Skip to main content
SpringerLink
Log in

Social studies of scholarly life with sensor-based ethnographic observations

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Social network analysis is playing an increasingly important role in sociological studies. At the same time, new technologies such as wearable sensors make it possible to collect new types of social network data. We employed RFID tags to capture face-to-face interactions of participants of two consecutive Ph.D. retreats of a graduate school on climate research. We use this data in order to explore how it may support ethnographic observations and to gain further insights on scholarly interactions. The unique feature of the data is the opportunity to distinguish short and long conversations, which often have a different nature from a sociological point of view. Furthermore, an advantage of this data is the availability of socio-demographic, research-related, and situational attributes of participants. We show that, even though an interaction partner is often found rather randomly during coffee breaks of retreats, a strong homophily between participants from the same institutions or research areas exists. We identify cores of the networks and participants who play ambassador roles between communities, e.g., persons who visit the retreat for the second time are more likely to be ambassadors. Overall, we show the usefulness and potential of RFID tags for scientometric studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. http://www.sociopatterns.org.

  2. In general, both RFID readers and servers can be connected to LAN, but for our experiments we had to set up our own infrastructure due to the lack of LAN.

References

  • Alani, H., Szomszor, M., Cattuto, C., Broeck, W., Correndo, G., & Barrat, A. (2009). Live social semantics. In Proceedings of the 8th international semantic web conference, ISWC’09 (pp. 698–714). Berlin: Springer.

  • Atzmueller, M. (2015). Subgroup Discovery. WIREs Data Mining and Knowledge Discovery, 5(1), 35–49.

    Article  Google Scholar 

  • Atzmueller, M. (2016). Detecting community patterns capturing exceptional link trails. In Proceedings of the IEEE/ACM ASONAM. Boston, MA, USA: IEEE Press.

  • Atzmueller, M. (2018). Compositional subgroup discovery on attributed social interaction networks. In Proceedings of the international conference on discovery science. Berlin, Germany: Springer.

  • Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., et al. (2014). Ubicon and its applications for ubiquitous social computing. New Review of Hypermedia and Multimedia, 20(1), 53–77.

    Article  Google Scholar 

  • Atzmueller, M., Doerfel, S., & Mitzlaff, F. (2016a). Description-oriented community detection using exhaustive subgroup discovery. Information Sciences, 329, 965–984.

    Article  Google Scholar 

  • Atzmueller, M., Fries, B., & Hayat, N. (2016b). Sensing, processing and analytics—Augmenting the ubicon platform for anticipatory ubiquitous computing. In Proceedings of the ACM conference on pervasive and ubiquitous computing adjunct publication, UbiComp’16 Adjunct. New York, NY, USA: ACM Press.

  • Atzmueller, M., & Lemmerich, F. (2018). Homophily at academic conferences. In Proceeding of the WWW 2018 (Companion). IW3C2/ACM.

  • Atzmueller, M., Soldano, H., Santini, G., & Bouthinon, D. (2018). MinerLSD: Efficient local pattern mining on attributed graphs. In Proceeding of the 2018 IEEE international conference on data mining workshops (ICDMW).

  • Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3), 590–614.

    Article  MathSciNet  MATH  Google Scholar 

  • Barrat, A., Cattuto, C., Szomszor, M., Broeck, W. V. D., & Alani, H. (2010). Social dynamics in conferences: Analyses of data from the live social semantics application. In The Semantic Web—ISWC 2010, Lecture Notes in Computer Science (pp. 17–33). Berlin: Springer.

  • Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.

    Article  Google Scholar 

  • Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25(2), 163–177.

    Article  MATH  Google Scholar 

  • Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D., & Mascolo, C. (2014). Tracking serendipitous interactions: How individual cultures shape the office. In Proceedings of the 17th ACM conference on computer supported cooperative work & social computing, CSCW’14 (pp. 1072–1081). New York, NY, USA: ACM.

  • Cattuto, C., Broeck, W. V. D., Barrat, A., Colizza, V., Pinton, J.-F., & Vespignani, A. (2010). Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS ONE, 5(7), e11596.

    Article  Google Scholar 

  • Domínguez, P. S., & Hollstein, D. B. (Eds.). (2014). Mixed methods social networks research: Design and applications (1st ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • Duivesteijn, W., & Knobbe, A. (2011). Exploiting false discoveries—Statistical validation of patterns and quality measures in subgroup discovery. In Proceedings of ICDM (pp. 151–160). IEEE.

  • Eberle, J., Stegmann, K., Fischer, F., Barrat, A., & Lund, K. (2017). Finding collaboration partners in a scientific community: The role of cognitive group awareness, career level, and disciplinary background collaboration and integration of newcomers in scientific communities. In The 12th international conference on computer supported collaborative learning, making a difference: Prioritizing equity and access in CSCL. 12th international conference on computer supported collaborative learning (pp. 519–526). Philadelphia, USA: International Society of the Learning Sciences.

  • Erdős, P. (1959). On random graphs I. Publicationes Mathematicae (Debrecen), 6, 290–297.

    MathSciNet  MATH  Google Scholar 

  • Erdős, P., & Rényi, A. (1960). On the evolution of random graphs. In Publication of the Mathematical Institute of the Hungarian Academy of Sciences (pp. 17–61).

  • Frank, A. M., Froese, R., Hof, B. C., Scheffold, M. I. E., Schreyer, F., Zeller, M., et al. (2017). Riding alone on the elevator. Learning and Teaching, 10(3), 1–19.

    Article  Google Scholar 

  • Frank, O. (1997). Composition and structure of social networks. Mathématiques et Sciences Humaines, Mathematics and Social Sciences, 137, 11–23.

    MathSciNet  MATH  Google Scholar 

  • Gionis, A., Mannila, H., Mielikäinen, T., & Tsaparas, P. (2007). Assessing data mining results via swap randomization. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(3), 14.

    Article  Google Scholar 

  • Goffman, E. (1989). On fieldwork. Journal of Contemporary Ethnography, 18(2), 123–132.

    Article  Google Scholar 

  • Görlich, M., & Rödder, S. (2017). Zwischen Lernort und Disputationsprobe. Eine empirische Untersuchung von Advisory Panel Meetings in einem strukturierten Promotionsprogramm in der Klimaforschung. In Geschlossene Gesellschaften - 38. Kongress der Deutschen Gesellschaft für Soziologie (Vol. 38).

  • Harris, J. K. (2013). An introduction to exponential random graph modeling (new ed.). Los Angeles: Sage Publications Inc.

  • Heiberger, R. H., & Riebling, J. R. (2016). Installing computational social science: Facing the challenges of new information and communication technologies in social science. Methodological Innovations, 9, 2059799115622763.

    Article  Google Scholar 

  • Interdonato, R., Atzmueller, M., Gaito, S., Kanawati, R., Largeron, C., & Sala, A. (2019). Feature-rich networks: Going beyond complex network topologies. Applied Network Science, 4, 4.

    Article  Google Scholar 

  • Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.-F., & Van den Broeck, W. (2011). What’s in a crowd? Analysis of face-to-face behavioral networks. Journal of Theoretical Biology, 271(1), 166–180.

    Article  MathSciNet  MATH  Google Scholar 

  • Kibanov, M., Atzmueller, M., Illig, J., Scholz, C., Barrat, A., Cattuto, C., et al. (2015). Is web content a good proxy for real-life interaction? A case study considering online and offline interactions of computer scientists. In 2015 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) (pp. 697–704).

  • Lau, D. C., & Murnighan, J. K. (1998). Demographic diversity and faultlines: The compositional dynamics of organizational groups. Academy of Management Review, 23(2), 325–340.

    Article  Google Scholar 

  • Leifeld, P., Cranmer, S. J., & Desmarais, B. A. (2018). Temporal exponential random graph models with btergm: Estimation and bootstrap confidence intervals. Journal of Statistical Software, 83(6).

  • Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319.

    Article  Google Scholar 

  • Macek, B.-E., Scholz, C., Atzmueller, M., & Stumme, G. (2012). Anatomy of a conference. In Proceedings of the 23rd ACM conference on hypertext and social media, HT’12 (pp. 245–254). New York, NY, USA: ACM.

  • Mastrandrea, R., & Barrat, A. (2016). How to estimate epidemic risk from incomplete contact diaries data? PLOS Computational Biology, 12(6), e1005002.

    Article  Google Scholar 

  • Mastrandrea, R., Fournet, J., & Barrat, A. (2015). Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLoS ONE, 10(9), e0136497.

    Article  Google Scholar 

  • McPherson, J. M., & Smith-Lovin, L. (1987). Homophily in voluntary organizations: Status distance and the composition of face-to-face groups. American Sociological Review, 52(3), 370–379.

    Article  Google Scholar 

  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.

    Article  Google Scholar 

  • Merton, R. K. (1968). The matthew effect in science: The reward and communication systems of science are considered. Science, 159(3810), 56–63.

    Article  Google Scholar 

  • Merton, R. K. (1942). Science and technology in a democratic order. Journal of Legal and Political Sociology, 1(1/2), 115–126.

    Google Scholar 

  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.

    Article  MathSciNet  MATH  Google Scholar 

  • Newman, M. E. J. (2004). Detecting community structure in networks. The European Physical Journal B, 38, 321–330.

    Article  Google Scholar 

  • Olguin, D., & Pentland, A. (2010). Sensor-based organisational design and engineering. International Journal of Organisational Design and Engineering, 1(1/2), 69.

    Article  Google Scholar 

  • Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., et al. (2007). Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences, 104(18), 7332–7336.

    Article  Google Scholar 

  • Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173–191.

    Article  Google Scholar 

  • Scholz, C., Atzmueller, M., Barrat, A., Cattuto, C., & Stumme, G. (2013a). New insights and methods for predicting face-to-face contacts. In Seventh international AAAI conference on weblogs and social media.

  • Scholz, C., Atzmueller, M., Kibanov, M., & Stumme, G. (2013b). How do people link? Analysis of contact structures in human face-to-face proximity networks. In 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2013) (pp. 356–363).

  • Scholz, C., Atzmueller, M., & Stumme G. (2012). On the predictability of human contacts: Influence factors and the strength of stronger ties. In 2012 International conference on privacy, security, risk and trust and 2012 international conference on social computing (pp. 312–321).

  • Scripps, J., Tan, P. N., & Esfahanian, A. H. (2007). Exploration of link structure and community-based node roles in network analysis. In Seventh IEEE international conference on data mining (ICDM 2007) (pp. 649–654).

  • Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591–611.

    Article  MathSciNet  MATH  Google Scholar 

  • Singer, P., Helic, D., Hotho, A., & Strohmaier, M. (2015). Hyptrails: A bayesian approach for comparing hypotheses about human trails. In Proceedings of WWW New York, NY, USA: ACM.

  • Smieszek, T., Barclay, V. C., Seeni, I., Rainey, J. J., Gao, H., Uzicanin, A., et al. (2014). How should social mixing be measured: Comparing web-based survey and sensor-based methods. BMC Infectious Diseases, 14, 136.

    Article  Google Scholar 

  • Smieszek, T., Castell, S., Barrat, A., Cattuto, C., White, P. J., & Krause, G. (2016). Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: Method comparison and participants’ attitudes. BMC Infectious Diseases, 16, 341.

    Article  Google Scholar 

  • Sood, S. K., & Mahajan, I. (2017). Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus. Computers in Industry, 91, 33–44.

    Article  Google Scholar 

  • Sood, S. K., & Mahajan, I. (2018). Fog-cloud based cyber-physical system for distinguishing, detecting and preventing mosquito borne diseases. Future Generation Computer Systems, 88, 764–775.

    Article  Google Scholar 

  • Stehlé, J., Charbonnier, F., Picard, T., Cattuto, C., & Barrat, A. (2013). Gender homophily from spatial behavior in a primary school: A sociometric study. Social Networks, 35(4), 604–613.

    Article  Google Scholar 

  • Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J.-F., et al. (2011). High-resolution measurements of face-to-face contact patterns in a primary school. PLoS ONE, 6(8), e23176.

    Article  Google Scholar 

  • Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468–472.

    Article  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (1st ed.). Cambridge: Cambridge University Press. (Number 8 in Structural analysis in the social sciences).

    Book  MATH  Google Scholar 

  • Watts, D. J. (2004). The “new” science of networks. Annual Review of Sociology, 30(1), 243–270.

    Article  Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440.

    Article  MATH  Google Scholar 

  • Wu, L., Waber, B., Aral, S., Brynjolfsson, E., & Pentland, A. (2008). Mining face-to-face interaction networks using sociometric badges: Predicting productivity in an IT configuration task. In ICIS 2008 Proceedings.

  • Yin, Z., Gupta, M., Weninger, T., & Han, J. (2010). Linkrec: A unified framework for link recommendation with user attributes and graph structure. In Proceedings of the 19th international conference on world wide web, WWW’10 (pp. 1211–1212).

  • Zhou, Y., Cheng, H., & Yu, J. X. (2009). Graph clustering based on structural/attribute similarities. The Proceedings of the VLDB Endowment, 2(1), 718–729.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Christoph Scholz and Björn Fries for helping to collect RFID data during the retreats. This work has been partially supported by Germany’s Excellence Strategy (DFG EXC 177 CliSAP) and the German Research Foundation (DFG) project “MODUS” (Grant AT 88/4-1).

Author information

Authors and Affiliations

  1. Interdisciplinary Research Center for Information System Design (ITeG), Knowledge and Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany

    Mark Kibanov & Gerd Stumme

  2. Socium, University of Bremen, Mary-Somervile Str. 9, 28205, Bremen, Germany

    Raphael H. Heiberger

  3. Department of Social Sciences, University of Hamburg, Grindelberg 5, 20144, Hamburg, Germany

    Simone Rödder

  4. Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands

    Martin Atzmueller

Authors
  1. Mark Kibanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raphael H. Heiberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simone Rödder
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Atzmueller
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gerd Stumme
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark Kibanov.

Appendices

Appendix 1: Homophily

For the conference in 2015 we could only consider 2 days, i.e., can calculate a normal ERGM in which we consider the ties of the first day of the retreat. As we mentioned in “Data collection” Section, only few connections were captured during the last day of the 2015 retreat as same tags were put together and we had to remove some captured interactions as we could not distinguish between real interactions and tags that were just lying near each other. Therefore, the inversion of the time effect could not be reproduced since only few ties are overlapping in conference day 1 and day 2. We find no gender effect in the 2015 conference, even for longer interactions. All other results in Table 8 support the findings for the 2014 conference. The same is true given the stability of effects considering longer interactions (Fig. 15).

Table 8 Results of exponential random graph models for the convent in 2015, all interactions
Full size table
Fig. 15
figure 15

Goodness-of-fit measures for different network properties of TERGM models, all interactions. Box plots represent simulated estimates, solid lines empirical values. The closer lines are to estimates’ means and their 95% confidence intervals (i.e., within whiskers), the better the fit of the model for the respective value. a Gives goodness-of-fit for the full model C of the TERGM for the convent in 2014, b for the full model B of the ERGM for 2015

Full size image

Appendix 2: Structure of cores of the networks

See Fig. 16.

Fig. 16
figure 16

Properties of members of maximal connected component dependent on threshold

Full size image

Appendix 3: Roles of participants

Fig. 17
figure 17

Roles of participants of 2014 retreat depending on their gender related to different types of communities

Full size image
Fig. 18
figure 18

Roles of participants of 2014 retreat depending on their country of birth related to different types of communities

Full size image
Fig. 19
figure 19

Roles of participants of 2014 retreat depending on their status related to different types of communities

Full size image
Fig. 20
figure 20

Roles of participants of 2014 retreat depending on their affiliation with the host university related to different types of communities

Full size image
Fig. 21
figure 21

Roles of participants of 2015 retreat depending on their gender related to different types of communities

Full size image
Fig. 22
figure 22

Roles of participants of 2015 retreat depending on their country of birth related to different types of communities

Full size image
Fig. 23
figure 23

Roles of participants of 2015 retreat depending on their academic status related to different types of communities

Full size image
Fig. 24
figure 24

Roles of participants of 2015 retreat depending on their affiliation with the university of Hamburg related to different types of communities

Full size image
Fig. 25
figure 25

Roles of participants of 2015 retreat depending on whether they visited retreat 2014 related to different types of communities

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kibanov, M., Heiberger, R.H., Rödder, S. et al. Social studies of scholarly life with sensor-based ethnographic observations. Scientometrics 119, 1387–1428 (2019). https://doi.org/10.1007/s11192-019-03097-w

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-019-03097-w

Keywords

Search

Navigation