We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. We start with a machine learning problem of predicting a user's preference among two alternative choices of Twitter friends. We show that co-following information provides strong signals for diverse classification tasks and that these signals persist even when (i) the most discriminative features are removed and (ii) only relatively "sparse" users with fewer than 152 but more than 43 Twitter friends are considered. Going beyond mere classification performance optimization, we present applications of our methodology to Computational Social Science. Here we confirm stereotypes such as that the country singer Kenny Chesney (@kennychesney) is more popular among @GOP followers, whereas Lady Gaga (@ladygaga) enjoys more support from @TheDemocrats followers. In the domain of marketing we give evidence that celebrity endorsement is reflected in co-following and we demonstrate how our methodology can be used to reveal the audience similarities between Apple and Puma and, less obviously, between Nike and Coca-Cola. Concerning a user's popularity we find a statistically significant connection between having a more "average" followership and having more followers than direct rivals. Interestingly, a audience also seems to be linked to a audience in terms of their co-following.
@cite_24: With hundreds of millions of participants, social media services have become commonplace. Unlike a traditional social network service, a microblogging network like Twitter is a hybrid network, combining aspects of both social networks and information networks. Understanding the structure of such hybrid networks and predicting new links are important for many tasks such as friend recommendation, community detection, and modeling network growth. We note that the link prediction problem in a hybrid network is different from previously studied networks. Unlike the information networks and traditional online social networks, the structures in a hybrid network are more complicated and informative. We compare most popular and recent methods and principles for link prediction and recommendation. Finally we propose a novel structure-based personalized link prediction model and compare its predictive performance against many fundamental and popular link prediction methods on real-world data from the Twitter microblogging network. Our experiments on both static and dynamic data sets show that our methods noticeably outperform the state-of-the-art.
@cite_19: New ties are often formed between people who already have friends in common. Though the social sciences have addressed the effects of existing structural patterns on the formation of new ties, less attention has been given to ties in directed networks. Drawing from the microblogging service Twitter, we conducted a web-based experiment in which subjects were asked to rate their interest in forming ties to other people, blind to existing network connections between them. We show that two structural characteristics, transitivity and mutuality, are significant predictors of the desire to form new ties. Our findings shed light on tie formation, especially in online networks.
@cite_15: 
@cite_25: 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.
Determining which of two alternatives a Twitter user is more likely to follow is related to friend recommendation or link prediction as, in a sense, we are suggesting which of the two links should be formed. Intuitively, transitivity and mutuality of links are important signals for link prediction @cite_19 but, as discussed previously, we do not want to use such three people you follow also follow X'' information as it leads to a different type of application, closer to community detection. User similarity based on user attributes has also been used as a feature for link prediction @cite_15 @cite_24 @cite_25 . But this work still partly relies on mutuality and transitivity, which is equivalent to re-enforcing partisan camps without noting any existing similarities in terms of shared interests. For example, such approaches would most likely fail to pick up the similarity between @Puma and @TheAppleInc that we observe in .