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From Social Media to Expert Reports: The Impact of Source Selection on Automatically Validating Complex Conceptual Models of Obesity

  • Mannila Sandhu
  • Philippe J. GiabbanelliEmail author
  • Vijay K. Mago
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11578)

Abstract

Models are predominantly developed using either quantitative data (e.g., for structured equation models) or qualitative data obtained through questionnaires designed by researchers (e.g., for fuzzy cognitive maps). The wide availability of social media data and advances in natural language processing raise the possibility of developing models from qualitative data naturally produced by users. This is of particular interest for public health surveillance and policymaking, as social media provide the opinions of constituents. In this paper, we contrast a model produced by social media with one produced via expert reports. We use the same process to derive a model in each case, thus focusing our analysis on the impact of source selection. We found that three expert reports were sufficient to touch on more aspects of a complex problem (measured by the number of relationships) than several million tweets. Consequently, developing a model exclusively from social media may lead to oversimplifying a problem. This may be avoided by complementing social media with expert reports. Alternatively, future research should explore whether a much larger volume of tweets would be needed, which also calls for improvements in scalable methods to transform qualitative data into models.

Keywords

Conceptual modeling Network analysis Social web mining Theme mining Twitter mining 

Notes

Acknowledgments

The authors are indebted to Mitacs Canada for providing the financial support which allowed MS to perform this research at Furman University, while mentored by PJG (local advisor) and VKM (home advisor). Publication costs are supported by an NSERC Discovery Grant for VKM. We thank Chetan Harichandra Mendhe for gathering the tweets under supervision of VKM.

Contributions. MS wrote the scripts to generate the results and analyzed them. PJG wrote the manuscript and designed the methods. MS was advised by PJG and VKM, who jointly initiated the study. All authors read and approved of this manuscript.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mannila Sandhu
    • 1
  • Philippe J. Giabbanelli
    • 2
    Email author
  • Vijay K. Mago
    • 1
  1. 1.Department of Computer ScienceLakehead UniversityThunder BayCanada
  2. 2.Computer Science DepartmentFurman UniversityGreenvilleUSA

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