research-article

A comparative study of statistical features of language in blogs-vs-splogs

Authors:

Soumya Datta,

Sudeshna SarkarAuthors Info & Claims

AND '08: Proceedings of the second workshop on Analytics for noisy unstructured text data

Pages 63 - 66

https://doi.org/10.1145/1390749.1390760

Published: 24 July 2008 Publication History

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Abstract

Language usage in Blogs deviate from the language used in traditional corpora largely due to the noise from various causes like spelling errors, grammatical irregularity, overuse of abbreviations and symbolic characters like emoticons. Spam Blogs or Splogs comprise the subset of blogs, which are usually written to target specific audience for marketing promotions and are mostly generated by software that readily imitates Zipfian distribution of words. Therefore it becomes a difficult task to separate splogs from non-splogs using only frequentist distribution of unigrams. In this detailed comparative study we present and highlight several additional statistical features of language, which are hard to imitate and serve as good discriminator between splogs and blogs.

References

[1]

A. Java, P. Kolari, T. Finin, J. Mayfield, A. Joshi, and J. Martineau. BlogVox: Separating Blog Wheat from Blog Chaff. In Proceedings of the Workshop on Analytics for Noisy Unstructured Text Data, 20th International Joint Conference on Artificial Intelligence (IJCAI-2007), January 2007.

Google Scholar

[2]

T. Joachims. Making large-scale support vector machine learning practical. pages 169--184, 1999.

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Google Scholar

[3]

P. Kolari, A. Java, T. Finin, T. Oates, and A. Joshi. Detecting Spam Blogs: A Machine Learning Approach. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006). Computer Science and Electrical Engineering, University of Maryland, Baltimore County, July 2006.

Digital Library

Google Scholar

[4]

Y.-R. Lin, H. Sundaram, Y. Chi, J. Tatemura, and B. L. Tseng. Splog detection using self-similarity analysis on blog temporal dynamics. In AIRWeb '07: Proceedings of the 3rd international workshop on Adversarial information retrieval on the web, pages 1--8, New York, NY, USA, 2007. ACM.

Digital Library

Google Scholar

[5]

K. Yoshida, F. Adachi, T. Washio, H. Motoda, T. Homma, A. Nakashima, H. Fujikawa, and K. Yamazaki. Density-based spam detector. In KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 486--493, New York, NY, USA, 2004. ACM.

Digital Library

Google Scholar

Cited By

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Singh A(2017)Mining of Social Media data of University studentsEducation and Information Technologies10.1007/s10639-016-9501-122:4(1515-1526)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10639-016-9501-1
Prasath R(2010)Learning age and gender using co-occurrence of non-dictionary words from stylistic variationsProceedings of the 7th international conference on Rough sets and current trends in computing10.5555/1876210.1876278(544-550)Online publication date: 28-Jun-2010
https://dl.acm.org/doi/10.5555/1876210.1876278
Prasath R(2010)Learning Age and Gender Using Co-occurrence of Non-dictionary Words from Stylistic VariationsRough Sets and Current Trends in Computing10.1007/978-3-642-13529-3_58(544-550)Online publication date: 2010
https://doi.org/10.1007/978-3-642-13529-3_58
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Index Terms

A comparative study of statistical features of language in blogs-vs-splogs
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. Information retrieval
    1. Document representation

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Information & Contributors

Information

Published In

AND '08: Proceedings of the second workshop on Analytics for noisy unstructured text data

July 2008

130 pages

ISBN:9781605581965

DOI:10.1145/1390749

Conference Chairs:
Daniel Lopresti
Lehigh University
,
Shourya Roy
IBM India Research Lab
,
Klaus Schulz
University of Munich
,
L. Venkata Subramaniam
India Research Lab

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2008

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AND '08

AND '08: Second Workshop on Analytics for Noisy Unstructured Text Data

July 24, 2008

Singapore

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Overall Acceptance Rate 15 of 22 submissions, 68%

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Singh A(2017)Mining of Social Media data of University studentsEducation and Information Technologies10.1007/s10639-016-9501-122:4(1515-1526)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10639-016-9501-1
Prasath R(2010)Learning age and gender using co-occurrence of non-dictionary words from stylistic variationsProceedings of the 7th international conference on Rough sets and current trends in computing10.5555/1876210.1876278(544-550)Online publication date: 28-Jun-2010
https://dl.acm.org/doi/10.5555/1876210.1876278
Prasath R(2010)Learning Age and Gender Using Co-occurrence of Non-dictionary Words from Stylistic VariationsRough Sets and Current Trends in Computing10.1007/978-3-642-13529-3_58(544-550)Online publication date: 2010
https://doi.org/10.1007/978-3-642-13529-3_58
Dey LHaque SLopresti DRoy SSchulz KSubramaniam L(2009)Studying the effects of noisy text on text mining applicationsProceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data10.1145/1568296.1568314(107-114)Online publication date: 23-Jul-2009
https://dl.acm.org/doi/10.1145/1568296.1568314
Rustagi MPrasath RGoswami SSarkar S(2009)Learning Age and Gender of Blogger from Stylistic VariationProceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence10.1007/978-3-642-11164-8_33(205-212)Online publication date: 15-Dec-2009
https://dl.acm.org/doi/10.1007/978-3-642-11164-8_33

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Blogs: spinning a web of virality

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