DOI

10.17077/etd.zm7ol4dj

Document Type

Dissertation

Date of Degree

Summer 2018

Degree Name

PhD (Doctor of Philosophy)

Degree In

Informatics

First Advisor

Ohlmann, Jeffrey W.

Second Advisor

Zhao, Kang

First Committee Member

Dalrymple, Kajsa

Second Committee Member

Srinivasan, Padmini

Third Committee Member

Street, Nick

Abstract

Social media has changed the way that we create, use, and disseminate information and presents an unparalleled opportunity to gather large-scale data on the networks, behaviors, and opinions of individuals. This dissertation focuses on the role of social media and social networks in recruitment, examining the complex interactions between offline recruiting activities, online social media, and recruiting outcomes. Specifically, it explores how the information college football recruits reveal about themselves online is related to their decisions as well as how this information can diffuse and influence the decisions of others.

Recruitment occurs in many contexts, and this research draws comparisons between college football and personnel recruiting. This work is one of the first large-scale studies of social media in college football recruiting, and uses a unique dataset that is both broad and deep, capturing information about 2,644 recruits, 682 schools, 764 coaches, and 2,397 current college football players and tracking offline and online behavior over six months. This dissertation comprises three case studies corresponding to the major decisions in the football recruiting cycle—the coach’s decision to make a scholarship offer, the athlete’s decision to commit, and the athlete’s decision to decommit.

The first study investigates the relationship between a recruit’s social media use and his recruiting success. Informed by previous work on impression management in personnel recruitment, I construct logistic classifiers to identify self-promotion and ingratiation in 5.5 million tweets and use regression analysis to model the relationship between tweets and scholarship offers over time. The results indicate that tweet content predicts whether an athlete will receive a new offer in the next month. Furthermore, the level of Twitter activity is strongly related to recruiting success, suggesting that simply possessing a social media account may offer a significant advantage in terms of attracting coaches’ attention and earning scholarship offers. These findings underscore the critical role of social media in athletic recruitment and may benefit recruits by informing their branding and communication strategies.

The second study examines whether a recruit’s social media activity presages his college preferences. I combine data on recruits’ college options, recruiting activities, Twitter connections, and Twitter content to construct a logistic classifier predicting which school a recruit will select out of those that have offered him a scholarship. My results highlight the value of social media data—especially the hashtags posted by the athlete and his online social network connections—for predicting his commitment decision. These findings may prove useful for college coaches seeking innovative methods to compete for elite talent, as well as assisting them in allocating recruiting resources.

The third study focuses on athletic turnover, i.e., decommitments. I construct a logistic classifier to predict the occurrence of decommitments over time based on recruits’ college choices, recruiting activities, online social networks, and the decommitment behavior of their peers. The results further underscore the power of online social networks for predicting offline recruiting outcomes, giving coaches the tools to better identify vulnerable commitments.

Keywords

College football, Predictive analytics, Recruitment, Social media, Social networks, Twitter

Pages

ix, 147 pages

Bibliography

Includes bibliographical references (pages 126-147).

Copyright

Copyright © 2018 Kristina Gavin Bigsby

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