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Information theory as a measure of information content

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  • Jack Belzer

Abstract

In efficient coding, Information‐Communication‐Coding Theory based on probabilities of occurrence assigns short codes to events with little information content and long codes to events with high information content. This provides a direct relationship of code size to amount of information content. Entropies of surrogates such as citations, abstracts, first paragraphs, last paragraphs, and first and last paragraphs are measures of how well each class of surrogates predicts the relevancy of documents. They are measures of meaningful information in the text of surrogates. Such measures of information are important to information system designers.

Suggested Citation

  • Jack Belzer, 1973. "Information theory as a measure of information content," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 300-304, July.
    • Handle: RePEc:bla:jamest:v:24:y:1973:i:4:p:300-304
    DOI: 10.1002/asi.4630240411
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    Cited by:

    1. Jin Li & Yulan Zhang & Jianping Li & Jiangze Du, 2023. "The Role of Sentiment Tendency in Affecting Review Helpfulness for Durable Products: Nonlinearity and Complementarity," Information Systems Frontiers, Springer, vol. 25(4), pages 1459-1477, August.
    2. Li, Liangqiang & Yang, Liang & Zhao, Meng & Liao, Miyan & Cao, Yunzhong, 2022. "Exploring the success determinants of crowdfunding for cultural and creative projects: An empirical study based on signal theory," Technology in Society, Elsevier, vol. 70(C).

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