poster

Conversationally-inspired stylometric features for authorship attribution in instant messaging

Authors:

Marco Cristani,

Cristina Segalin,

Alessandro Vinciarelli,

Vittorio MurinoAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 1121 - 1124

https://doi.org/10.1145/2393347.2396398

Published: 29 October 2012 Publication History

Abstract

Authorship attribution (AA) aims at recognizing automatically the author of a given text sample. Traditionally applied to literary texts, AA faces now the new challenge of recognizing the identity of people involved in chat conversations. These share many aspects with spoken conversations, but AA approaches did not take it into account so far. Hence, this paper tries to fill the gap and proposes two novelties that improve the effectiveness of traditional AA approaches for this type of data: the first is to adopt features inspired by Conversation Analysis (in particular for turn-taking), the second is to extract the features from individual turns rather than from entire conversations. The experiments have been performed over a corpus of dyadic chat conversations (77 individuals in total). The performance in identifying the persons involved in each exchange, measured in terms of area under the Cumulative Match Characteristic curve, is 89.5%.

References

[1]

A. Abbasi and H. Chen. Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace. ACM Transactions on Information Systems, 26(2):1--29, 2008.

Digital Library

[2]

A. Abbasi, H. Chen, and J. Nunamaker. Stylometric identification in electronic markets: Scalability and robustness. Journal of Management Information Systems, 25(1):49--78, 2008.

Digital Library

[3]

S. Argamon, M. Koppel, J. Pennebaker, and J. Schler. Automatically profiling the author of an anonymous text. Communications of ACM, 52(2):119--123, 2009.

Digital Library

[4]

R. Bolle, J. Connell, S. Pankanti, N. Ratha, and A. Senior. Guide to Biometrics. Springer Verlag, 2003.

Digital Library

[5]

O. De Vel, A. Anderson, M. Corney, and G. Mohay. Mining e-mail content for auhtor identification forensics. ACM SIGMOD Record, 30(4), 2001.

Digital Library

[6]

R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley and Sons, 2001.

Digital Library

[7]

D. I. Holmes. The evolution of stylometry in humanities scholarship. Literary and Linguistic Computing, 13(3):111--117, 1998.

[8]

F. Iqbal, H. Binsalleeh, B. C. M. Fung, and M. Debbabi. A unified data mining solution for authorship analysis in anonymous textual communications. Information Sciences, 2011.

Digital Library

[9]

L. Kuncheva. A stability index for feature selection. In IASTED International Multi-Conference Artificial Intelligence and Applications, pages 390--395, 2007.

Digital Library

[10]

H. Liu and H. Motoda. Computational Methods of Feature Selection. Chapman and Hall, 2008.

Digital Library

[11]

A. Orebaugh and J. Allnutt. Classification of instant messaging communications for forensics analysis. Social Networks, pages 22--28, 2009.

[12]

E. Stamatatos. A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3):538--556, 2009.

Digital Library

[13]

A. Vinciarelli, M. Pantic, and H. Bourlard. Social Signal Processing: Survey of an emerging domain. Image and Vision Computing Journal, 27(12):1743--1759, 2009.

Digital Library

[14]

R. Zheng, J. Li, H. Chen, and Z. Huang. A framework for authorship identification of online messages: Writing-style features and classification techniques. Journal of the American Society for Information Science and Technology, 57(3):378--393, 2006.

Digital Library

[15]

D. Zhou, L. Zhang. Can online behavior unveil deceivers? -- an exploratory investigation of deception in instant messaging. In Proceedings of the Annual Hawaii International Conference on System Sciences, 2004.

Digital Library

Cited By

Lee DCho H(2024)Recognition algorithm for cross-texting in text chat conversationsData & Knowledge Engineering10.1016/j.datak.2023.102261150(102261)Online publication date: Mar-2024
https://doi.org/10.1016/j.datak.2023.102261
Shuzan MChowdhury MChowdhury MMurugappan MHoque Bhuiyan EArslane Ayari MKhandakar A(2023)Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram SignalsBioengineering10.3390/bioengineering1002016710:2(167)Online publication date: 28-Jan-2023
https://doi.org/10.3390/bioengineering10020167
Hu XOu WAcharya SDing SD’Gama RYu H(2023)TDRLM: Stylometric learning for authorship verification by Topic-DebiasingExpert Systems with Applications10.1016/j.eswa.2023.120745233(120745)Online publication date: Dec-2023
https://doi.org/10.1016/j.eswa.2023.120745
Show More Cited By

Index Terms

Conversationally-inspired stylometric features for authorship attribution in instant messaging
1. Applied computing
  1. Document management and text processing

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Published In

cover image ACM Conferences

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Lee DCho H(2024)Recognition algorithm for cross-texting in text chat conversationsData & Knowledge Engineering10.1016/j.datak.2023.102261150(102261)Online publication date: Mar-2024
https://doi.org/10.1016/j.datak.2023.102261
Shuzan MChowdhury MChowdhury MMurugappan MHoque Bhuiyan EArslane Ayari MKhandakar A(2023)Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram SignalsBioengineering10.3390/bioengineering1002016710:2(167)Online publication date: 28-Jan-2023
https://doi.org/10.3390/bioengineering10020167
Hu XOu WAcharya SDing SD’Gama RYu H(2023)TDRLM: Stylometric learning for authorship verification by Topic-DebiasingExpert Systems with Applications10.1016/j.eswa.2023.120745233(120745)Online publication date: Dec-2023
https://doi.org/10.1016/j.eswa.2023.120745
Kumar ASpaniol MBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)There is a fine Line between Personalization and Surveillance: Semantic User Interest Tracing via Entity-level AnalyticsProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531592(22-33)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531592
Maupomé DArmstrong MBelbahar RAlezot JBalassiano RRancourt FQueudot MMosser SMeurs M(2022)Automatically Estimating the Severity of Multiple Symptoms Associated with DepressionEarly Detection of Mental Health Disorders by Social Media Monitoring10.1007/978-3-031-04431-1_11(247-261)Online publication date: 15-Sep-2022
https://doi.org/10.1007/978-3-031-04431-1_11
Vargas FPardo T(2022)Studying Dishonest Intentions in Brazilian Portuguese TextsDeceptive AI10.1007/978-3-030-91779-1_12(166-178)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-91779-1_12
Mazurek MRomaniuk M(2021)Attribution of authorship in instant messaging software applications, based on similarity measures of the stylometric features’ vectorComputer Science and Mathematical Modelling10.5604/01.3001.0015.2735(33-41)Online publication date: 30-Jun-2021
https://doi.org/10.5604/01.3001.0015.2735
Alonso-Fernandez FBelvisi NHernandez-Diaz KMuhammad NBigun J(2021)Writer Identification Using Microblogging Texts for Social Media ForensicsIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2021.30780733:3(405-426)Online publication date: Jul-2021
https://doi.org/10.1109/TBIOM.2021.3078073
Buker AVinciarelli A(2021)I Feel it in Your Fingers: Inference of Self-Assessed Personality Traits from Keystroke Dynamics in Dyadic Interactive Chats2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)10.1109/ACII52823.2021.9597389(1-8)Online publication date: 28-Sep-2021
https://doi.org/10.1109/ACII52823.2021.9597389
Shuzan MChowdhury MHossain MChowdhury MReaz MUddin MKhandakar AMahbub ZAli S(2021)A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning ModelIEEE Access10.1109/ACCESS.2021.30953809(96775-96790)Online publication date: 2021
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