Johan Bollen
Johan Lambert Trudo Maria Bollen (born 1971) is a scientist investigating complex systems and networks, the relation between social media and a variety of socio-economic phenomena such as the financial markets, public health, and social well-being, as well as Science of Science with a focus on impact metrics derived from usage data. He presently works as associate professor at the Indiana University School of Informatics of Indiana University Bloomington and a fellow at the SparcS Institute of Wageningen University and Research Centre in the Netherlands. He is best known for his work on scholarly impact metrics, measuring public well-being from large-scale social media data, and correlating Twitter mood to stock market prices.[2] He has taught courses on data mining, information retrieval, and digital libraries. His research has been funded by The Andrew W. Mellon Foundation, National Science Foundation, Library of Congress, National Aeronautics and Space Administration and the Los Alamos National Laboratory. In his free time, he DJs at the Root Cellar Lounge in Bloomington, Indiana. He specializes in Deep House and Techno.
Johan Bollen | |
---|---|
Born | |
Nationality | Belgian |
Citizenship | United States of America |
Known for | Twitter mood predictions, Science of Science, MESUR[1] |
Scientific career | |
Fields | Complex Systems and Networks, Science of Science |
Institutions | Indiana University |
Doctoral advisor | Francis Heylighen |
Website | http://informatics.indiana.edu/jbollen/index.html |
Biography
Bollen received his MS in experimental psychology from the Free University of Brussels in 1993 after a master thesis "Learning to Select Activities: a Conditionable System for an Autonomous Robot that Learns to Use Drive Reduction as Reinforcement."[3] He defended his Ph.D. in psychology from the Free University of Brussels in October 2001 on the subject of cognitive models of human hypertext navigation.[4] From 2002 to 2005 he was an assistant professor at the Department of Computer Science of Old Dominion University. He was a staff scientist at the Los Alamos National Laboratory from 2005 to 2009. He is currently a professor at the Indiana University School of Informatics and Computing.
Work
Bollen's work has been extensively covered in the press and popular media and has received over 6,900 citations (2016 h-index 33). Bollen was awarded two patents: one for "Usage based indicators to assess the impact of scholarly works: architecture and method" (US 8135662 B2) and "Predicting economic trends via network communication mood tracking" (US 8380607 B2). His work has found widespread application, e.g. in systems for digital library recommendations systems (Ex Libris bX Recommender Service) and in systems that use social media information to generate financial trading signals. As a result of his work on predicting the stock market based on Twitter mood, Derwent Capital Markets started the Absolute Return fund, the worlds's first Twitter hedge fund.[5] However, it later shut down after thirteen months.[6]
Selected academic works
Bollen has published over 70 papers.[7] A selection of the highly cited ones:[8]
- 2011, Twitter mood predicts the stock market.[2]
- 2005, Co-authorship networks in the digital library research community.[9][10]
- 2009, Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena [11]
- 2006: Bollen, Johan; Rodriquez, Marko A.; Van de Sompel, Herbert (2006). "Journal status". Scientometrics. 69 (3): 669–687.[12]
- 2009, A Principal Component Analysis of 39 Scientific Impact Measures[13]
- 2005, Toward alternative metrics of journal impact: A comparison of download and citation data[14]
- 2014, Proposed SOFA, a new method for distribution of research funding.[15]
References
- "Mesur – Homepage". Mesur.org. Archived from the original on 2013-12-01. Retrieved 2013-12-01.
- Bollen, Johan; Mao, Huina; Zeng, Xiao-Jun (March 2011). "Twitter mood predicts the stock market". Journal of Computational Science. 2 (1): 1–8. arXiv:1010.3003. doi:10.1016/j.jocs.2010.12.007. S2CID 14727513.
- "Johan Bollen – CV" (PDF). Cs.odu.edu. Retrieved 2013-12-01.
- "News". Vub.ac.be. Retrieved 2013-12-01.
- Assoian, Michael. "British hedge fund invests 25 million pounds for IU professor's Twitter research | Campus | Indiana Daily Student". Idsnews.com. Archived from the original on 2013-12-03. Retrieved 2013-12-01.
- "Investing in the social media age". GulfNews.com. 2013-09-15. Retrieved 2013-12-01.
- Johan Bollen at DBLP Bibliography Server
- Johan Bollen publications indexed by Google Scholar
- http://public.lanl.gov/herbertv/papers/Papers/2005/IPMliu.pdf
- Liu, Xiaoming; Bollen, Johan; Nelson, Michael L.; Van de Sompel, Herbert (1 December 2005). "Co-authorship networks in the digital library research community". Information Processing & Management. 41 (6): 1462–1480. arXiv:cs/0502056. Bibcode:2005cs........2056L. doi:10.1016/j.ipm.2005.03.012. S2CID 2536130.
- Bollen, Johan; Pepe, Alberto; Mao, Huina (2009). "Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena". arXiv:0911.1583 [cs.CY].
- Bollen, Johan; Rodriquez, Marko A.; Van de Sompel, Herbert (1 December 2006). "Journal status". Scientometrics. 69 (3): 669–687. arXiv:cs/0601030. doi:10.1007/s11192-006-0176-z. S2CID 8572274.
- Bollen, Johan; Van de Sompel, Herbert; Hagberg, Aric; Chute, Ryan; Mailund, Thomas (28 June 2009). Mailund, Thomas (ed.). "A Principal Component Analysis of 39 Scientific Impact Measures". PLOS ONE. 4 (6): e6022. arXiv:0902.2183. Bibcode:2009PLoSO...4.6022B. doi:10.1371/journal.pone.0006022. PMC 2699100. PMID 19562078.
- Bollen, Johan; Van de Sompel, Herbert; Smith, Joan A.; Luce, Rick (1 December 2005). "Toward alternative metrics of journal impact: A comparison of download and citation data". Information Processing & Management. 41 (6): 1419–1440. arXiv:cs/0503007. Bibcode:2005IPM....41.1419B. doi:10.1016/j.ipm.2005.03.024. S2CID 9864663.
- Bollen, Johan (8 August 2018). "Who would you share your funding with?". Nature. 560 (7717): 143. Bibcode:2018Natur.560..143B. doi:10.1038/d41586-018-05887-3. PMID 30089925.