We present a study that examines how a social media activism campaign aimed at improving gender diversity within engineering gained and maintained momentum in its early period. We examined over 50,000 Tweets posted over the first ~75 days of the #ILookLikeAnEngineer campaign and found that diverse participation -of types of users -increased activity at crucial moments. We categorize these triggers into four types: 1) Event-Driven: Alignment of the campaign with offline events related to the issue (Diversity SFO, Disrupt, etc.); 2) Media-Driven: News coverage of the events in the media (TechCrunch, CNN, BBC, etc.); 3) Industry-Driven: Web participation in the campaign by large organizations (Microsoft, Tesla, GE, Cisco, etc.); and 4) Personality-Driven: Alignment of the events with popular and/or known personalities (e.g. Isis Anchalee; Michelle Sun; Ada Lovelace.). This study illustrates how one mechanism -triggering -supports connective action in social media campaign.

Social media provides a mechanism for people to engage with social causes across a range of issues. It also provides a strategic tool to those looking to advance a cause to exchange, promote or publicize their ideas. In such instances, AI can be either an asset if used appropriately or a barrier. One of the key issues for a workforce diversity campaign is to understand in real-time who is participating -specifically, whether the participants are individuals or organizations, and in case of individuals, whether they are male or female. In this paper, we present a study to demonstrate a case for AI for social good that develops a model to infer in real-time the different user types participating in a cause-driven hashtag campaign on Twitter, ILookLikeAnEngineer (ILLAE). A generic framework is devised to classify a Twitter user into three classes: organization, male and female in a real-time manner. The framework is tested against two datasets (IL-LAE and a general dataset) and outperforms the baseline binary classifiers for categorizing organization/individual and male/female. The proposed model can be applied to future social cause-driven campaigns to get real-time insights on the macro-level social behavior of participants.

Although research on different hashtag activism campaigns abounds, no study has looked at how different affordances of social media support a single campaign. We use data from a hashtag activism campaign, #ILookLikeAnEngineer, launched to showcase diversity within engineering workforce, to examine how different elements of a campaign blend together. We specifically identify three distinct but interconnected ways in which social media supports activism: 1) modality — it allows users to participate through text, photos, and links; 2) messaging — it allows users to post and support multiple though related topics; and 3) actors — it provides a voice to different participants (individuals/organizations, men/women). Our analysis supports the idea that multivocality — the core idea that people leverage multiple ways of participating — is the key to campaign success. Our analysis of 19,492 original tweets and 89,650 retweets shows that multivocality allowed the campaign to receive support not just from individuals but from large corporations, media, and NGOs, who were able to share their perspective using their preferred modality giving rise to a new form of digital polyphonic narrative that supports their agenda.

His multidisciplinary academic and industry experience spans two key disciplines: Human-Computer Interaction and Social Media Communication and Analytics. He is currently engaged in a number of research projects funded by the National Science Foundation (NSF). In some of his recent projects he has applied big data techniques and tools to investigate the role of social media in engaging public and under-represented communities towards STEM education and informal learning.

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