{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T06:21:58Z","timestamp":1761805318369,"version":"3.41.2"},"reference-count":61,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2014,8,12]],"date-time":"2014-08-12T00:00:00Z","timestamp":1407801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,8,12]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 The purpose of this paper is to present a thorough analysis of three complementary features of real-scale really simple syndication (RSS)\/Atom feeds, namely, publication activity, items characteristics and their textual vocabulary, that the authors believe are crucial for emerging Web 2.0 applications. Previous works on RSS\/Atom statistical characteristics do not provide a precise and updated characterization of feeds\u2019 behavior and content, characterization that can be used to successfully benchmark the effectiveness and efficiency of various Web syndication processing\/analysis techniques. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 The authors empirical study relies on a large-scale testbed acquired over an eight-month campaign from 2010. They collected a total number of 10,794,285 items originating from 8,155 productive feeds. The authors deeply analyze feeds productivity (types and bandwidth), content (XML, text and duplicates) and textual content (vocabulary and buzz-words). <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The findings of the study are as follows: 17 per cent of feeds produce 97 per cent of the items; a formal characterization of feeds publication rate conducted by using a modified power law; most popular textual elements are the title and description, with the average size of 52 terms; cumulative item size follows a lognormal distribution, varying greatly with feeds type; 47 per cent of the feed-published items share the same description; the vocabulary does not belong to Wordnet terms (4 per cent); characterization of vocabulary growth using Heaps\u2019 laws and the number of occurrences by a stretched exponential distribution conducted; and ranking of terms does not significantly vary for frequent terms. <\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n \u2013 Modeling dedicated Web applications capacities, Defining benchmarks, optimizing Publish\/Subscribe index structures. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 It especially opens many possibilities for tuning Web applications, like an RSS crawler designed with a resource allocator and a refreshing strategy based on the Gini values and evolution to predict bursts for each feed, according to their category and class for targeted feeds; an indexing structure which matches textual items\u2019 content, which takes into account item size according to targeted feeds, size of the vocabulary and term occurrences, updates of the vocabulary and evolution of term ranks, typos and misspelling correction; filtering by pruning items for content duplicates of different feeds and correlation of terms to easily detect replicates. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 A content-oriented analysis of dynamic Web information.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ijwis-06-2014-0023","type":"journal-article","created":{"date-parts":[[2014,9,25]],"date-time":"2014-09-25T05:15:12Z","timestamp":1411622112000},"page":"291-320","source":"Crossref","is-referenced-by-count":7,"title":["RSS feeds behavior analysis, structure and vocabulary"],"prefix":"10.1108","volume":"10","author":[{"given":"Nicolas","family":"Travers","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeinab","family":"Hmedeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nelly","family":"Vouzoukidou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cedric","family":"du Mouza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vassilis","family":"Christophides","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michel","family":"Scholl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122921194060200_b21","doi-asserted-by":"crossref","unstructured":"Ahmad, F.\n and \n Kondrak, G.\n (2005), \u201cLearning a spelling error model from search query logs\u201d, Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing \u2013 HLT \u201905, Association for Computational Linguistics, pp. 955-962.","DOI":"10.3115\/1220575.1220695"},{"key":"key2020122921194060200_b6","doi-asserted-by":"crossref","unstructured":"Aktolga, E.\n and \n Allan, J.\n (2011), \u201cReranking search results for sparse queries\u201d, Proceeding of the ACM 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