This special issue solicits papers presenting innovative research at the nexus of text mining and social network analysis. Topics of interest include (but are not limited to) the following:
- Content and discourse analysis
- Text mining and natural language processing
- Statistical analysis of textual data
- Study of co-occurrence networks of words
- The impact of language use on social networks
- Big Data analytics of large text corpora
- Extraction of socio-semantic networks from natural language text data
The above-mentioned topics should always be combined with the study of social networks: intended either as networks of people, groups and organizations, or as network of words, concepts and topics. Work at the intersection of these fields lags behind in theoretical, empirical, and methodological foundations.
The papers published in this issue will present either new methodologies or useful applications in business. They will extend prior research which already demonstrated the advantages of combining social network and semantic analysis for business intelligence (e.g., Aswani, Kar, Ilavarasan, & Dwivedi, 2018; Fronzetti Colladon & Gloor, 2018; Fronzetti Colladon & Vagaggini, 2017; Gloor, Fronzetti Colladon, Grippa, & Giacomelli, 2017). For example, actions to support successful interactions with clients or to improve employee communication come from a better understanding of the impact that language use has within and across organizations (Diesner, Frantz, & Carley, 2005; Fronzetti Colladon, Saint-Charles, & Mongeau, 2018; Gloor, Fronzetti Colladon, Giacomelli, Saran, & Grippa, 2017). The study of networks of words, on the other hand, supports brand management in the era of big data (Fronzetti Colladon, 2018). Analogously, text mining helps brand managers identify (virtual) consumer tribes (Gloor, Fronzetti Colladon, de Oliveira, & Rovelli, 2018) and develop customized marketing strategies (He, Zha, & Li, 2013).
Additionally, a limited number of papers reviewing the state of the art in semantic social network analysis will be considered, if they discuss business applications. Papers about methodological issues and different approaches arising when extracting networks from textual data (Diesner, 2013), or when combining social networks with discourse and semantic analysis (Gloor, 2017; Saint-Charles & Mongeau, 2018), are also invited.
Manuscript submission deadline: 31-Dec-2018
Notification of Review: 30-Mar-2019
Revision due: 31-May-2019
Notification of 2nd Review: 1-July-2019
2nd Revision [if needed] due: 1-Sep-2019
Notification of Final Acceptance: 30-Sep-2019
Expected Publication: TBA
All submissions have to be prepared according to the Guide for Authors as published in the Journal website at: https://www.elsevier.com/journals/international-journal-of-information-management/0268-4012/guide-for-authors
Authors should select “SI: SNA & Text Mining”, from the “Choose Article Type” pull- down menu during the submission process. All contributions must not have been previously published or be under consideration for publication elsewhere. Link for submission of manuscript is https://www.evise.com/evise/jrnl/IJIM
A submission based on one or more papers that appeared elsewhere has to comprise major value-added extensions over what appeared previously (at least 50% new material). Authors are requested to attach to the submitted paper their relevant, previously published articles and a summary document explaining the enhancements made in the journal version.
All submitted papers will undergo a rigorous peer-review process that will consider programmatic relevance, scientific quality, significance, originality, style and clarity.
The acceptance process will focus on papers that address original contributions in the form of theoretical, empirical and case research, which lead to new perspectives on Combining Social Network Analysis and Text Mining. Papers must be grounded on the body of scholarly works in this area (exemplified by some of the references below) but yet discover new frontiers so that collectively, the Special Section will serve communities of researchers and practitioners as an archival repository of the state of the art in Text Mining combined with Social Network Analysis.
Andrea Fronzetti Colladon
University of Rome Tor Vergata
Center for Collective Intelligence
Massachusetts Institute of Technology
Cambridge, MA, United States
Domenica Fioredistella Iezzi
University of Rome Tor Vergata
Aswani, R., Kar, A. K., Ilavarasan, P. V., & Dwivedi, Y. K. (2018). Search engine marketing is not all gold: Insights from Twitter and SEOClerks. International Journal of Information Management, 38(1), 107–116. https://doi.org/10.1016/j.ijinfomgt.2017.07.005
Diesner, J. (2013). From Texts to Networks: Detecting and Managing the Impact of Methodological Choices for Extracting Network Data from Text Data. KI - Künstliche Intelligenz, 27(1), 75–78. https://doi.org/10.1007/s13218-012-0225-0
Diesner, J., Frantz, T. L., & Carley, K. M. (2005). Communication networks from the Enron email corpus “It’s always about the people. Enron is no different.” Computational and Mathematical Organization Theory, 11(3), 201–228. https://doi.org/10.1007/s10588-005-5377-0
Fronzetti Colladon, A. (2018). The Semantic Brand Score. Journal of Business Research, 88, 150–160. https://doi.org/10.1016/j.jbusres.2018.03.026
Fronzetti Colladon, A., & Gloor, P. A. (2018). Measuring the impact of spammers on e-mail and Twitter networks. International Journal of Information Management, in press. https://doi.org/10.1016/j.ijinfomgt.2018.09.009
Fronzetti Colladon, A., Saint-Charles, J., & Mongeau, P. (2018). The impact of language homophily and similarity of social position on employees’ digital communication. In D. F. Iezzi, L. Celardo, & M. Misuraca (Eds.), JADT’ 18. Proceedings of the 14th International Conference on Statistical Analysis of Textual Data (pp. 293–297). Rome, Italy: Universitalia.
Fronzetti Colladon, A., & Vagaggini, F. (2017). Robustness and stability of enterprise intranet social networks: The impact of moderators. Information Processing & Management, 53(6), 1287–1298. https://doi.org/10.1016/j.ipm.2017.07.001
Gloor, P. A. (2017). Sociometrics and Human Relationships: Analyzing Social Networks to Manage Brands, Predict Trends, and Improve Organizational Performance. London, UK: Emerald Publishing Limited.
Gloor, P. A., Fronzetti Colladon, A., de Oliveira, J. M., & Rovelli, P. (2018). Identifying Tribes on Twitter through Shared Context. In 8th International Conference on Collaborative Innovation Networks COINs 18, forthcoming. Suzhou, China.
Gloor, P. A., Fronzetti Colladon, A., Giacomelli, G., Saran, T., & Grippa, F. (2017). The Impact of Virtual Mirroring on Customer Satisfaction. Journal of Business Research, 75, 67–76. https://doi.org/10.1016/j.jbusres.2017.02.010
Gloor, P. A., Fronzetti Colladon, A., Grippa, F., & Giacomelli, G. (2017). Forecasting managerial turnover through e-mail based social network analysis. Computers in Human Behavior, 71, 343–352. https://doi.org/10.1016/j.chb.2017.02.017
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464–472. https://doi.org/10.1016/j.ijinfomgt.2013.01.001
Saint-Charles, J., & Mongeau, P. (2018). Social influence and discourse similarity networks in workgroups. Social Networks, 52, 228–237. https://doi.org/10.1016/j.socnet.2017.09.001