| Abstrakt: |
With the proliferation of online technologies, social media recruitment has become an essential part of any company's outreach campaign. A social media platform can provide marketing posts with access to a large pool of candidates and at a low cost. It also provides the opportunity to quickly customize and refine messages in response to the reception. With online marketing, the key question is: which communities are attracted by recruitment tweets on social media? In this work, we profile the Twitter accounts that interact with a set of recruitment tweets by the U.S. Army's Recruitment Command through a network-centric perspective. By harnessing how users signal their affiliations through user information, we extract and analyze communities of social identities. From Social Identity Theory, these social identities can be critical drivers of behavior, like the decision to enlist in the military. With this framework, we evaluate the effectiveness of the U.S. Army's recruitment campaign on Twitter, observing that these campaigns typically attract communities with military exposure like veterans or those that identify with professional careers and fitness (e.g., student, professionals, athletes). The campaign also attracts, but at a much lower level, interaction from those in the digital industries—data scientists, cybersecurity professionals, and so forth. When analyzing the accounts in terms of their degree of automation, we find a set of intent-unknown bot accounts interacting with the tweets, and that many of the recruitment accounts are perceived as automated accounts. These observations can aid in campaign refinement: targeting the digital community and getting a broader reach for online recruitment publicity campaigns. [ABSTRACT FROM AUTHOR] |
