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  1. Gorrell, G.; Bontcheva, K.: Classifying Twitter favorites : Like, bookmark, or Thanks? (2016) 0.03 
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    Abstract
    Since its foundation in 2006, Twitter has enjoyed a meteoric rise in popularity, currently boasting over 500 million users. Its short text nature means that the service is open to a variety of different usage patterns, which have evolved rapidly in terms of user base and utilization. Prior work has categorized Twitter users, as well as studied the use of lists and re-tweets and how these can be used to infer user profiles and interests. The focus of this article is on studying why and how Twitter users mark tweets as "favorites"-a functionality with currently poorly understood usage, but strong relevance for personalization and information access applications. Firstly, manual analysis and classification are carried out on a randomly chosen set of favorited tweets, which reveal different approaches to using this functionality (i.e., bookmarks, thanks, like, conversational, and self-promotion). Secondly, an automatic favorites classification approach is proposed, based on the categories established in the previous step. Our machine learning experiments demonstrate a high degree of success in matching human judgments in classifying favorites according to usage type. In conclusion, we discuss the purposes to which these data could be put, in the context of identifying users' patterns of interests.
  2. Perianes-Rodriguez, A.; Ruiz-Castillo, J.: ¬The impact of classification systems in the evaluation of the research performance of the Leiden Ranking universities (2018) 0.03 
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    Abstract
    In this article, we investigate the consequences of choosing different classification systems-namely, the way publications (or journals) are assigned to scientific fields-for the ranking of research units. We study the impact of this choice on the ranking of 500 universities in the 2013 edition of the Leiden Ranking in two cases. First, we compare a Web of Science (WoS) journal-level classification system, consisting of 236 subject categories, and a publication-level algorithmically constructed system, denoted G8, consisting of 5,119 clusters. The result is that the consequences of the move from the WoS to the G8 system using the Top 1% citation impact indicator are much greater than the consequences of this move using the Top 10% indicator. Second, we compare the G8 classification system and a publication-level alternative of the same family, the G6 system, consisting of 1,363 clusters. The result is that, although less important than in the previous case, the consequences of the move from the G6 to the G8 system under the Top 1% indicator are still of a large order of magnitude.
  3. Cronin, B.: Thinking about data (2013) 0.02 
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    Date
    22. 3.2013 16:18:36
  4. Grudin, J.: Human-computer interaction (2011) 0.02 
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    Date
    27.12.2014 18:54:22
  5. Williamson, N.J.: Classification issues in 2011 : report (2012) 0.02 
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    Date
    22. 1.2012 13:17:00
  6. Cronin, B.: ¬The writing on the wall (2015) 0.02 
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    Date
    26. 4.2015 19:27:22
  7. Xiao, G.: ¬A knowledge classification model based on the relationship between science and human needs (2013) 0.02 
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    Date
    22. 2.2013 12:36:34
  8. Szostak, R.: Speaking truth to power in classification : response to Fox's review of my work; KO 39:4, 300 (2013) 0.02 
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    Date
    22. 2.2013 12:35:05
  9. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.02 
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    Date
    18. 3.2014 19:13:22
  10. Sauperl, S.A.: UDC as a standardisation method for providing titles of documents (2015) 0.02 
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    Date
    10.11.2015 10:22:31
  11. Castro, A. de: Mental models may fail when faced with self-referential descriptors (2016) 0.02 
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    Date
    22. 1.2016 14:45:01
  12. Shaw, R.; Golden, P.; Buckland, M.: Using linked library data in working research notes (2015) 0.02 
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    Date
    15. 1.2016 19:22:28
  13. Foskett, D.J.: Systems theory and its relevance to documentary classification (2017) 0.02 
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    Date
    6. 5.2017 18:46:22
  14. Bursa, O. et al.: Enriching preferences using DBpedia and Wordnet (2016) 0.02 
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  15. Guidi, F.; Sacerdoti Coen, C.: ¬A survey on retrieval of mathematical knowledge (2015) 0.02 
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    Date
    22. 2.2017 12:51:57
  16. Paul-Hus, A.; Desrochers, N.; Rijcke, S.de; Rushforth, A.D.: ¬The reward system of science (2017) 0.02 
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    Date
    20. 1.2015 18:30:22
  17. Calì, A. et al.: Processing keyword queries under access limitations (2016) 0.02 
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    1. 2.2016 18:25:22
  18. Egghe, L.; Guns, R.; Rousseau, R.; Leuven, K.U.: Erratum (2012) 0.02 
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    Date
    14. 2.2012 12:53:22
  19. Papadakis, I. et al.: Highlighting timely information in libraries through social and semantic Web technologies (2016) 0.02 
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    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  20. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.02 
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    Date
    1. 2.2016 18:25:22

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