-
Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018)
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- Theme
- Data Mining
-
Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018)
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- Theme
- Data Mining
-
Organisciak, P.; Schmidt, B.M.; Downie, J.S.: Giving shape to large digital libraries through exploratory data analysis (2022)
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- Theme
- Data Mining
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Schwartz, F.; Fang, Y.C.: Citation data analysis on hydrogeology (2007)
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- Abstract
- This article explores the status of research in hydrogeology using data mining techniques. First we try to explain what citation analysis is and review some of the previous work on citation analysis. The main idea in this article is to address some common issues about citation numbers and the use of these data. To validate the use of citation numbers, we compare the citation patterns for Water Resources Research papers in the 1980s with those in the 1990s. The citation growths for highly cited authors from the 1980s are used to examine whether it is possible to predict the citation patterns for highly-cited authors in the 1990s. If the citation data prove to be steady and stable, these numbers then can be used to explore the evolution of science in hydrogeology. The famous quotation, "If you are not the lead dog, the scenery never changes," attributed to Lee Iacocca, points to the importance of an entrepreneurial spirit in all forms of endeavor. In the case of hydrogeological research, impact analysis makes it clear how important it is to be a pioneer. Statistical correlation coefficients are used to retrieve papers among a collection of 2,847 papers before and after 1991 sharing the same topics with 273 papers in 1991 in Water Resources Research. The numbers of papers before and after 1991 are then plotted against various levels of citations for papers in 1991 to compare the distributions of paper population before and after that year. The similarity metrics based on word counts can ensure that the "before" papers are like ancestors and "after" papers are descendants in the same type of research. This exercise gives us an idea of how many papers are populated before and after 1991 (1991 is chosen based on balanced numbers of papers before and after that year). In addition, the impact of papers is measured in terms of citation presented as "percentile," a relative measure based on rankings in one year, in order to minimize the effect of time.
- Theme
- Data Mining
-
Wu, T.; Pottenger, W.M.: ¬A semi-supervised active learning algorithm for information extraction from textual data (2005)
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- Theme
- Data Mining
-
Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013)
0.02
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- Theme
- Data Mining
-
O'Brien, H.L.; Lebow, M.: Mixed-methods approach to measuring user experience in online news interactions (2013)
0.02
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- Theme
- Data Mining
-
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)
0.02
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- Theme
- Data Mining
-
Zhang, Z.; Li, Q.; Zeng, D.; Ga, H.: Extracting evolutionary communities in community question answering (2014)
0.02
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- Theme
- Data Mining
-
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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- Theme
- Data Mining
-
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)
0.02
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- Theme
- Data Mining
-
Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008)
0.02
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- Theme
- Data Mining
-
Saggi, M.K.; Jain, S.: ¬A survey towards an integration of big data analytics to big insights for value-creation (2018)
0.02
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- Theme
- Data Mining
-
Borgman, C.L.; Wofford, M.F.; Golshan, M.S.; Darch, P.T.: Collaborative qualitative research at scale : reflections on 20 years of acquiring global data and making data global (2021)
0.02
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- Theme
- Data Mining
