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Authors: Giacomo Domeniconi 1 ; Konstantinos Semertzidis 2 ; Vanessa Lopez 3 ; Elizabeth M. Daly 3 ; Spyros Kotoulas 3 and Gianluca Moro 1

Affiliations: 1 University of Bologna, Italy ; 2 University of Ioannina, Greece ; 3 IBM Research, Ireland

ISBN: 978-989-758-193-9

Keyword(s): Clustering Algorithms, Conversation Threads, Topic Detection.

Related Ontology Subjects/Areas/Topics: Data Engineering ; Data Management and Quality ; Data Management for Analytics ; Data Modeling and Visualization ; Data Structures and Data Management Algorithms ; Information Quality

Abstract: Efficiently detecting conversation threads from a pool of messages, such as social network chats, emails, comments to posts, news etc., is relevant for various applications, including Web Marketing, Information Retrieval and Digital Forensics. Existing approaches focus on text similarity using keywords as features that are strongly dependent on the dataset. Therefore, dealing with new corpora requires further costly analyses conducted by experts to find out new relevant features. This paper introduces a novel method to detect threads from any type of conversational texts overcoming the issue of previously determining specific features for each dataset. To automatically determine the relevant features of messages we map each message into a three dimensional representation based on its semantic content, the social interactions in terms of sender/recipients and its timestamp; then clustering is used to detect conversation threads. In addition, we propose a supervised approach to detect c onversation threads that builds a classification model which combines the above extracted features for predicting whether a pair of messages belongs to the same thread or not. Our model harnesses the distance measure of a message to a cluster representing a thread to capture the probability that a message is part of that same thread. We present our experimental results on seven datasets, pertaining to different types of messages, and demonstrate the effectiveness of our method in the detection of conversation threads, clearly outperforming the state of the art and yielding an improvement of up to a 19%. (More)

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Paper citation in several formats:
Domeniconi, G.; Semertzidis, K.; Lopez, V.; Daly, E.; Kotoulas, S. and Moro, G. (2016). A Novel Method for Unsupervised and Supervised Conversational Message Thread Detection.In Proceedings of the 5th International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-193-9, pages 43-54. DOI: 10.5220/0006001100430054

@conference{data16,
author={Giacomo Domeniconi. and Konstantinos Semertzidis. and Vanessa Lopez. and Elizabeth M. Daly. and Spyros Kotoulas. and Gianluca Moro.},
title={A Novel Method for Unsupervised and Supervised Conversational Message Thread Detection},
booktitle={Proceedings of the 5th International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2016},
pages={43-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006001100430054},
isbn={978-989-758-193-9},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - A Novel Method for Unsupervised and Supervised Conversational Message Thread Detection
SN - 978-989-758-193-9
AU - Domeniconi, G.
AU - Semertzidis, K.
AU - Lopez, V.
AU - Daly, E.
AU - Kotoulas, S.
AU - Moro, G.
PY - 2016
SP - 43
EP - 54
DO - 10.5220/0006001100430054

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