Big Social Data Analytics

Schedule and location

Tuesday May 23th –  Wednesday May 24th  

Tampere University of Technology, Tampere (visiting address: Korkeakoulunkatu 10)

Tue 23.5: Festia Building, Kappelisali FA128

Wed 24.5: TUT Central Building, Kampusklubi, 5th Floor



Registration is open May 5th - May 16th.


Prof. Ravi Vatrapu, Centre for Business Data Analytics, Copenhagen Business School, Denmark

Dr. Raghava Mukkamala, Centre for Business Data Analytics, Copenhagen Business School, Denmark

Dr. Jari Jussila, DARE Business Data Research Group, Lab. of Industrial and Information Management, Tampere University of Technology, Finland


Professor Hannu Kärkkäinen, DARE Business Data Research Group, Laboratory of Industrial and Information Management, Tampere University of Technology, Finland.


 This course is designed to provide knowledge of key concepts in and methods of big social data analytics from a computational social science perspective.  Course contents will cover issues in and aspects of manipulating, storing, and analysing big social data in order to create organizational and societal value. Especially, the course will cover four main analyses themes: Visual Analytics, Social Network Analysis, Text Analytics and Predictive Analytics under the topic of Big Social Data Analytics.

In addition to the above, the course will also cover the new emerging approach Social Set Analysis (SSA) based on phenomenological sociology, ecological psychology and set theory for a class of problems in social media campaigns in crowdfunding platforms and knowledge ecosystems. The course will involve hands-on demos and tutorials with data as well as discussion of conceptual problems and methodological aspects


Detailed Program 


1st Day (Tuesday, 23-05-2017)

Location: Festia kappelisali FA128 (TUT)





09:00 – 10:30

Logical Fallacies with Big Social Data Analytics


A Generative Framework for Philosophies of Computational Social Science


Set-Theoretical Computational Social Science: Social Set Analysis (SSA)

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., . . . Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721-723. doi:DOI 10.1126/science.1167742


Cioffi-Revilla, C. (2013). Introduction to Computational Social Science: Principles and Applications: Springer Science & Business Media.


Vatrapu, R., Mukkamala, R. R., Hussain, A., & Flesch, B. (2016). Social Set Analysis: A Set Theoretical Approach to Big Data Analytics. IEEE Access, 4, 2542-2571. doi:10.1109/ACCESS.2016.2559584

10:30 - 10:45



Theme: Visual Analytics

10:45 – 12:00

Fundamentals of Visualization and Graphics






Visual Analytics of Big Social Data

Ware, C. (2013). Information visualization: perception for design (Third ed.): Elsevier.


Executive Summary Thomas, J. J., & Cook, K. A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press.


Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67.


Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.


Chapter 27 of Munzner, T. (2009). Visualization. Fundamentals of Graphics, Third Edition. AK Peters, 675-707.


Pantazos, K., Lauesen, S., & Vatrapu, R. (2013). End-User Development of Information Visualization. In Y. Dittrich, M. Burnett, A. Mørch, & D. Redmiles (Eds.), End-User Development (Vol. 7897, pp. 104-119): Springer Berlin Heidelberg.


Flesch, B., Vatrapu, R., Mukkamala, R. R., & Hussain, A. (2015). Social set visualizer: A set theoretical approach to big social data analytics of real-world events. Paper presented at the 2015 IEEE International Conference on Big Data (IEEE Big Data).

12:00 - 13:00

Lunch Break


Theme: Social Network Analysis

13:00 - 14:30

Introduction to Social Networks Analysis and Network Measures, Twitter data collection and Network Visualisation with Gephi


Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.
Huhtamäki, J. (2016). Ostinato Process Model for Visual Network Analytics: Experiments in Innovation Ecosystems. (Tampere University of Technology. Publication; Vol. 1425). Tampere University of Technology.
Aramo-Immonen, H., Kärkkäinen, H., Jussila, J. J., Joel-Edgar, S., & Huhtamäki, J. (2016). Visualizing informal learning behavior from conference participants' Twitter data with the Ostinato Model. Computers in Human Behavior, 55, 584-595. 
Huhtamäki, J., Still, K., Isomursu, M., Russell, M., & Rubens, N. (2012, September). Networks of Growth: The Case of Young Innovative Companies in Finland. In Proceedings of the 7th European Conference on Innovation and Entrepreneurship: ECIE (p. 307). Academic Conferences Limited.

14:30 - 14:45



14:45 - 16:00

Team Assignment: Extraction of Twitter data and Network Visualisation with Gephi


Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. ICWSM, 8, 361-362.
Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one, 9(6), e98679. 
McSweeney, P. J. (2009). Gephi Network Statistics. Presentado en Google Summer of Code. Recuperado a partir de http://gephi. org/google-soc/gephi-netalgo. pdf. 
Huhtamäki, J., & Parviainen, O. (2013). Verkostoanalyysi sosiaalisen median tutkimuksessa. Otteita verkosta-Verkon ja sosiaalisen median tutkimusmenetelmät. Vastapaino, Tampere. 





2nd Day (Wednesday, 24-05-2017)

Location: Kampusklubi, TUT Central Building, 5th Floor (TUT)





Theme: Text Analytics

09:00 – 10:30

Introduction to Natural Language Processing (NLP)


Tokenization, Stemming, Lemmatization, Parts of Speech Tagging


Topic Modelling using Latent Dirichlet allocation (LDA)


Text Classification using Naïve Bayes Algorithm


Naive   Bayes   and   Sentiment



Aggarwal, C. C., & Zhai, C. (2012). Mining text data: Springer Science & Business Media.


Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: O'Reilly Media, Inc.

10:30 - 10:45



10:45 – 12:00

Hand-on Exercises on NLP tasks using NLTK and TextBlob Python Libraries


12:00 - 13:00

Lunch Break


Theme: Predictive Analysis

13:00 - 14:30

Fundamentals of Predictive Analytics


Predictive Analytics with Big Social Data

Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice: OTexts:


Buus Lassen, N., la Cour, L., & Vatrapu, R. (2017). Predictive Analytics with Social Media Data. In L. Sloan, & A. Quan-Haase (Eds.), The SAGE Handbook of Social Media Research Methods (Chapter 20, pp. 328-341). London: Sage Publications, Incorporated.

14:30 - 14:45



14:45 - 16:00

Future of Big Social Data Analytics: Conceptual Challenges, Technical Possibilities, Practical Applications and Ethical Issues


Credit points

Doctoral students participating in the seminar can obtain 2 credit points. This requires participating on both days and carrying out and returning the requested assignments.

Registration fee

This seminar is free-of-charge for member organization's staff and their PhD students. For others the participation fee is 400 €. The participation fee includes access to the event and the event materials. Lunch and dinner are not included.