Agents in Collaborative Work

Hello & Goodbye: Conversation Boundary Identification Using Text Classification

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