Popularity in social media is an important objective for professional users (e.g. companies, celebrities, and public figures, etc). A simple yet prominent metric utilized to measure the popularity of a user is the number of fans or followers she succeed to attract to her page. Popularity is influenced by several factors which identifying them is an interesting research topic. This paper aims to understand this phenomenon in social media by exploring the popularity evolution for professional users in Facebook. To this end, we implemented a crawler and monitor the popularity evolution trend of 8k most popular professional users on Facebook over a period of 14 months. The collected dataset includes around 20 million popularity values and 43 million posts. We characterized different popularity evolution patterns by clustering the users temporal number of fans and study them from various perspectives including their categories and level of activities. Our observations show that being active and famous correlate positively with the popularity trend.
Simultaneously, many studies have tried to model and forecast popularity, specially for content @cite_21 . Bandari utilized article features like source, category, and subjectivity to predict the popularity of an article on Twitter with 84 It is worth mentioning that several companies monitor Facebook FanPages activities and provide reports, by charging their customers, with general analysis for their clients. One of them that provides aggregated popularity results for single users, is SocialBakers . They claim that their services allow brands to measure, compare, and contrast the success of their social media campaigns with competitive intelligence. In summary, although few studies have looked to the different aspects of Facebook FanPages , but their focus were mostly for a small group of users. To the best of the authors knowledge none of the previous studies has specifically investigated the evolution of popularity in a large scale and for a long period. This paper is the first study that looks to this aspect for a list of 8K popular FanPages and also investigates the influential factors to the popularity evolution trends.
@cite_21
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The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity a priori, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network (“niche interests”) from those of more general interest to the user community. This distinction is useful for predicting a story’s eventual popularity from users’ early reactions to the story. Foursquare, the currently most popular location-based social network, allows users not only to share the places (venues) they visit but also post micro-reviews (tips) about their previous experiences at specific venues as well as "like" previously posted tips. The number of "likes" a tip receives ultimately reflects its popularity among users, providing valuable feedback to venue owners and other users. In this paper, we provide an extensive analysis of the popularity dynamics of Foursquare tips using a large dataset containing over 10 million tips and 9 million likes posted by over 13,5 million users. Our results show that, unlike other types of online content such as news and photos, Foursquare tips experience very slow popularity evolution, attracting user likes through longer periods of time. Moreover, we find that the social network of the user who posted the tip plays an important role on the tip popularity throughout its lifetime, but particularly at earlier periods after posting time. We also find that most tips experience their daily popularity peaks within the first month in the system, although most of their likes are received after the peak. Moreover, compared to other types of online content (e.g., videos), we observe a weaker presence of the rich-get-richer effect in our data, demonstrating a lower correlation between the early and late popularities. Finally, we evaluate the stability of the tip popularity ranking over time, assessing to which extent the current popularity ranking of a set of tips can be used to predict their popularity ranking at a future time. To that end, we compare a prediction approach based solely on the current popularity ranking against a method that exploits a linear regression model using a multidimensional set of predictors as input. Our results show that use of the richer set of features can indeed improve the prediction accuracy, provided that enough data is available to train the regression model. News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84 accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web. News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84 accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.