Massive Online Social Networks Research Articles

Expanding the measurement of culture with a sample of two billion humans

Culture has played a pivotal role in human evolution. Yet, the ability of social scientists to study culture is limited by the currently available measurement instruments. Scholars of culture must regularly choose between scalable but sparse survey-based methods or restricted but rich ethnographic methods. Here, we demonstrate that massive online social networks can advance the study of human culture by providing quantitative, scalable and high-resolution measurement of behaviourally revealed cultural values and preferences. We employ data across nearly 60 000 topic dimensions drawn from two billion Facebook users across 225 countries and territories. We first validate that cultural distances calculated from this measurement instrument correspond to traditional survey-based and objective measures of cross-national cultural differences. We then demonstrate that this expanded measure enables rich insight into the cultural landscape globally at previously impossible resolution. We analyse the importance of national borders in shaping culture and compare subnational divisiveness with gender divisiveness across countries. Our measure enables detailed investigation into the geopolitical stability of countries, social cleavages within small- and large-scale human groups, the integration of migrant populations and the disaffection of certain population groups from the political process, among myriad other potential future applications.

Expanding the Measurement of Culture with a Sample of Two Billion Humans

Culture has played a pivotal role in human evolution. Yet, the ability of social scientists to study culture is limited by currently available measurement instruments. Scholars of culture must regularly choose between scalable but sparse survey-based methods or restricted but rich ethnographic methods. Here, we demonstrate that massive online social networks can advance the study of human culture by providing quantitative, scalable, and high-resolution measurement of behaviorally revealed cultural values and preferences. We employ publicly available data across nearly 60,000 topic dimensions drawn from two billion Facebook users across 225 countries and territories. The data capture preferences inferred by Facebook from online behaviours on the platform, behaviors on external websites and apps, and offline behaviours captured by smartphones and other devices. We first validate that cultural distances calculated from this measurement instrument correspond to survey-based and objective measures of cultural differences. We then demonstrate that this measure enables insight into the cultural landscape globally at previously impossible resolution. We analyze the importance of national borders in shaping culture and explore unique cultural markers that identify subnational population groups. The global collection of massive data on human behavior provides a high-dimensional complement to traditional cultural metrics, potentially enabling novel insight into fundamental questions in the social sciences. The measure enables detailed investigation into the countries’ geopolitical stability, social cleavages within both small and large-scale human groups, the integration of migrant populations, and the disaffection of certain population groups from the political process, among myriad other potential future applications.

Shortest Path Computing Using Sketch Structure

Nowadays various application domains is dramatically increasing, the number of nodes may reach the scale of hundreds of millions or even more. Computing shortest paths between two given nodes is a fundamental operation over graphs, but known to be nontrivial over large disk-resident instances of graph data. While a number of techniques exist for answering reach ability queries and approximating node distances efficiently, determining actual shortest paths (i.e. the sequence of nodes involved) is often neglected. However, in applications arising in massive online social networks, biological networks, and knowledge graphs it is often essential to find out many, if not all, shortest paths between two given nodes. Shortest distance query is a fundamental operation in large-scale networks. Many existing methods in the literature take a landmark embedding approach, which selects a set of graph nodes as landmarks and computes the shortest distances from each landmark to all nodes as an embedding. To answer a shortest distance query, the pre computed distances from the landmarks to the two query nodes are used to compute an approximate shortest distance based on the triangle inequality. In this paper, I analyze the factors that affect the accuracy of distance estimation in landmark embedding. In particular, find that a globally selected, query in dependent landmark set may introduce a large relative error, especially for nearby query nodes. To address this issue, I propose a query-dependent local landmark scheme, which identifies a local landmark close to both query nodes and provides more accurate distance estimation than the traditional global landmark approach. We propose efficient local landmark indexing and retrieval techniques with a scalable sketch-based index structure that not only supports estimation of node distances, but also computes corresponding shortest paths themselves to achieve low offline indexing complexity and online query complexity.

Words on the Web: Noninvasive Detection of Emotional Contagion in Online Social Networks

Does semantic expression spread online from person to person? And if so, what kinds of expression are most likely to spread? To address these questions, we developed a nonexperimental, noninvasive method to detect and quantify contagion of semantic expression in massive online social networks, which we review and discuss here. Using only observational data, the method avoids performing emotional experiments on users of online social networks, a research practice that recently became an object of criticism and concern. Our model combines geographic aggregation and instrumental variables regression to measure the effect of an exogenous variable on an individual's expression and the influence of this change on the expression of others to whom that individual is socially connected. In a previous work, we applied our method to the emotional content of posts generated by a large sample of users over a period of three years. Those results suggest that each post expressing a positive or negative emotion can cause friends to generate one to two additional posts expressing the same emotion, and it also inhibits their use of the opposite emotion. Here, we generalize our method so it can be applied to contexts different than emotional expression and to different forms of content generated by the users of online platforms. The method allows us to determine the usage of words in the same semantic category spread, and to estimate a signed relationship between different semantic categories, showing that an increase in the usage of one category alters the usage of another category in one's social contacts. Finally, it also allows us to estimate the total cumulative effect that a person has on all of her social contacts.

Detecting Emotional Contagion in Massive Social Networks

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.

Combining Cultural Analytics and Networks Analysis: Studying a Social Network Site with User-Generated Content

Humanities and social sciences require new research approaches to deal with and to benefit from the explosion of digital media. Thanks to large scale data storage and processing, close reading of individual items can be supplemented with analysis of broader trends from massive amounts of data. Cultural Analytics (CA) is a recently developed methodology for the exploration of content and visual form in large image and video collections. It has already been applied to a variety of media types, including TV programs, feature films, newspapers, and video games. However, until now these applications did not take into account the social context of media. In many cases, media artifacts under scrutiny are generated or used by people in a social setting, either in forms of (digital) communities, or in terms of social relations, which can be also analyzed as a valuable source of information. Social network analysis (SNA) is a set of methods for the analysis of human networks, including massive online social networks. In this article, we argue that CA and SNA can be combined synergistically, using data and images from deviantArt, the leading online network of user-created art, as a case study.

"Beyond the social web: the geo-social revolution" by Salvatore Scellato with Ching-man Au Yeung as Coordinator

While in the last years massive online social networks such as Facebook and Twitter have become extremely popular, gathering and engaging millions of users, only recently the widespread adoption of mobile devices has led to a large portion of users continuously using these social services during their daily lives. These devices offer geolocation capabilities: the ability to share your location, to generate location-tagged information and to search for it adds a crucial spatial dimension to online social networking. As a result, online social services are increasingly becoming location-aware, allowing users to create and access information about their geographic whereabouts: the trend is progressively going from specialized providers offering location-as-a-service to a widespread new concept of location-as-a-feature , where every online social platform integrates geographic information into their services. This provides broad and plentiful data to investigate how spatial and social structure blend together, opening exciting research directions with promising scientific and practical applications. In this article I will present how the socio-spatial properties of online social networks can be studied and how novel geo-social network measures can be defined to blend together social and spatial properties. The availability of geographic data, together with the rising importance of mobile devices, is likely to transform the Web and how it is used by billions of people every day.

Focus on Authors

Focus on Authors

Social networks that matter: Twitter under the microscope

Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers.

Social Networks that Matter: Twitter Under the Microscope

Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the declared set of friends and followers.