-
Song, J.; Huang, Y.; Qi, X.; Li, Y.; Li, F.; Fu, K.; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.4, S.915-927
-
Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011)
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- Classification
- TZG (FH K)
- GHBS
- TZG (FH K)
- Pages
- XX, 622 S
-
Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013)
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- Abstract
- Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1399-1410
-
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
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
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- Source
- 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
- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411
-
Jäger, L.: Von Big Data zu Big Brother (2018)
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- Date
- 22. 1.2018 11:33:49
-
Blake, C.: Text mining (2011)
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- Source
- Annual review of information science and technology. 45(2011) no.1, S.121-155
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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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- Pages
- S.177-191
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Berendt, B.; Krause, B.; Kolbe-Nusser, S.: Intelligent scientific authoring tools : interactive data mining for constructive uses of citation networks (2010)
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- Abstract
- Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.
- Source
- Information processing and management. 46(2010) no.1, S.1-10
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Mandl, T.: Text mining und data minig (2013)
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- S.183-191
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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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- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2566-2579
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Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017)
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- Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1671-1686
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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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- Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
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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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- Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1597-1614
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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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- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.21-31
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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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- Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1668-1678
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Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012)
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- Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2549-2554,
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Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014)
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- Journal of the Association for Information Science and Technology. 65(2014) no.3, S.621-634
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Ekbia, H.; Mattioli, M.; Kouper, I.; Arave, G.; Ghazinejad, A.; Bowman, T.; Suri, V.R.; Tsou, A.; Weingart, S.; Sugimoto, C.R.: Big data, bigger dilemmas : a critical review (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1523-1545
