Semantic Stability in Wikipedia

Conference paper

DOI: 10.1007/978-3-319-50901-3_31

Part of the Studies in Computational Intelligence book series (SCI, volume 693)
Cite this paper as:
Stanisavljevic D., Hasani-Mavriqi I., Lex E., Strohmaier M., Helic D. (2017) Semantic Stability in Wikipedia. In: Cherifi H., Gaito S., Quattrociocchi W., Sala A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham

Abstract

In this paper we assess the semantic stability of Wikipedia by investigating the dynamics of Wikipedia articles’ revisions over time. In a semantically stable system, articles are infrequently edited, whereas in unstable systems, article content changes more frequently. In other words, in a stable system, the Wikipedia community has reached consensus on the majority of articles. In our work, we measure semantic stability using the Rank Biased Overlap method. To that end, we preprocess Wikipedia dumps to obtain a sequence of plain-text article revisions, whereas each revision is represented as a TF-IDF vector. To measure the similarity between consequent article revisions, we calculate Rank Biased Overlap on subsequent term vectors. We evaluate our approach on 10 Wikipedia language editions including the five largest language editions as well as five randomly selected small language editions. Our experimental results reveal that even in policy driven collaboration networks such as Wikipedia, semantic stability can be achieved. However, there are differences on the velocity of the semantic stability process between small and large Wikipedia editions. Small editions exhibit faster and higher semantic stability than large ones. In particular, in large Wikipedia editions, a higher number of successive revisions is needed in order to reach a certain semantic stability level, whereas, in small Wikipedia editions, the number of needed successive revisions is much lower for the same level of semantic stability.

Keywords

semantic stability semantic similarity TF-IDF RBO Wikipedia 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.VIRTUAL VEHICLE Research CenterGrazAustria
  2. 2.Graz University of Technology and Know-Center GmbHGrazAustria
  3. 3.Graz University of TechnologyGrazAustria
  4. 4.GESIS and University of Koblenz-LandauCologneGermany

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