-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
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- Date
- 22. 3.2013 19:43:01
- Language
- e
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Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
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- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
-
Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
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- Date
- 7. 3.2019 16:32:22
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- e
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Jäger, L.: Von Big Data zu Big Brother (2018)
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- Abstract
- 1983 bewegte ein einziges Thema die gesamte Bundesrepublik: die geplante Volkszählung. Jeder Haushalt in Westdeutschland sollte Fragebögen mit 36 Fragen zur Wohnsituation, den im Haushalt lebenden Personen und über ihre Einkommensverhältnisse ausfüllen. Es regte sich massiver Widerstand, hunderte Bürgerinitiativen formierten sich im ganzen Land gegen die Befragung. Man wollte nicht "erfasst" werden, die Privatsphäre war heilig. Es bestand die (berechtigte) Sorge, dass die Antworten auf den eigentlich anonymisierten Fragebögen Rückschlüsse auf die Identität der Befragten zulassen. Das Bundesverfassungsgericht gab den Klägern gegen den Zensus Recht: Die geplante Volkszählung verstieß gegen den Datenschutz und damit auch gegen das Grundgesetz. Sie wurde gestoppt. Nur eine Generation später geben wir sorglos jedes Mal beim Einkaufen die Bonuskarte der Supermarktkette heraus, um ein paar Punkte für ein Geschenk oder Rabatte beim nächsten Einkauf zu sammeln. Und dabei wissen wir sehr wohl, dass der Supermarkt damit unser Konsumverhalten bis ins letzte Detail erfährt. Was wir nicht wissen, ist, wer noch Zugang zu diesen Daten erhält. Deren Käufer bekommen nicht nur Zugriff auf unsere Einkäufe, sondern können über sie auch unsere Gewohnheiten, persönlichen Vorlieben und Einkommen ermitteln. Genauso unbeschwert surfen wir im Internet, googeln und shoppen, mailen und chatten. Google, Facebook und Microsoft schauen bei all dem nicht nur zu, sondern speichern auf alle Zeiten alles, was wir von uns geben, was wir einkaufen, was wir suchen, und verwenden es für ihre eigenen Zwecke. Sie durchstöbern unsere E-Mails, kennen unser persönliches Zeitmanagement, verfolgen unseren momentanen Standort, wissen um unsere politischen, religiösen und sexuellen Präferenzen (wer kennt ihn nicht, den Button "an Männern interessiert" oder "an Frauen interessiert"?), unsere engsten Freunde, mit denen wir online verbunden sind, unseren Beziehungsstatus, welche Schule wir besuchen oder besucht haben und vieles mehr.
- Date
- 22. 1.2018 11:33:49
-
Blake, C.: Text mining (2011)
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Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016)
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Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014)
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- Abstract
- The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.
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-
Bella, A. La; Fronzetti Colladon, A.; Battistoni, E.; Castellan, S.; Francucci, M.: Assessing perceived organizational leadership styles through twitter text mining (2018)
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Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018)
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Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012)
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Chardonnens, A.; Hengchen, S.: Text mining for cultural heritage institutions : a 5-step method for cultural heritage institutions (2017)
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Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016)
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Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010)
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Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010)
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Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015)
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Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014)
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Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019)
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Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010)
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Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
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Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010)
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