Local and Global Influence on Twitter

The analysis of influence in social network is drawing more and more attention. It can be applied in different areas such as political campaigns and marketing. In this work, the analysis of influence in Twitter, based on users‟ profile statistics in a real-time scale, was studied and discussed. Two methods of identifying influential users by given keyword in real-time are introduced. To understand the relationship between users‟ influence features and social states in real life, two influence measures were presented: Local Influence which the user has on his/her immediate set of contacts and global Influence which the user has on the entire social network. This study describes in details these two metrics and shows their implementation for a real social network. Our case study, using Twitter, showed that the proposed model can create clusters of users in 2D space corresponding to their social standing, and can further be used to classify previously-unseen users into the correct classes with an f-measure of 0.82 which is significantly higher than benchmark algorithms. F-measure is often used for measuring the accuracy of the test for classification

Birds of a Feather Talk Together: User Influence on Language Adoption

Language is in constant flux be it from changes in meaning to the introduction of new terms. At the user level it changes by users accommodating their language in relation to whom they are in contact with. By mining diffusion's of new terms across social networks we detect the influence between users and communities. This is then used to compute the user activation threshold at which they adopt new terms dependent on their neighbours. We apply this method to four different networks from two popular on-line social networks (Reddit and Twitter). This research highlights novel results: by testing the network through random shuffles we show that the time at which a user adopts a term is dependent on the local structure, however, a large part of the influence comes from the global structure and that influence between users and communities is not significantly dependent on network structures

Birds of a feather talk together: user influence on language adoption

Language is in constant flux be it from changes in meaning to the introduction of new terms. At the user level it changes by users accommodating their language in relation to whom they are in contact with. By mining diffusion’s of new terms across social networks we detect the influence between users and communities. This is then used to compute the user activation threshold at which they adopt new terms dependent on their neighbours. We apply this method to four different networks from two popular on-line social networks (Reddit and Twitter). This research highlights novel results: by testing the network through random shuffles we show that the time at which a user adopts a term is dependent on the local structure, however, a large part of the influence comes from the global structure and that influence between users and communities is not significantly dependent on network structures

The Value of Network Information

The business model of companies such as Facebook, MySpace, and Twitter, relies on monetizing the information on the interactions and influences of their users. How valuable is such information, and is its use beneficial or detrimental for consumer welfare? We study these questions in a model where a monopoly sells a network good and may price discriminate using network information: information on consumers influences and/or on consumers susceptibilities to influence. Our framework incorporates a rich set of market products, including goods characterized by global and local network effects. We derive results on the value of network information and determine under which conditions, relative to uniform price, consumer surplus increases. We demonstrate the applicability of our framework using survey data on various types of relationships

Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure

Where are my followers? Understanding the Locality Effect in Twitter

Twitter is one of the most used applications in the current Internet with more than 200M accounts created so far. As other large-scale systems Twitter can obtain enefit by exploiting the Locality effect existing among its users. In this paper we perform the first comprehensive study of the Locality effect of Twitter. For this purpose we have collected the geographical location of around 1M Twitter users and 16M of their followers. Our results demonstrate that language and cultural characteristics determine the level of Locality expected for different countries. Those countries with a different language than English such as Brazil typically show a high intra-country Locality whereas those others where English is official or co-official language suffer from an external Locality effect. This is, their users have a larger number of followers in US than within their same country. This is produced by two reasons: first, US is the dominant country in Twitter counting with around half of the users, and second, these countries share a common language and cultural characteristics with US

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur