Abstract
Online communities have become increasingly popular sources of information for both users and organisations. Every day thousands of users ask questions on these platforms, yet this knowledge-sharing process is not very studied. In this paper we aim to fill this knowledge-gap, by providing a general framework for studying the knowledge-sharing processes in such online communities. Specifically, we provide a three-step algorithm, that can create process models from interleaved and unlabelled conversations. We provide an instantiation of our framework, and conduct several experiments to evaluate its performance using the process mining tool Disco. From these experiments we show that it is possible to gain meaningful insights from the conversations on online communities using process mining techniques
Original language | English |
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Title of host publication | Proceedings of the 2020 International Joint Conference on Neural Networks |
Number of pages | 8 |
Publisher | IEEE |
Publication date | 2020 |
ISBN (Print) | 978-1-7281-6927-9 |
DOIs | |
Publication status | Published - 2020 |
Event | 2020 International Joint Conference on Neural Networks - Virtual event, Glasgow , United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 |
Conference
Conference | 2020 International Joint Conference on Neural Networks |
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Location | Virtual event |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 19/07/2020 → 24/07/2020 |
Keywords
- Process Mining
- Information-seeking Conversations
- Classification