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An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages

Authors:

Ion Androutsopoulos,

Konstantinos V. Chandrinos,

Constantine D. SpyropoulosAuthors Info & Claims

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

Pages 160 - 167

https://doi.org/10.1145/345508.345569

Published: 01 July 2000 Publication History

Abstract

The growing problem of unsolicited bulk e-mail, also known as “spam”, has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in “encrypted” form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader.

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Cited By

Kundra SKant RSingh SSaini RGupta BJain V(2024)Amalgamating an Intelligent Variant of the Gravitational Search Algorithm with Decision Trees for Email Spam DetectionInternational Journal of Modern Physics C10.1142/S0129183124501912Online publication date: 20-Jun-2024
https://doi.org/10.1142/S0129183124501912
Campit J(2023)Analyzing Customer Satisfaction using Support Vector Machine and Naive Bayes Utilizing Filipino TextWSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT10.37394/232015.2023.19.5019(514-524)Online publication date: 6-Jun-2023
https://doi.org/10.37394/232015.2023.19.50
Nguyen T(2023)Topic based document modeling for information filteringCTU Journal of Innovation and Sustainable Development10.22144/ctujoisd.2023.04015:ISDS(102-109)Online publication date: 16-Oct-2023
https://doi.org/10.22144/ctujoisd.2023.040
Show More Cited By

Index Terms

An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. World Wide Web
    1. Web applications
      1. Internet communications tools
        Email

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Published In

cover image ACM Conferences

SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval

July 2000

396 pages

ISBN:1581132263

DOI:10.1145/345508

Chairmen:
Emmanuel Yannakoudakis
Athens Univ. of Economics and Business, Greece
,
Nicholas J. Belkin
Rutgers Univ.
,
Mun-Kew Leong
Kent Ridge Digital Labs
,
Peter Ingwersen
Royal School of Library and Information Science

Copyright © 2000 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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Greek Com Soc: Greek Computer Society
SIGIR: ACM Special Interest Group on Information Retrieval
Athens U of Econ & Business: Athens University of Economics and Business

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2000

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SIGIR00: 23rd ACM International SIGIR Conference on Research and Development in Information Retrieval

July 24 - 28, 2000

Athens, Greece

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Cited By

Kundra SKant RSingh SSaini RGupta BJain V(2024)Amalgamating an Intelligent Variant of the Gravitational Search Algorithm with Decision Trees for Email Spam DetectionInternational Journal of Modern Physics C10.1142/S0129183124501912Online publication date: 20-Jun-2024
https://doi.org/10.1142/S0129183124501912
Campit J(2023)Analyzing Customer Satisfaction using Support Vector Machine and Naive Bayes Utilizing Filipino TextWSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT10.37394/232015.2023.19.5019(514-524)Online publication date: 6-Jun-2023
https://doi.org/10.37394/232015.2023.19.50
Nguyen T(2023)Topic based document modeling for information filteringCTU Journal of Innovation and Sustainable Development10.22144/ctujoisd.2023.04015:ISDS(102-109)Online publication date: 16-Oct-2023
https://doi.org/10.22144/ctujoisd.2023.040
Nosrati VRahmani MJolfaei ASeifollahi S(2023)A Weak-Region Enhanced Bayesian Classification for Spam Content-Based FilteringACM Transactions on Asian and Low-Resource Language Information Processing10.1145/351042022:3(1-18)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1145/3510420
Kumar IDutta K(2023)Rhyme detection of Hindi and Rajasthani Poems using Statistical-Based Methods2023 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC457730.2023.10262983(1-5)Online publication date: 21-Apr-2023
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Wang RCao NGuo YJi SKumar S(2022)A Comparative Analysis of Fraudulent Recruitment Advertisement Detection Methods in the IoT EnvironmentJournal of Sensors10.1155/2022/45835122022(1-11)Online publication date: 8-Nov-2022
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Butnaru AMylonas APitropakis N(2021)Towards Lightweight URL-Based Phishing DetectionFuture Internet10.3390/fi1306015413:6(154)Online publication date: 13-Jun-2021
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Timbadia DVesaokar N(2021)Spam Text DetectionInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT2173151(698-704)Online publication date: 12-Jun-2021
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