Studies on friendships in online social networks involving geographic distance have so far relied on the city location provided in users' profiles. Consequently, most of the research on friendships have provided accuracy at the city level, at best, to designate a user's location. This study analyzes a Twitter dataset because it provides the exact geographic distance between corresponding users. We start by introducing a strong definition of "friend" on Twitter (i.e., a definition of bidirectional friendship), requiring bidirectional communication. Next, we utilize geo-tagged mentions delivered by users to determine their locations, where "@username" is contained anywhere in the body of tweets. To provide analysis results, we first introduce a friend counting algorithm. From the fact that Twitter users are likely to post consecutive tweets in the static mode, we also introduce a two-stage distance estimation algorithm. As the first of our main contributions, we verify that the number of friends of a particular Twitter user follows a well-known power-law distribution (i.e., a Zipf's distribution or a Pareto distribution). Our study also provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides concrete evidence that Twitter can be a useful platform for assigning a more accurate scalar value to the degree of friendship between two users.
To understand the nature of friendships online with respect to geographic distance, some efforts have focused on users' online profiles that include their city of residence @cite_8 @cite_32 . @cite_8 , experimental results based on the LiveJournal social network https: www.livejournal.com demonstrated a close relationship between geographic distance and probability distribution of friendship, where the probability of befriending a particular user on LiveJournal is inversely proportional to the positive power of the number of closer users. Contrary to @cite_8 , based on the data collected from Tuenti, https: www.tuenti.com a Spanish social networking service, it was found in @cite_32 that social interactions online are only weakly affected by spatial proximity, with other factors dominating.
@cite_32 @cite_8
2086567932 2182951319 1995629273 1780950168
Online friendship connections are often not representative of social relationships or shared interest between users, but merely provide a public display of personal identity. A better picture of online social behaviour can be achieved by taking into account the intensity of communication levels between users, yielding useful insights for service providers supporting this communication. Among the several factors impacting user interactions, geographic distance might be affecting how users communicate with their friends. While spatial proximity appears influencing how people connect to each other even on the Web, the relationship between social interaction and spatial distance remains unexplored. In this work we analyse the relationship between online user interactions and geographic proximity with a detailed study of a large Spanish online social service. Our results show that while geographic distance strongly affects how social links are created, spatial proximity plays a negligible role on user interactions. These findings offer new insights on the interplay between social and spatial factors influencing online user behaviour and open new directions for future research and applications. the problems of predicting, classifying, and annotating friends relations in friends networks, based upon network structure and user profile data. First, we document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, we explain how the problem of classifying a user pair in a social network, as directly connected or not, poses the problem of selecting and constructing relevant features. We document feature analyzers for attributes that depend only on graph attributes, those that depend on individual user demographics and set-valued attributes (e.g., interests, communities, and educational institutions), and those that depend on candidate user pairs. We then extend our data model using whole-network attributes and report machine learning experiments on learning the concept of a connected pair of friends from LiveJournal data. Finally, we develop a theory of dependent types for deriving causal explanations and discuss how this can be used to scale statistical relational learning up to our full corpus, a recent crawl of over a million records from LiveJournal. We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals’ physical locations over time. Social media allow for an unprecedented amount of interaction between people online. A fundamental aspect of human social behavior, however, is the tendency of people to associate themselves with like-minded individuals, forming homogeneous social circles both online and offline. In this work, we apply a new model that allows us to distinguish between social ties of varying strength, and to observe evidence of homophily with regards to politics, music, health, residential sector & year in college, within the online and offline social network of 74 college students. We present a multiplex network approach to social tie strength, here applied to mobile communication data - calls, text messages, and co-location, allowing us to dimensionally identify relationships by considering the number of communication channels utilized between students. We find that strong social ties are characterized by maximal use of communication channels, while weak ties by minimal use. We are able to identify 75 of close friendships, 90 of weaker ties, and 90 of Facebook friendships as compared to reported ground truth. We then show that stronger ties exhibit greater profile similarity than weaker ones. Apart from high homogeneity in social circles with respect to political and health aspects, we observe strong homophily driven by music, residential sector and year in college. Despite Facebook friendship being highly dependent on residence and year, exposure to less homogeneous content can be found in the online rather than the offline social circles of students, most notably in political and music aspects.