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21
Measuring Message Propagation and Social Influence on Twitter.com
"... Abstract. Although extensive studies have been conducted on online social networks (OSNs), it is not clear how to characterize information propagation and social influence, two types of important but not well defined social behavior. This paper presents a measurement study of 58M messages collected ..."
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Abstract. Although extensive studies have been conducted on online social networks (OSNs), it is not clear how to characterize information propagation and social influence, two types of important but not well defined social behavior. This paper presents a measurement study of 58M messages collected from 700K users on Twitter.com, a popular social medium. We analyze the propagation patterns of general messages and show how breaking news (Michael Jackson’s death) spread through Twitter. Furthermore, we evaluate different social influences by examining their stabilities, assessments, and correlations. This paper addresses the complications as well as challenges we encounter when measuring message propagation and social influence on OSNs. We believe that our results here provide valuable insights for future OSN research. 1
Information Transfer in Social Media
"... Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes ..."
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Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes based on the information–theoretic notion of transfer entropy, or information transfer. This theoretically grounded measure is based on dynamic information, captures fine– grain notions of influence, and admits a natural, predictive interpretation. Networks inferred by transfer entropy can differ significantly from static friendship networks because most friendship links are not useful for predicting future dynamics. We demonstrate through analysis of synthetic and real–world data that transfer entropy reveals meaningful hidden network structures. In addition to altering our notion of who is influential, transfer entropy allows us to differentiate between weak influence over large groups and strong influence over small groups.
Supervised Rank Aggregation for Predicting Influence in Networks
, 2011
"... focused on the identification of the most important actors in a social network. This has resulted in several measures of influence and authority. While most of such sociometrics (e.g., PageRank) are driven by intuitions based on an actors location in a network, asking for the “most influential ” act ..."
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focused on the identification of the most important actors in a social network. This has resulted in several measures of influence and authority. While most of such sociometrics (e.g., PageRank) are driven by intuitions based on an actors location in a network, asking for the “most influential ” actors in itself is an ill-posed question, unless it is put in context with a specific measurable task. Constructing a predictive task of interest in a given domain provides a mechanism to quantitatively compare different measures of influence. Furthermore, when we know what type of actionable insight to gather, we need not rely on a single network centrality measure. A combination of measures is more likely to capture various aspects of the social network that are predictive and beneficial for the task. Towards this end, we propose an approach to supervised rank aggregation, driven by techniques from Social Choice Theory. We illustrate the effectiveness of this method through experiments on Twitter and citation networks. I.
Extraction and Analysis of Facebook Friendship Relations
"... Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and offer of ..."
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Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and offer of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (offline) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem). However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms.
Bimodal invitation-navigation fair bets model for authority identification in a social network
- In WWW 2012
"... We consider the problem of identifying the most respected, authoritative members of a large-scale online social network (OSN) by constructing a global ranked list of its members. The problem is distinct from the problem of identifying influencers: we are interested in identifying members who are inf ..."
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We consider the problem of identifying the most respected, authoritative members of a large-scale online social network (OSN) by constructing a global ranked list of its members. The problem is distinct from the problem of identifying influencers: we are interested in identifying members who are influential in the real world, even when not necessarily so on the OSN. We focus on two sources for information about user authority: (a) invitations to connect, which are usually sent to people whom the inviter respects, and (b) members’ browsing behavior, as profiles of more important people are viewed more often than others’. We construct two directed graphs over the same set of nodes (representing member profiles): the invitation graph and the navigation graph respectively. We show that the standard PageRank algorithm, a baseline in web page ranking, is not effective in people ranking, and develop a social capital based model, called the fair bets model, as a viable solution. We then propose a novel approach, called bimodal fair bets, for combining information from two (or more) endorsement graphs drawn from the same OSN, by simultaneously using the authority scores of nodes in one graph to inform the other, and vice versa, in a mutually reinforcing fashion. We evaluate the ranking results on the LinkedIn social network using this model, where members who have Wikipedia profiles are assumed to be authoritative. Experimental results show that our approach outperforms the baseline approach by a large margin.
Non-conservative Diffusion and its Application to Social Network Analysis
, 2011
"... The random walk is fundamental to modeling dynamic processes on networks. Metrics based on the random walk have been used in many applications from image processing to Web page ranking. However, how appropriate are random walks to modeling and analyzing social networks? We argue that unlike a random ..."
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Cited by 4 (1 self)
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The random walk is fundamental to modeling dynamic processes on networks. Metrics based on the random walk have been used in many applications from image processing to Web page ranking. However, how appropriate are random walks to modeling and analyzing social networks? We argue that unlike a random walk, which conserves the quantity diffusing on a network, many interesting social phenomena, such as the spread of information or disease on a social network, are fundamentally non-conservative. When an individual infects her neighbor with a virus, the total amount of infection increases. We classify diffusion processes as conservative and non-conservative and show how these differences impact the choice of metrics used for network analysis, as well as our understanding of network structure and behavior. We show that Alpha-Centrality, which mathematically describes non-conservative diffusion, leads to new insights into the behavior of spreading processes on networks. We give a scalable approximate algorithm for computing the Alpha-Centrality in a massive graph. We validate our approach on real-world online social networks of Digg. We show that a non-conservative metric, such as Alpha-Centrality, produces better agreement with empirical measure of influence than conservative metrics, such as PageRank. We hope that our investigation will inspire further exploration into the realms of conservative and non-conservative metrics in social network analysis.
Agents of Influence in Social Networks
"... In recent years, social networking sites and social media have become a very important part of peoples ’ lives, driving everything from family relationships to revolutions. In this work, we study the different patterns of interaction behavior seen in an online social network. We investigate the diff ..."
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In recent years, social networking sites and social media have become a very important part of peoples ’ lives, driving everything from family relationships to revolutions. In this work, we study the different patterns of interaction behavior seen in an online social network. We investigate the difference in the relative time people allocate to their friends versus that which their friends allocate to them, and propose a measure for this difference in time allocation. The distribution of this measure is used to identify classes of social agents through agglomerative hierarchical clustering. These classes are then characterized in terms of two important structural attributes: Degree distributions and clustering coefficients. We demonstrate our approach on two large social networks obtained from Facebook. For each network we have the list
Information-Theoretic Measures of Influence Based on Content Dynamics
"... The fundamental building block of social influence is for one person to elicit a response in another. Researchers measur-ing a “response ” in social media typically depend either on detailed models of human behavior or on platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most c ..."
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Cited by 2 (0 self)
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The fundamental building block of social influence is for one person to elicit a response in another. Researchers measur-ing a “response ” in social media typically depend either on detailed models of human behavior or on platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most content on social networks is difficult to model because the modes and motivation of human expression are diverse and incompletely understood. We introduce content transfer, an information-theoretic measure with a predictive interpreta-tion that directly quantifies the strength of the effect of one user’s content on another’s in a model-free way. Estimating this measure is made possible by combining recent advances in non-parametric entropy estimation with increasingly so-phisticated tools for content representation. We demonstrate on Twitter data collected for thousands of users that con-tent transfer is able to capture non-trivial, predictive rela-tionships even for pairs of users not linked in the follower or mention graph. We suggest that this measure makes large quantities of previously under-utilized social media content accessible to rigorous statistical causal analysis.
Tracking Human Migration from Online Attention
"... Abstract. The dynamics behind human migrations are very complex. Economists have intensely studied them because of their importance for the global economy. However, tracking migration is costly, and available data tends to be outdated. Online data can be used to extract proxies for migration flows, ..."
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Abstract. The dynamics behind human migrations are very complex. Economists have intensely studied them because of their importance for the global economy. However, tracking migration is costly, and available data tends to be outdated. Online data can be used to extract proxies for migration flows, and these proxies would not be meant to replicate tradi-tional measurements but are meant to complement them. We analyze a random sample of a microblogging service popular in Brazil (more than 13M posts and 22M reposts) and accurately predict the total number of migrants in 35 Brazilian cities. These results are so accurate that they have promising implications in monitoring emerging economies. 1
The impact of dynamic interactions in multi-scale analysis of network structure
- CoRR
, 2012
"... To find interesting structure in networks, community detec-tion algorithms have to take into account not only the net-work topology, but also dynamics of interactions between nodes. We investigate this claim using the paradigm of syn-chronization in a network of coupled oscillators. As the net-work ..."
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To find interesting structure in networks, community detec-tion algorithms have to take into account not only the net-work topology, but also dynamics of interactions between nodes. We investigate this claim using the paradigm of syn-chronization in a network of coupled oscillators. As the net-work evolves to a global steady state, nodes belonging to the same community synchronize faster than nodes belonging to different communities. Traditionally, nodes in network syn-chronization models are coupled via one-to-one, or conserva-tive interactions. However, social interactions are often one-to-many, as for example, in social media, where users broad-cast messages to all their followers. We formulate a novel model of synchronization in a network of coupled oscillators in which the oscillators are coupled via one-to-many, or non-conservative interactions. We study the dynamics of different interaction models and contrast their spectral properties. To find multi-scale community structure in a network of inter-acting nodes, we define a similarity function that measures the degree to which nodes are synchronized and use it to hierarchically cluster nodes. We study real-world social net-works, including networks of two social media providers. To evaluate the quality of the discovered communities in a so-cial media network we propose a community quality metric based on user activity. We find that conservative and non-conservative interaction models lead to dramatically differ-ent views of community structure even within the same net-work. Our work offers a novel mathematical framework for exploring the relationship between network structure, topol-ogy and dynamics.
