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UP 2017 : Following User Pathways: Cross Platform and Mixed Methods Analysis in Social Media Studies

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Submission Deadline Feb 1, 2017
Notification Due Apr 28, 2017
Final Version Due Jul 15, 2017
Categories    social media   mixed method analysis   big social data   cross platform
 

Call For Papers

Special Issue: Following User Pathways in the International Journal of Human Computer Interaction

Call for Papers
Social media and the resulting tidal wave of available data have changed how researchers analyze communities at scale. However, the full potential for science has not yet been achieved, despite the popularity of social media analysis in the past decade. To date, few researchers invest in cross-platform analyses due to various reasons, e.g. variations in curating data, disparate methods and tools, complexity of cross-platform and mixed method analysis, methodological conflicts arising from mixed method studies, etc.
“Following user pathways: Using cross platform and mixed methods analysis in social media studies” brings together a community of researchers and professionals to address methodological, analytical, conceptual, and technological challenges and opportunities of mapping user across platforms with mixed method analysis in social media ecosystems.
This SI specifically focuses on interdisciplinary submissions, and an emphasis will be placed on submissions that cross online social media platforms and apply mixed method approaches. The topics of interests for this special issue include, but are not limited to:
• Motivational analysis across different Social Media platforms
• Multi-dimensional Representations of Person, Event and Society on Social Media
• Discrepancies in Representation of Events Across Platforms
• Barriers to Multi-platform and Mixed Methods analysis
• Ethical, Legal, and Social Implications, especially privacy issues
• Technical and Implementation Aspects of Multi-platform analysis
• Experiences and best practices in interdisciplinary multi-platform projects
• Mixed-method Approaches to Social Media Path Mapping
• Addressing Bias in Social Media Studies

Aims and Scope
This special issue will provide a systematic overview and state-of-the-art research in the field of cross platform, mixed method analysis in Online Social Media (OSM) studies, outlining new developments in fundamental, approaches, methodologies, and applications in this area. Despite the popularity of social media analysis in the past decade, few researchers invest in cross-platform analyses. This is a major oversight as over 42% of Online Social Media users have multiple social media accounts (Duggan, Ellison, Lampe, Lenhart, & Madden, 2014). The current maturity level of social media and social network research is lower than its potential due to this oversight.
In order to facilitate more realistic analyses, social models, and theories, researchers need to approach social media as a holistic ecosystem: the scientific community must map user pathways to match users’ activities. Key contribution differences are the observation viewpoint and elicitation of points of reference by analyzing multiple platforms. Whilst the scientific value of single platform studies is significant, their isolated investigations only give us insights into well-grounded research processes rather than assisting in the construction of a general approach.
This special issue aims to consolidate diverse research practices and methodologies of social media analysis into a structured and unified vision for user experience, HCI research, and an overarching understanding and observation point of digital studies.

A Unified Vision for Cross-platform, Mixed-method Analyses
The special issue will further support the creation of a unified vision and structured approach for multi-modal and mixed method social media research based off the foundational workshop co-located at ACM Factors in Human Computing (CHI) in 2016 (Hall et al., 2016). Submitted papers should address at least one of the following points:
1. How can a complete social media path be mapped, and what does a complete representation look like?
This overarching question looks for framing and conceptual modelling of complete user pathways. Especially the qualitative aspects of user motivation and needs, and the quantitative aspect of instantiation design are captured here.
2. What technical affordances need to be implemented to develop a single framework?
Few instantiations exist that support cross-platform data extraction. Even fewer exist that support for mixed-method analyses. This question looks for contributions on integrated and automated cross-platform and mixed-method analyses.
3. What impact does cross-platform, mixed method analysis have on research method bias?
It is expected that the observation lenses across platforms and with the differing methods capture differing structural, content, and temporal aspects. This simultaneously delivers the chance to increase or decrease implicit bias in research designs (González-Bailón, Wang, Rivero, & Borge-Holthoefer, 2014; Ruths & Pfeffer, 2014). Theoretical and empirical contributions addressing these challenges are envisioned with this question.
4. What are the ethical parameters of path mapping to avoid exploitative conduct?
Necessary to note is that ethical data curation follows the Belmont Principles and/or the guidelines of the Association of Internet researchers (Grimmelmann, 2015; Markham & Buchanan, 2012). The study design and curation must be reasonable, non-exploitative, and balance data extraction with benefit to society. These questions address the broad ethical issues in internet and cross-platform research.

Submission Instructions
This special issue solicits original work not under consideration for publication in any other conference or journal. We encourage interested authors to contact the guest editor Dr. Margeret Hall hall@kit.edu to discuss specific topics by 19 December 2017. Papers will be subject to a strict review procedure for final selection to this special issue. Submissions that extend previous workshop/conference papers must contain at least 40% new material relative to the conference paper, and note in the submission letter where these differences lie. Submissions will be reviewed by at least three reviewers. All submissions are expected to follow the formal guidelines of IJHCI: http://www.tandfonline.com/action/authorSubmission?journalCode=hihc20&page=instructions

Important Dates
Abstract Submissions (optional): 19 December 2016
Submission of papers: 1 February 2017
Notification of review results: 28 April 2017
Submission of revised papers: 15 July 2017
Notification of final review results: 15 August 2017
Expected Publication: Q1 2018

References
Duggan, M., Ellison, N., Lampe, C., Lenhart, A., & Madden, M. (2014). Pew Social Media Report 2015. Retrieved from http://www.pewinternet.org/2015/01/09/social-media-update-2014/
González-Bailón, S., Wang, N., Rivero, A., & Borge-Holthoefer, J. (2014). Assessing the bias in samples of large online networks. Social Networks, 38(January), 16–27. http://doi.org/10.1016/j.socnet.2014.01.004
Gosling, S. D., & Mason, W. (2015). Internet Research in Psychology. Annual Review of Psychology, 66(1), 877–902. http://doi.org/10.1146/annurev-psych-010814-015321
Grimmelmann, J. (2015). The Law and Ethics of Experiments on Social Media Users. Colorado Technology Law Journal, 13(219), 219–272.
Hall, M., Mazarakis, A., Peters, I., Chorley, M., Caton, S., Mai, J.-E., & Strohmaier, M. (2016). Following User Pathways : Cross Platform and Mixed Methods Analysis. In CHI’16 Extended Abstracts. San Jose, USA: ACM Press. http://doi.org/http://dx.doi.org/10.1145/2851581.2856500
Markham, A., & Buchanan, E. (2012). Ethical Decision-Making and Internet Research Recommendations from the AoIR Ethics Working Committee. Recommendations from the AoIR Ethics Working Committee (Version 2.0). Chicago, IL: Association of Internet Researchers. Retrieved from http://www.aoir.org/documents/ethics-guide
Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064. http://doi.org/10.1126/science.346.6213.1063


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