#ILookLikeAnEngineer: Using Social Media Based Hashtag Activism Campaigns as a Lens to Better Understand Engineering Diversity Issues

Each year, significant investment of time and resources is made to improve diversity within engineering across a range of federal and state agencies, private/not-for-profit organizations, and foundations. In spite of decades of investments, efforts have not yielded desired returns - participation by minorities continues to lag at a time when STEM workforce requirements are increasing. In recent years a new stream of data has emerged - online social networks, including Twitter, Facebook, and Instagram - that act as a key sensor of social behavior and attitudes of the public. Almost 87% of the American population now participates in some form of social media activity. Consequently, social networking sites have become powerful indicators of social action and social media data has shown significant promise for studying many issues including public health communication, political campaign, humanitarian crisis, and, activism. We argue that social media data can likewise be leveraged to better understand and improve engineering diversity. As a case study to illustrate the viability of the approach, we present findings from a campaign, #ILookLikeAnEngineer (using Twitter data - 19,354 original tweets and 29,529 retweets), aimed at increasing gender diversity in the engineering workplace. The campaign provided a continuous momentum to the overall effort to increase diversity and novel ways of connecting with relevant audience. Our analysis demonstrates that diversity initiatives related to STEM attract voices from various entities including individuals, large corporations, media outlets, and community interest groups.

[1]  Tim Highfield Social Media and Everyday Politics , 2016 .

[2]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[3]  Heather B. Gonzalez,et al.  Science, Technology, Engineering, and Mathematics (STEM) Education: A Primer [August 1, 2012] , 2012 .

[4]  Maximillian Hänska Ahy,et al.  Networked communication and the Arab Spring: Linking broadcast and social media , 2016, New Media Soc..

[5]  Amandeep Dhir,et al.  Uses and Gratifications of digital photo sharing on Facebook , 2016, Telematics Informatics.

[6]  Amy L Kotsenas,et al.  The Strategic Imperative for the Use of Social Media in Health Care. , 2018, Journal of the American College of Radiology : JACR.

[7]  Christian Reuter,et al.  Retrospective Review and Future Directions for Crisis Informatics , 2021, Information Refinement Technologies for Crisis Informatics.

[8]  Benyuan Liu,et al.  Predicting Flu Trends using Twitter data , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[9]  M. Sanders STEM, STEM Education, STEMmania , 2009 .

[10]  Aditya Johri,et al.  How Diverse Users and Activities Trigger Connective Action via Social Media: Lessons from the Twitter Hashtag Campaign #ILookLikeAnEngineer , 2018, HICSS.

[11]  Amit P. Sheth,et al.  Finding Influential Authors in Brand-Page Communities , 2012, ICWSM.

[12]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[13]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[14]  Aditya Johri,et al.  Real-Time Inference of User Types to Assist with more Inclusive and Diverse Social Media Activism Campaigns , 2018, AIES.

[15]  박경숙,et al.  Engineering Design: A Facilitator for Science, Technology, Engineering, and Mathematics [STEM] Education , 2009 .

[16]  Ana-Maria Popescu,et al.  A Machine Learning Approach to Twitter User Classification , 2011, ICWSM.