Search (30 results, page 1 of 2)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Internet"
  1. Arbelaitz, O.; Martínez-Otzeta. J.M.; Muguerza, J.: User modeling in a social network for cognitively disabled people (2016) 0.07 
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    Abstract
    Online communities are becoming an important tool in the communication and participation processes in our society. However, the most widespread applications are difficult to use for people with disabilities, or may involve some risks if no previous training has been undertaken. This work describes a novel social network for cognitively disabled people along with a clustering-based method for modeling activity and socialization processes of its users in a noninvasive way. This closed social network is specifically designed for people with cognitive disabilities, called Guremintza, that provides the network administrators (e.g., social workers) with two types of reports: summary statistics of the network usage and behavior patterns discovered by a data mining process. Experiments made in an initial stage of the network show that the discovered patterns are meaningful to the social workers and they find them useful in monitoring the progress of the users.
    Date
    22. 1.2016 12:02:26
  2. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.05 
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    Abstract
    This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.
    Theme
    Data Mining
  3. Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012) 0.04 
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    Theme
    Data Mining
  4. Lee, L.-H.; Chen, H.-H.: Mining search intents for collaborative cyberporn filtering (2012) 0.03 
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    Abstract
    This article presents a search-intent-based method to generate pornographic blacklists for collaborative cyberporn filtering. A novel porn-detection framework that can find newly appearing pornographic web pages by mining search query logs is proposed. First, suspected queries are identified along with their clicked URLs by an automatically constructed lexicon. Then, a candidate URL is determined if the number of clicks satisfies majority voting rules. Finally, a candidate whose URL contains at least one categorical keyword will be included in a blacklist. Several experiments are conducted on an MSN search porn dataset to demonstrate the effectiveness of our method. The resulting blacklist generated by our search-intent-based method achieves high precision (0.701) while maintaining a favorably low false-positive rate (0.086). The experiments of a real-life filtering simulation reveal that our proposed method with its accumulative update strategy can achieve 44.15% of a macro-averaging blocking rate, when the update frequency is set to 1 day. In addition, the overblocking rates are less than 9% with time change due to the strong advantages of our search-intent-based method. This user-behavior-oriented method can be easily applied to search engines for incorporating only implicit collective intelligence from query logs without other efforts. In practice, it is complementary to intelligent content analysis for keeping up with the changing trails of objectionable websites from users' perspectives.
  5. Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.03 
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    Theme
    Data Mining
  6. Welzer, H.: ¬Die smarte Diktatur : der Angriff auf unsere Freiheit (2016) 0.03 
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    RSWK
    Data Mining
    Subject
    Data Mining
  7. Stuart, D.: Web metrics for library and information professionals (2014) 0.02 
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    LCSH
    Data mining
    Subject
    Data mining
  8. Schultz, S.: ¬Die eine App für alles : Mobile Zukunft in China (2016) 0.02 
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    Date
    22. 6.2018 14:22:02
  9. Barrio, P.; Gravano, L.: Sampling strategies for information extraction over the deep web (2017) 0.02 
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    Abstract
    Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections-and in which order-to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.
  10. Landwehr, A.: China schafft digitales Punktesystem für den "besseren" Menschen (2018) 0.01 
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    Date
    22. 6.2018 14:29:46
  11. Andrade, T.C.; Dodebei, V.: Traces of digitized newspapers and bom-digital news sites : a trail to the memory on the internet (2016) 0.01 
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    Date
    19. 1.2019 17:42:22
  12. Social Media und Web Science : das Web als Lebensraum, Düsseldorf, 22. - 23. März 2012, Proceedings, hrsg. von Marlies Ockenfeld, Isabella Peters und Katrin Weller. DGI, Frankfurt am Main 2012 (2012) 0.01 
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  13. Oguz, F.; Koehler, W.: URL decay at year 20 : a research note (2016) 0.01 
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    Date
    22. 1.2016 14:37:14
  14. Hartmann, B.: Ab ins MoMA : zum virtuellen Museumsgang (2011) 0.01 
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    Date
    3. 5.1997 8:44:22
  15. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.01 
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    Date
    22. 1.2011 14:27:06
  16. Okoli, C.; Mehdi, M.; Mesgari, M.; Nielsen, F.A.; Lanamäki, A.: Wikipedia in the eyes of its beholders : a systematic review of scholarly research on Wikipedia readers and readership (2014) 0.01 
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    Date
    18.11.2014 13:22:03
  17. Firnkes, M.: Schöne neue Welt : der Content der Zukunft wird von Algorithmen bestimmt (2015) 0.01 
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    Date
    5. 7.2015 22:02:31
  18. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.01 
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    Date
    4. 8.2015 19:22:04
  19. Evans, H.K.; Ovalle, J.; Green, S.: Rockin' robins : do congresswomen rule the roost in the Twittersphere? (2016) 0.01 
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    Date
    22. 1.2016 11:51:19
  20. Dufour, C.; Bartlett, J.C.; Toms, E.G.: Understanding how webcasts are used as sources of information (2011) 0.01 
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    Date
    22. 1.2011 14:16:14

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