4.3.2 Presentation of Results

Given the penchant among social network scholars for incorporating visual representation of data and findings into the presentation of their results, the potential risks that arise from how results are shared must be carefully considered. A clear element of these considerations lies in the potential identification of members of a study population (for example, the Facebook described above and in Zimmer 2010)—and whether that identification could violate confidentiality agreements (especially when research participants were actively involved in data collection) or expectations of privacy (particularly for passive data). These potential de-anonymization or deductive disclosure risks must be considered when strategizing how to present research results (e.g., what level of detail is appropriate in publication tables and figures), how to safely share data (e.g., how to develop terms of use, and what must be considered PII that should not be shared), and how to store data (e.g., what level of data encryption is appropriate).

Beyond the research applications, scholars are also increasingly sharing the results of their research with the participants who are represented in the data (Ferris and Sass-Kortsak 2011). This has been raised as a potential benefit to the community being studied, as the analyses conducted can often reveal information about the population that is not readily apparent (Breiger 2005). While these aims are laudable, and increasingly accepted by a wider swath of the research community,88 they do not come without ethical challenges (Delva, Leventhal, and Helleringer 2016).

For example, in the context of complete network research, intraorganizational studies that provide expectations of reasonably bounded populations are still more common within social network scholarship than many other fields. In such cases, the concerns that arise from potentially sharing research results within the study population are especially important. While studies’ implications may not raise to the level of life-or-death consequences mentioned above, researchers must still remain vigilant about how their work should optimally retain protections from risks for their research subjects. If consulting for business applications, research outcomes can often be focused on aspects directly related to a company’s bottom line. For example, if research identifies communication bottle-necks or structural redundancies that reduce efficiency, the individuals in those positions could be at risk of ramifications including job loss. Researchers who commonly work in such contexts have therefore developed strategies to protect participants from such harms (Borgatti and Molina 2005).

Maintaining the protections promised by informed consent, voluntary participation, and maintaining confidentiality; minimally means limiting the level at which results will be presented. For example network metrics and or visualizations may be restricted to presentation only at aggregate levels so that individuals cannot be identified at all, or with added noise to obfuscate the accuracy of any such identifications (Borgatti and Molina 2003). Moreover, in the case of organization-based research, clear boundaries are necessary regarding who owns and has access to the raw data (Kadushin 2005). Several prominent scholars with experience working in such settings advocate explicit contracts that clearly delineate how these restrictions will be setup and put into practice, and develop standards of data ownership that allow the researchers to maintain control over these decisions (Kadushin 2012; Borgatti and Molina 2005).