The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A familiar face(book): profile elements as signals in an online social network
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
How and why people Twitter: the role that micro-blogging plays in informal communication at work
Proceedings of the ACM 2009 international conference on Supporting group work
Is it really about me?: message content in social awareness streams
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
A case study of micro-blogging in the enterprise: use, value, and related issues
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Structural Predictors of Tie Formation in Twitter: Transitivity and Mutuality
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Network properties and social sharing of emotions in social awareness streams
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Social capital on facebook: differentiating uses and users
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fragile online relationship: a first look at unfollow dynamics in twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The impact of network structure on breaking ties in online social networks: unfollowing on twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting tie strength in a new medium
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Twitter and the development of an audience: those who stay on topic thrive!
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Determining credibility from social network structure
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Follow the green: growth and dynamics in twitter follower markets
Proceedings of the 2013 conference on Internet measurement conference
Specialization, homophily, and gender in a social curation site: findings from pinterest
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
The bursty dynamics of the Twitter information network
Proceedings of the 23rd international conference on World wide web
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Follower count is important to Twitter users: it can indicate popularity and prestige. Yet, holistically, little is understood about what factors -- like social behavior, message content, and network structure - lead to more followers. Such information could help technologists design and build tools that help users grow their audiences. In this paper, we study 507 Twitter users and a half-million of their tweets over 15 months. Marrying a longitudinal approach with a negative binomial auto-regression model, we find that variables for message content, social behavior, and network structure should be given equal consideration when predicting link formations on Twitter. To our knowledge, this is the first longitudinal study of follow predictors, and the first to show that the relative contributions of social behavior and mes-sage content are just as impactful as factors related to social network structure for predicting growth of online social networks. We conclude with practical and theoretical implications for designing social media technologies.