This paper analyzes the diffusion of information on Facebook through users' News Feeds. The researchers analyzed data on over 262,000 Facebook Pages and their fans over a 6-month period. They found that while diffusion chains on Facebook can be extremely long, they are typically not the result of single chain reactions, but rather many short diffusion chains merging together. The researchers used statistical models to analyze these diffusion chains and found no evidence that a user's demographics or Facebook activity could predict the length of diffusion chains they initiate.
Это интереснейшее исследование того, насколько лента новостей Facebook хороша как механика распространения информации (виральности) в Facebook. Ставит под сомнение некоторые известные приемы посева классических вирусов.
This document provides an introduction to social network analysis (SNA), including its origins in social science and mathematics. SNA focuses on relationships between individuals, groups, or institutions rather than attributes of individuals. It represents social structures as networks and uses graph theory concepts to analyze them. SNA is useful for understanding information flow, identifying influential individuals, and improving network effectiveness. The document discusses key SNA concepts like networks, tie strength, central players, and network cohesion.
This literature survey discusses papers on the topological structure of social networks and information propagation. Regarding network structure, papers found that social networks like Facebook exhibit scale-free and small-world properties with high clustering. Different networks may have different structures depending on factors like symmetry. Regarding information spread, factors like sender involvement, tie strength, and communication influence forwarding. Messages tend to spread through closely connected friendship networks rather than broadly. Key influencers and the structure of interaction graphs also impact propagation patterns.
Presented at IZEAfest in Orlando, FL
Social network and sharing analysis including:
+Document analysis at scale: Meme tracking combined with other variables like sentiment and bias
+Social network at scale: Information cascades and virality, inference of social networks given meme-like information as contagions
+The node level perspective and its effects on what an individual sees and shares: Illusions, effort and overload, topics, personality and demographics
+Personas and segmentation: Grouping based on demographics and interests
This document summarizes a research project on predicting whether online content will become viral based on its spread through the core-periphery structure of social networks. The research aims to improve on prior work focusing on community structure by also considering how content spreads between the dense core and less connected periphery of networks. It involves generating a synthetic social network model with scale-free, community, and core-periphery properties, developing a model for content spread, and comparing the approach to real networks. The hypothesis is that for content to spread widely across communities and become viral, it must first reach the highly connected core nodes.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses how rumors spread quickly through social networks. It simulates a simple rumor spreading process on real-world social networks like Twitter and Orkut as well as theoretical network models. The results show that rumors spread much faster in the structures of actual social networks and preferential attachment networks than in random or complete networks. Specifically, a rumor reaching 45.6 million Twitter users within 8 rounds of communication.
Это интереснейшее исследование того, насколько лента новостей Facebook хороша как механика распространения информации (виральности) в Facebook. Ставит под сомнение некоторые известные приемы посева классических вирусов.
This document provides an introduction to social network analysis (SNA), including its origins in social science and mathematics. SNA focuses on relationships between individuals, groups, or institutions rather than attributes of individuals. It represents social structures as networks and uses graph theory concepts to analyze them. SNA is useful for understanding information flow, identifying influential individuals, and improving network effectiveness. The document discusses key SNA concepts like networks, tie strength, central players, and network cohesion.
This literature survey discusses papers on the topological structure of social networks and information propagation. Regarding network structure, papers found that social networks like Facebook exhibit scale-free and small-world properties with high clustering. Different networks may have different structures depending on factors like symmetry. Regarding information spread, factors like sender involvement, tie strength, and communication influence forwarding. Messages tend to spread through closely connected friendship networks rather than broadly. Key influencers and the structure of interaction graphs also impact propagation patterns.
Presented at IZEAfest in Orlando, FL
Social network and sharing analysis including:
+Document analysis at scale: Meme tracking combined with other variables like sentiment and bias
+Social network at scale: Information cascades and virality, inference of social networks given meme-like information as contagions
+The node level perspective and its effects on what an individual sees and shares: Illusions, effort and overload, topics, personality and demographics
+Personas and segmentation: Grouping based on demographics and interests
This document summarizes a research project on predicting whether online content will become viral based on its spread through the core-periphery structure of social networks. The research aims to improve on prior work focusing on community structure by also considering how content spreads between the dense core and less connected periphery of networks. It involves generating a synthetic social network model with scale-free, community, and core-periphery properties, developing a model for content spread, and comparing the approach to real networks. The hypothesis is that for content to spread widely across communities and become viral, it must first reach the highly connected core nodes.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses how rumors spread quickly through social networks. It simulates a simple rumor spreading process on real-world social networks like Twitter and Orkut as well as theoretical network models. The results show that rumors spread much faster in the structures of actual social networks and preferential attachment networks than in random or complete networks. Specifically, a rumor reaching 45.6 million Twitter users within 8 rounds of communication.
Networks provide connections and positions that influence health outcomes. Social network analysis examines relationships between actors to understand how networks impact behavior. Networks matter through both connectionist mechanisms like diffusion, and positional mechanisms like social roles. Network data can be analyzed at different levels from individual ego networks to global networks, and can involve one or multiple types of relationships between nodes. Social network data is commonly represented through matrices and lists to encode network structure and allow computational analysis.
Community Evolution in the Digital Space and Creation of SocialInformation C...Saptarshi Ghosh
A social homogeneous group can be formed irrespective to geo-spatial contiguity and research reveals that interaction through online communication fosters social behaviours like teamwork, ties, bonding and trust building as well as community building.
This document outlines a research agenda for Web 3.0 technologies focusing on location awareness, ubiquitous collaboration, and their impacts. Key questions are proposed around how location-aware information delivery and contextualization in space affects memorability and knowledge organization, and how optimal collaboration structures can be measured and supported online to enhance learning, content production, and social outcomes. Relevant learning and social theories are discussed. Usability factors like task-fit and multimodal information are also addressed.
The document discusses community detection in networks and multilayer networks. It begins with an introduction to community detection, how to calculate communities using various algorithms, and the importance of resolution parameters. It then provides a short introduction to multilayer networks and examples of community detection applied to real-world networks, including social networks, protein interaction networks, and legislative voting networks. The key points are that network representations provide a flexible framework for studying complex data, community detection is a powerful tool for exploring network structures, and network structures can identify essential features for modeling and understanding data applications.
Community detection algorithms are used to identify densely connected groups of nodes in networks. Modularity optimization is commonly used, which detects communities as groups of nodes with more connections within groups than expected by chance. Parameters like resolution affect results. Multilayer networks model systems with multiple network layers over nodes. Multilayer modularity generalizes modularity to multilayer networks. Community detection in multilayer networks provides insights into structures across data types and applications.
Scott Gomer presented on social network analysis (SNA). He reviewed literature on SNA and its use as a tool to analyze social structures and influence. He discussed SNA's capabilities in identifying key relationships and influencers through visual sociograms. However, SNA also has limitations such as complexity with large networks. Gomer collected binary data on relationships within a network and analyzed it using sociograms to illustrate examples of social link platforms. He concluded that SNA is a qualitative tool that can provide useful insights for marketing research by studying relationships.
Icwsm10 S MateiVisible Effort: A Social Entropy Methodology for Managing Com...guest803e6d
A theoretically-grounded learning feedback tool suite, the Visible Effort (VE) Mediawiki extension, is proposed for optimizing online group learning activities by measuring the amount of equality and the emergence of social structure in groups that participate in Computer-Mediated Collaboration (CMC). Building on social entropy theory, drawn from Shannon’s Mathematical Theory of Communication, VE captures levels of CMC unevenness and group structure and visualizes them on wiki Web pages through background colors, charts, and tabular data. Visual information provides users entropic feedback on how balanced and equitable collaboration is within their online group are, while helping them to maintain it within optimal levels. Finally, we present the theoretical and practical implications of VE and the measures behind it, as well as illustrate VE’s capabilities by describing a quasi-experimental teaching activity (use scenario) in tandem with a detailed discussion of theoretical justification, methodological underpinning, and technological capabilities of the approach.
This document provides an overview of social network analysis from online behavior and data mining techniques. It discusses classical social network analysis approaches and frameworks. Various types of social network analysis are described, including whole network analysis and personal network analysis. Models and measures used in social network analysis are also outlined.
This document provides a history of network visualization from its origins in the 18th century to modern applications. It discusses:
- Early work by Euler and kinship diagrams that relied on visual representations to study networks.
- J.L. Moreno's sociograms in the 1930s that sparked interest in social network analysis through visualizations.
- Developments through the 20th century as researchers sought to move from artistic to more scientific visualizations.
- Challenges of visualizing large, complex networks while representing multiple node and edge attributes simultaneously.
- Benefits of visualization in making invisible network structures visible and providing multi-dimensional insights beyond metrics alone.
This document summarizes a research paper about factors that predict whether users will unfollow other users on Twitter. The researchers analyzed structural properties of 911 Twitter users' social networks, such as number of followers, reciprocity of follows, and common connections. They found that reciprocity, network density, prestige ratio, follow-back rate, and number of common neighbors strongly predicted whether ties would be broken through unfollowing. The paper provides more detailed analysis using multi-level logistic regression to model how network structure impacts breaking ties on Twitter.
This document discusses network data collection. It begins by providing examples of how social structure matters and influences outcomes. It then discusses different ways to detect social structure through network data collection, including small group questionnaires, large surveys, observations, and digital data scraping. The document outlines key network questions that can shape data collection, such as how networks form and their consequences. It also discusses sampling and defining network boundaries. Overall, the document provides an overview of network data collection methods and considerations.
Preso on social network analysis for rtp analytics unconferenceBruce Conner
Selected highlights of Coursera Social Networking course, taught by Prof. Lada Adamic of the Univ. of Michigan. Presented at the annual Annual RTP Analytics Unconference, May 4, 2013
This document discusses social network analysis (SNA), a tool used to analyze social relationships and networks. SNA elicits, analyzes, and visualizes how actors interact and resources move through a network. It represents actors as nodes and their relationships as ties. The document provides examples of SNA's application in education, health, and agriculture. It outlines the process of conducting SNA through workshops or surveys to collect node attribute and tie/link data, which is then analyzed using software to visualize the network. The document suggests opportunities to further develop SNA, such as presenting networks back to communities and measuring social capital.
Network Data Collection
The document discusses collecting social network data. It covers three main topics:
1) Introduces social network analysis and why networks are important in social science. Networks matter because of connections that allow diffusion and because positions in networks influence roles and behavior.
2) Discusses research design considerations for collecting network data, including specifying relations of interest based on theoretical mechanisms, boundary selection, and sampling approaches.
3) Addresses accuracy of network survey data and how to handle inaccurate or missing data. The goal is to systematically understand connections between actors using empirical network data and analysis methods.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
This document discusses community detection in networks. Community detection aims to identify tightly knit groups within networks. The document outlines popular community detection algorithms like modularity maximization and stochastic block models. It also discusses applications of community detection to multilayer networks and examples like congressional voting networks and Facebook networks. Community detection is a useful tool for exploring network structure and identifying essential features in data.
Incremental Community Mining in Location-based Social NetworkIJAEMSJORNAL
A social network can be defined as a set of social entities connected by a set of social relations. These relations often change and differ in time. Thus, the fundamental structure of these networks is dynamic and increasingly developing. Investigating how the structure of these networks evolves over the observation time affords visions into their evolution structure, elements that initiate the changes, and finally foresee the future structure of these networks. One of the most relevant properties of networks is their community structure – set of vertices highly connected between each other and loosely connected with the rest of the network. Subsequently networks are dynamic, their underlying community structure changes over time as well, i.e they have social entities that appear and disappear which make their communities shrinking and growing over time. The goal of this paper is to study community detection in dynamic social network in the context of location-based social network. In this respect, we extend the static Louvain method to incrementally detect communities in a dynamic scenario following the direct method and considering both overlapping and non-overlapping setting. Finally, extensive experiments on real datasets and comparison with two previous methods demonstrate the effectiveness and potential of our suggested method.
The document discusses assortativity and dissortativity in complex networks. It defines assortativity as "nodes with similar degree connect preferably" and dissortativity as "nodes with low degree try to connect with highly connected nodes." Several studies are summarized that examine assortativity in social networks like Twitter. Happiness was found to be assortative in Twitter reciprocal reply networks. Distance from one's typical location on Twitter was also found to correlate with expressed happiness levels. Assortative networks are described as being more resilient to attacks while disassortative networks are more vulnerable.
Presentation of the paper:
Evaluating role mining algorithms. In Proceedings of the 14th ACM symposium on Access control models and technologies (SACMAT '09)
Ian Molloy, Ninghui Li, Tiancheng Li, Ziqing Mao, Qihua Wang, and Jorge Lobo. 2009.
ACM, New York, NY, USA, 95-104. DOI=10.1145/1542207.1542224 http://doi.acm.org/10.1145/1542207.1542224
Networks provide connections and positions that influence health outcomes. Social network analysis examines relationships between actors to understand how networks impact behavior. Networks matter through both connectionist mechanisms like diffusion, and positional mechanisms like social roles. Network data can be analyzed at different levels from individual ego networks to global networks, and can involve one or multiple types of relationships between nodes. Social network data is commonly represented through matrices and lists to encode network structure and allow computational analysis.
Community Evolution in the Digital Space and Creation of SocialInformation C...Saptarshi Ghosh
A social homogeneous group can be formed irrespective to geo-spatial contiguity and research reveals that interaction through online communication fosters social behaviours like teamwork, ties, bonding and trust building as well as community building.
This document outlines a research agenda for Web 3.0 technologies focusing on location awareness, ubiquitous collaboration, and their impacts. Key questions are proposed around how location-aware information delivery and contextualization in space affects memorability and knowledge organization, and how optimal collaboration structures can be measured and supported online to enhance learning, content production, and social outcomes. Relevant learning and social theories are discussed. Usability factors like task-fit and multimodal information are also addressed.
The document discusses community detection in networks and multilayer networks. It begins with an introduction to community detection, how to calculate communities using various algorithms, and the importance of resolution parameters. It then provides a short introduction to multilayer networks and examples of community detection applied to real-world networks, including social networks, protein interaction networks, and legislative voting networks. The key points are that network representations provide a flexible framework for studying complex data, community detection is a powerful tool for exploring network structures, and network structures can identify essential features for modeling and understanding data applications.
Community detection algorithms are used to identify densely connected groups of nodes in networks. Modularity optimization is commonly used, which detects communities as groups of nodes with more connections within groups than expected by chance. Parameters like resolution affect results. Multilayer networks model systems with multiple network layers over nodes. Multilayer modularity generalizes modularity to multilayer networks. Community detection in multilayer networks provides insights into structures across data types and applications.
Scott Gomer presented on social network analysis (SNA). He reviewed literature on SNA and its use as a tool to analyze social structures and influence. He discussed SNA's capabilities in identifying key relationships and influencers through visual sociograms. However, SNA also has limitations such as complexity with large networks. Gomer collected binary data on relationships within a network and analyzed it using sociograms to illustrate examples of social link platforms. He concluded that SNA is a qualitative tool that can provide useful insights for marketing research by studying relationships.
Icwsm10 S MateiVisible Effort: A Social Entropy Methodology for Managing Com...guest803e6d
A theoretically-grounded learning feedback tool suite, the Visible Effort (VE) Mediawiki extension, is proposed for optimizing online group learning activities by measuring the amount of equality and the emergence of social structure in groups that participate in Computer-Mediated Collaboration (CMC). Building on social entropy theory, drawn from Shannon’s Mathematical Theory of Communication, VE captures levels of CMC unevenness and group structure and visualizes them on wiki Web pages through background colors, charts, and tabular data. Visual information provides users entropic feedback on how balanced and equitable collaboration is within their online group are, while helping them to maintain it within optimal levels. Finally, we present the theoretical and practical implications of VE and the measures behind it, as well as illustrate VE’s capabilities by describing a quasi-experimental teaching activity (use scenario) in tandem with a detailed discussion of theoretical justification, methodological underpinning, and technological capabilities of the approach.
This document provides an overview of social network analysis from online behavior and data mining techniques. It discusses classical social network analysis approaches and frameworks. Various types of social network analysis are described, including whole network analysis and personal network analysis. Models and measures used in social network analysis are also outlined.
This document provides a history of network visualization from its origins in the 18th century to modern applications. It discusses:
- Early work by Euler and kinship diagrams that relied on visual representations to study networks.
- J.L. Moreno's sociograms in the 1930s that sparked interest in social network analysis through visualizations.
- Developments through the 20th century as researchers sought to move from artistic to more scientific visualizations.
- Challenges of visualizing large, complex networks while representing multiple node and edge attributes simultaneously.
- Benefits of visualization in making invisible network structures visible and providing multi-dimensional insights beyond metrics alone.
This document summarizes a research paper about factors that predict whether users will unfollow other users on Twitter. The researchers analyzed structural properties of 911 Twitter users' social networks, such as number of followers, reciprocity of follows, and common connections. They found that reciprocity, network density, prestige ratio, follow-back rate, and number of common neighbors strongly predicted whether ties would be broken through unfollowing. The paper provides more detailed analysis using multi-level logistic regression to model how network structure impacts breaking ties on Twitter.
This document discusses network data collection. It begins by providing examples of how social structure matters and influences outcomes. It then discusses different ways to detect social structure through network data collection, including small group questionnaires, large surveys, observations, and digital data scraping. The document outlines key network questions that can shape data collection, such as how networks form and their consequences. It also discusses sampling and defining network boundaries. Overall, the document provides an overview of network data collection methods and considerations.
Preso on social network analysis for rtp analytics unconferenceBruce Conner
Selected highlights of Coursera Social Networking course, taught by Prof. Lada Adamic of the Univ. of Michigan. Presented at the annual Annual RTP Analytics Unconference, May 4, 2013
This document discusses social network analysis (SNA), a tool used to analyze social relationships and networks. SNA elicits, analyzes, and visualizes how actors interact and resources move through a network. It represents actors as nodes and their relationships as ties. The document provides examples of SNA's application in education, health, and agriculture. It outlines the process of conducting SNA through workshops or surveys to collect node attribute and tie/link data, which is then analyzed using software to visualize the network. The document suggests opportunities to further develop SNA, such as presenting networks back to communities and measuring social capital.
Network Data Collection
The document discusses collecting social network data. It covers three main topics:
1) Introduces social network analysis and why networks are important in social science. Networks matter because of connections that allow diffusion and because positions in networks influence roles and behavior.
2) Discusses research design considerations for collecting network data, including specifying relations of interest based on theoretical mechanisms, boundary selection, and sampling approaches.
3) Addresses accuracy of network survey data and how to handle inaccurate or missing data. The goal is to systematically understand connections between actors using empirical network data and analysis methods.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
This document discusses community detection in networks. Community detection aims to identify tightly knit groups within networks. The document outlines popular community detection algorithms like modularity maximization and stochastic block models. It also discusses applications of community detection to multilayer networks and examples like congressional voting networks and Facebook networks. Community detection is a useful tool for exploring network structure and identifying essential features in data.
Incremental Community Mining in Location-based Social NetworkIJAEMSJORNAL
A social network can be defined as a set of social entities connected by a set of social relations. These relations often change and differ in time. Thus, the fundamental structure of these networks is dynamic and increasingly developing. Investigating how the structure of these networks evolves over the observation time affords visions into their evolution structure, elements that initiate the changes, and finally foresee the future structure of these networks. One of the most relevant properties of networks is their community structure – set of vertices highly connected between each other and loosely connected with the rest of the network. Subsequently networks are dynamic, their underlying community structure changes over time as well, i.e they have social entities that appear and disappear which make their communities shrinking and growing over time. The goal of this paper is to study community detection in dynamic social network in the context of location-based social network. In this respect, we extend the static Louvain method to incrementally detect communities in a dynamic scenario following the direct method and considering both overlapping and non-overlapping setting. Finally, extensive experiments on real datasets and comparison with two previous methods demonstrate the effectiveness and potential of our suggested method.
The document discusses assortativity and dissortativity in complex networks. It defines assortativity as "nodes with similar degree connect preferably" and dissortativity as "nodes with low degree try to connect with highly connected nodes." Several studies are summarized that examine assortativity in social networks like Twitter. Happiness was found to be assortative in Twitter reciprocal reply networks. Distance from one's typical location on Twitter was also found to correlate with expressed happiness levels. Assortative networks are described as being more resilient to attacks while disassortative networks are more vulnerable.
Presentation of the paper:
Evaluating role mining algorithms. In Proceedings of the 14th ACM symposium on Access control models and technologies (SACMAT '09)
Ian Molloy, Ninghui Li, Tiancheng Li, Ziqing Mao, Qihua Wang, and Jorge Lobo. 2009.
ACM, New York, NY, USA, 95-104. DOI=10.1145/1542207.1542224 http://doi.acm.org/10.1145/1542207.1542224
[After Going Live Studio] Software archaeologyGlobant
The document discusses Software Archaeology, which is defined as the systematic study of past software systems through analyzing remaining evidence such as code, tests, and documentation. It is presented as a method for evolving existing applications built by others. Software Archaeology involves an archeologist first making contact and studying the evidence to understand the application's "culture". They then do an excavation by deeply analyzing the code and functionality to gain full knowledge of the systemic context. Finally, the archeologist analyzes the excavation results to develop a plan and recommended approach for further development by a team that understands the system context like the original developers.
Article review : Does Game-Based Learning Work? Results from Three Recent Stu...Nurnabihah Mohamad Nizar
1. The document summarizes three recent studies on the effectiveness of game-based learning. The studies found that students who used video games as a learning aid performed better on tests compared to students who did not use games.
2. The studies involved over 2,000 students across business, economics, and management courses. Students who used games scored higher and had higher rates of A's compared to students who did not use games.
3. The researcher employed different statistical tests for each study - t-tests for business students and ANOVA tests for management students - and appropriately tailored the research methods to each course. The findings provide evidence that game-based learning can improve academic achievement.
This document discusses slavery in early U.S. history and how the condition of Africans changed over time. It provides statistics on slave numbers, excerpts from slave narratives, and asks how the experience of being a slave would affect someone after emancipation. The central questions are about why slavery existed and how and why the condition of Africans in America changed over centuries.
Casro Presentation Project And Change Management 1st June 2011sam_inamdar
This presentation shares experiences from a collaborative approach to creating innovative solutions through technology, including insights on management of a technology project life cycle; using tracking tools for change management; managing communications for a virtual team; and other means to
foster collaboration.
The document outlines a proposed program called HighScholar that aims to better align university education with industry needs by having companies recruit and partially fund students' education in exchange for commitments around course of study, internships, and post-graduation employment. It describes how students and companies would participate through application and selection processes, and what funding and commitment terms might look like to address issues around rising education costs, uncertain career prospects, and skills gaps. The goal is to give students clearer career paths while helping industries access a more tailored workforce.
I have two pets, a dog named Buddy and a cat named Mittens. Buddy is a golden retriever who loves to play fetch in the backyard. Mittens is a black cat who enjoys napping in the sun all day. I enjoy spending time with my furry companions every day.
This document discusses McDonald's marketing campaigns between 2008-2011 that generated billions of media impressions. It describes campaigns promoting the Big Mac's 40th anniversary through a remix contest on MySpace, the "Give A Hand" widget on Facebook that raised money for Ronald McDonald House Charities, and the "What Came First" viral campaign around the launch of Southern Style Chicken. It also discusses the McDonald's Moms program that shared mothers' experiences visiting McDonald's facilities, generating over 275 million impressions and improving consumers' views of McDonald's food quality. Finally, it mentions the McDonald's Champion Kids program at the 2008 Beijing Olympics that gave children exclusive Olympic access and the 2011 McDonald's All American basketball games that raised awareness for Ronald McDonald House Char
What You Will Learn: Find out more about the Magic Software 360º Customer View. We will show attendees how to streamline their business data flow and integrate JD Edwards financial tools with the modern age tech of SugarCRM.
In this webinar, we will discuss...
• The 360º Customer View
• The SugarCRM & JD Edwards Integration
• Demo
Dokumen tersebut merangkum tentang gerakan sosial edukasi FLP (Forum Lentera Pena) yang didirikan pada tahun 1997 di UI oleh Helvy Tiana Rosa, Asma Nadia, dan Maimon H. FLP bergerak dalam meningkatkan minat membaca dan menulis serta memperbaiki kualitas hidup masyarakat melalui berbagai program seperti workshop menulis, sayembara menulis, penerbitan buku, dan kerja sama dengan berbagai pihak. FLP tel
A general stochastic information diffusion model in social networks based on ...IJCNCJournal
Social networks are an important infrastructure for information, viruses and innovations propagation. Since users’
behavior has influenced by other users’ activity, some groups of people would be made regard to similarity of users’
interests. On the other hand, dealing with many events in real worlds, can be justified in social networks; spreading
disease is one instance of them. People’s manner and infection severity are more important parameters in
dissemination of diseases. Both of these reasons derive, whether the diffusion leads to an epidemic or not. SIRS is a
hybrid model of SIR and SIS disease models to spread contamination. A person in this model can be returned to
susceptible state after it removed. According to communities which are established on the social network, we use the
compartmental type of SIRS model. During this paper, a general compartmental information diffusion model would
be proposed and extracted some of the beneficial parameters to analyze our model. To adapt our model to realistic
behaviors, we use Markovian model, which would be helpful to create a stochastic manner of the proposed model.
In the case of random model, we can calculate probabilities of transaction between states and predicting value of
each state. The comparison between two mode of the model shows that, the prediction of population would be
verified in each state.
Information Contagion through Social Media: Towards a Realistic Model of the ...Axel Bruns
Paper by Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, Felix Münch, Lucia Falzon, and Lucy Resnyansky presented at the ACSPRI 2016 conference, Sydney, 19-22 July 2016/
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Assessment of the main features of the model of dissemination of information ...IJECEIAES
Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.
Multimode network based efficient and scalable learning of collective behaviorIAEME Publication
This document discusses multimode network-based approaches for efficiently learning collective behavior in large social networks. It provides an overview of existing approaches for predicting collective behavior based on the behaviors of connected individuals. Specifically, it describes methods that extract social dimensions from networks to represent affiliations between actors and then apply supervised learning to determine which dimensions are informative for behavior prediction. However, existing approaches do not scale well to networks with millions of actors. The document proposes a new edge-centric clustering approach to extract sparse social dimensions, enabling the efficient handling of very large networks while maintaining predictive performance.
This document discusses the use of social network graphs and analytics. It provides an overview of key concepts in social network analysis (SNA) including representing social networks, identifying strong and weak ties, central nodes, and overall network structure. Examples are given of how SNA is used in business, law enforcement, social media sites, and more. Key measures discussed include degree, betweenness, closeness, eigenvector centrality, density, and clustering coefficient. The small-world phenomenon and preferential attachment are also covered.
Network literacy is important as our world becomes increasingly connected through networks. A basic understanding of how networks work and their benefits and limitations is needed for all people living in today's networked world. Networks exist in every aspect of life, from communication systems and social networks to biological and economic networks. Understanding networks can help reveal patterns and allow comparisons across different types of systems. While networks are ubiquitous, the study of networks is currently absent from educational systems. This document provides an introduction to network literacy through seven essential concepts.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
This document provides an introduction to social network analysis. It discusses how network analysis allows us to understand social connections and positions. There are two key mechanisms through which networks can impact outcomes: connections, where networks matter because of what flows through them, and positions, where networks capture roles and social exchange. Network analysis provides tools to empirically study patterns of social structure by mapping relationships between actors.
A computational model of computer virus propagationUltraUploader
1) A computational model is developed to simulate the propagation of computer viruses and warning messages within organizational social and computer networks.
2) The model represents the networks as graphs and incorporates mechanisms of virus propagation, node state transitions, and the dissemination of warning messages.
3) Experiments show that random graphs with similar characteristics to real-world networks can model social networks, and isolating organizations may prevent virus infection but also limit receipt of important warning messages.
Epidemiological Modeling of News and Rumors on TwitterParang Saraf
Abstract: Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
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2. advertising through social media. However, practitioners provide introductory summary statistics that describe the
and researchers in this area can also benefit from empirical chains of diffusion that result. Unlike previous empirical
models of how information spreads in social networks. work, the data we present include every user exposure and
Models starting from an empirical base typically involve cover millions of individual diffusion events. We assess
an algorithm that predicts the observed behavior of infor- the conditions under which large cascades occur, with a
mation propagation. These are commonly developed using particular focus on analyzing the number of nodes respon-
blog or web data due to the availability of accurate infor- sible for triggering chains of adoption and the typical
mation regarding links and the time at which data was length of these chains. We then provide a detailed analysis
posted or transmitted. Analysis of link cascades (Leskovec of 179,010 “chain starters” over a six-month period start-
et al. 2007) and information diffusion (Gruhl et al. 2004) in ing on February 19, 2008 and investigate how their demo-
blogs, as well as photo bookmarking in Flickr (Cha et al. graphics and Facebook usage patterns may predict the
2008), can all be modeled by variations on standard epi- length of the diffusion chains that they initiate.
demiological methods. Models of social movement partici-
pation and contribution to collective action (Gould 1993;
Oliver, Marwell, and Teixeira 1985) are often tied to an Mechanics of Facebook Page Diffusion
empirical foundation, but with some exceptions (Macy
1991; Oliver, Marwell, and Prahl 1998) these do not focus Diffusion on Facebook is principally made possible by
as strongly on network properties and cascades. News Feed (Figure 1), which appears on every user’s
In most network models of diffusion, the contagion is homepage and surfaces recent friend activity such as pro-
triggered by a fairly small number of sources. In some file changes, shared links, comments, and posted notes. An
cases (e.g. Centola and Macy 2007; Watts and Strogatz important feature of News Feed is that it allows for passive
1998), the models are explicitly designed to assess the im- information sharing, where users can broadcast an action to
pact of an isolated event on the network as a whole. In their entire network of friends through News Feed (instead
other cases, such as in blog networks, the way information of active sharing methods such as a private message, where
is introduced lends itself to scenarios where one or a few a user picks a specific recipient or recipients). These stories
sources may trigger a cascade. This start condition there- are aggregated and filtered through an algorithm that ranks
fore makes sense both in models focused on the endoge- stories based on social and content-based features, then
nous effects of diffusion and in models based on empirical displayed to the friends along with stories from other users
scenarios in which a few nodes initiate cascades. However, in their networks.
it does create a particular conceptualization of how diffu- To analyze how ideas diffuse on Facebook, we concen-
sion processes work. At the basis of these models is the as- trate on the News Feed propagation of one particularly vi-
sumption that a small number of nodes triggers a large ral feature of Facebook, the Pages product. Pages were
chain-reaction, which is observed as a large cascade. This originally envisioned as distinct, customized profiles de-
assumption may not hold in social media systems, where signed for businesses, bands, celebrities, etc. to represent
diffusion events are often related to publicly visible pieces themselves on Facebook.
of content that are introduced into a particular network
from many otherwise disconnected sources. Information
will not necessarily spread through these networks via
long, branching chains of adoption, but may instead exhibit
diffusion patterns characterized by large-scale collisions of
shorter chains. Some evidence of this effect has already
been observed in blog networks (Leskovec et al. 2007).
Even if this initial assumption is valid and cascades in
social media are started by a tiny fraction of the user popu-
lation, these models typically lack external empirical vali-
dation. Accurate data on social network structures and
adoption events are difficult to collect and has not been
readily available until fairly recently. In the cases where
diffusion data have been tracked, it typically does not in-
clude the fine-grained exposure data necessary to fully
document diffusion. Given the earlier data limitations fac-
ing empirical studies of diffusion, the primary goal of this
study is to provide a detailed empirical description of large
cascades over social networks.
Figure 1. Screenshot of News Feed, Facebook’s principal method
Using data on 262,985 Facebook Pages, this paper pre-
of passive information sharing.
sents an empirical examination of large cascades through
the Facebook network. We first describe the process of
Page diffusion via Facebook’s News Feed, after which we
147
3. Since the original rollout in late 2007, hundreds of thou-
sands of Pages have been created for almost every con-
ceivable idea. Pages are made by corporations who wish to
establish an advertising presence on Facebook, artists and
celebrities who seek a place to interact with their fans, and
regular users who simply want to create a gathering place
for their interests.
Users interact with a Page by first becoming a “fan” of
the Page; they can then post messages, photos, and various
other types of content depending on the Page’s settings. As
of March 2009, the most popular Facebook Page was Ba-
rack Obama’s Page,2 with over 5.7 million fans.
When users become fans of a particular Page, their ac-
tion may be broadcast to their friends’ News Feeds (see
Figure 2). An important exception is that the users may
elect to delete the fanning story from their profile feed
(perhaps to reduce clutter on their profile, or because they
do not want to draw too much attention to their action). In Figure 3. Empirical CDFs of Page fanning over time for a
this case, the story will not be publicized on their friends’ random sample of 20 Pages.
News Feeds.
Diffusion of Pages occurs when 1) a user fans a Page; 2) users may see multiple friends perform a Page fanning ac-
this action is broadcast to their friends’ News Feeds; and 3) tion in a single News Feed story. For example, Charlie may
one or more of their friends sees the item and decides to see the following News Feed story: “Alice and Bob be-
become a fan as well. came a fan of Page XYZ.” In this case, Charlie’s node on
the tree would have two parents. Furthermore, if Alice and
Bob were on separate diffusion chains, the two chains have
now merged. We extract every such chain from server logs
that record fanning events and News Feed impressions for
all of the Pages in our sample.
Figure 2. Sample News Feed item of Page fanning.
Without News Feed diffusion, we might expect that
Pages acquire their fans at a roughly linear pace. However,
since Facebook users are so active, actions such as Page
fanning are quickly propagated through the network. As a
result, we see frequent spikes of Page fanning, presumably
driven by News Feed.
To motivate our subsequent analyses, Figure 3 shows Figure 4. A possible diffusion chain of length 1.
the empirical cumulative distribution function of Page fan
acquisition over time (the x-axis) for a random sample of We infer links of associations based on News Feed im-
20 Pages. Each graph starts at the Page creation date and pressions of friends’ Page fanning activity: if a user Ua saw
ends at the end of the sample period (August 19, 2008). that friend Ub became a fan of Page P within 24 hours prior
From just this small sample of Pages, we see that there is of Ua becoming a fan, we record an edge from the follower
no obvious pattern; clearly, Pages acquire their fans at Ub to the actor Ua. The 24-hour window is a logical break-
highly variable rates. This paper will provide some insight point that allows us to account for various methods of Page
into this phenomenon after the following section, which fanning (e.g., clicking a link on the News feed, navigating
provides a more detailed description of the data used in the to the Page and clicking “Become a Fan,” etc.) without be-
analysis. ing overly optimistic in assigning links. As we create larger
trees, some users (i.e., fans in the middle of a chain) may
become both actors and followers; some users may be ac-
Data tors but not followers (the chain-starters); and some users
In this paper, we analyze Page data by creating trees that can be followers but not actors (the leaves of the chains).
link actors and followers for each Page on Facebook. We Our dataset consists of all Facebook Pages created be-
measure diffusion via levels of a chain, as shown in Figure tween February 19, 2008 and August 19, 2008, and all of
4. An important note is that due to News Feed aggregation, their associated fans (as of August 19, 2008). These data
include 262,985 Pages that contained at least one diffusion
2
http://www.facebook.com/barackobama event. Because any Facebook user can create a Page about
any topic, there are a large number of Pages with just a few
148
4. fans. We will therefore limit our discussion to the subsets Date
% in
% Single- Max Chain
of these Pages that allow us to present more meaningful Page Name # Nodes Biggest
Created tons Length
Cluster
summary statistics. The
Previous cascade models typically have an assumption 6/7 7,936 55.38% 14.37% 57
Goonies
of a “global cascade,” which involves the finite subgraph City of
of susceptible individuals on an infinite graph (Watts 4/6 9,277 20.91% 54.97% 23
Chicago
2002). Our data represent the entire network of people who Fudd-
3/17 10,269 43.71% 36.30% 27
became a fan of a given Page before our cutoff date of ruckers
August 19, 2008. Since these data do not present a clear Tom Cruise 5/28 28,592 56.67% 28.44% 41
definition of susceptibility, we assume that for popular
Pages, the susceptible population consists of those that Usain Bolt 6/1 33,967 37.03% 31.28% 14
have adopted; in other words, for comparison’s sake, we Damian
assume these diffusion events are large enough to be con- 3/24 40,594 21.05% 45.91% 23
Marley
sidered global cascades. Stanley
3/21 41,620 28.82% 40.30% 28
Kubrick
Chains Dataset Cadbury 3/18 57,011 76.50% 16.39% 44
In addition to our general Pages data, we wish to observe Zinedine
the characteristics of chain length variation for different 2/24 76,624 59.31% 25.95% 55
Zidane
chain-starters. Due to the computational complexity of this NPR 2/20 93,132 24.72% 33.63% 34
particular analysis, we study this phenomenon by creating
a chains dataset of 10 Pages and all 399,022 of their asso- Table 1. Summary statistics for the chains dataset.
ciated fans as of August 19, 2008 (of the 399,022 fans,
179,010 were chain-starters and 220,012 were followers). (squares) and Slovenia (triangles). A few fans serve as the
Each of these Pages was at least 40 days old as of the end “bridge” that brings the two groups together. A third clus-
ter of Croatian fans (diamonds, shown in the bottom-right
of the analysis period and had at least 7,500 fans at that
cluster) hasn’t yet found its connecting bridge. Finally,
point. Given these criteria, a set of Pages were randomly
there are a few fans from other countries (circles) scattered
selected and filtered to remove overlapping subjects and within the two large clouds, perhaps Bosnian and Slove-
unrecognizable/foreign content that would be difficult for nian expatriates!
the authors to interpret. For each of these Pages, we
gathered all of their associated fans and calculated the
maximum chain length for each fan that started chains. We
also collected various user-level features, such as age, gen-
der, friend count, and various measures of Facebook activ-
ity. All data were analyzed in aggregate, and no personally
identifiable information was used in the analysis. Table 1
shows the summary statistics for these Pages.
The next section discusses an analysis of the “large-
clusters” phenomenon using the global Pages dataset. We
then present an examination of the maximum chain length
for each chain starter using the smaller chains dataset.
Large-Clusters Phenomenon
When the process described in Figure 4 is allowed to con-
tinue on a large scale, the result is that a flurry of chains,
all started by many people acting independently, often
merges together into one huge group of friends and ac-
quaintances. This merging occurs when one person fans a
Page after seeing two or more friends (who are on separate
chains) fan that same Page.
A case in point is a Page devoted to a popular European
cartoon, Stripy.3 The diagram in Figure 5 shows the car-
toon's close-knit communities of fans in both Bosnia Figure 5. Trees of diffusion for the Stripy Facebook Page.
3
http://www.facebook.com/pages/Stripy/25208353981
149
5. In fact, for some popular Pages (not part of our chains Prediction of Maximum Chain Length
dataset), more than 90% of the fans can be part of a single
group of people who are all somehow connected to one an- Knowing that a large percentage of a Page’s fans start
other. Typically, each of these close-knit communities con- page-fanning chains, we wish to further investigate what
tains thousands of separate starting points—individuals qualities separate these individuals from those that adopt
who independently decide to fan a particular Page. via diffusion. Specifically, we wish to investigate the ques-
As an example, as of August 21, 2008, 71,090 of 96,922 tion: given the demographic and Facebook usage statistics
fans (73.3%) of the Nastia Liukin (an American Olympic of each start node, can we predict the node’s maximum
gymnast) Page were in one connected cluster. Because the chain length?
Beijing Olympics were going on at the time, there was a Table 2 presents some summary statistics of our chains
high latent interest in the Page. So, users were highly likely dataset to get better acquainted with the users that start
to fan the Page if a friend alerted them to its existence via chains. In our chains dataset, starters make up 46.32% of
News Feed’s passive sharing mechanism. the users in the full dataset and 16.91% of the users in the
This large-cluster effect is widespread, especially for re- largest cluster of each Page.
cent Pages (it is more likely for older Pages to have distinct
waves of fanning): for Pages created after July 1, 2008, for Starters Non-Starters
example, the median Page had 69.48% of its fans in one Mean SD Mean SD
connected cluster as of August 19, 2008. A natural ques- Age 24.65 9.82% 24.07 9.00%
tion is to wonder how these large clusters come about: are Male 53.29% 55.60%
these clusters started by a very small percentage of the Facebook-age 411.44 342.94 408.64 276.23
nodes, as is commonly assumed in the literature? Or does a Friend-count 251.38 270.11 198.16 176.11
different pattern emerge? Activity-count 1.93 8.80 1.54 7.65
We analyze these data by looking at each Page and cal- Table 2. Summary statistics for the chains dataset. All differences
culating the size of each cluster of fans. Each cluster con- are statistically significant using a two-sample t-test.
sists of chains that have merged via the aggregated News
Feed mechanism described earlier. For many Pages, the Facebook-age denotes how long the user has been a
size of the largest cluster is orders of magnitude larger than member of Facebook, in days. Activity-count is a proxy to
the second-largest cluster: in the Nastia Liukin example, user activity, combining the total number of messages,
the second-largest connected cluster (after the 71,090-fan photos, and Facebook wall posts added by the user.
connected cluster) has only 30 fans. We see that starters and non-starters have fairly similar
After looking at the distribution of “start nodes” and statistics. However, start nodes tend to have more Face-
“follower nodes” in these clusters, we find no evidence to book friends than their non-starter counterparts and have
support the theory that just a few users are responsible for slightly larger Facebook-ages. Furthermore, as evidenced
the popularity of Pages. Instead, across all Pages of mean- by the higher variation in activity_count, more of the start
ingful size (>1000 fans), an average of 14.8% (SD 7.9%) nodes are very active Facebook users. A likely explanation
of the fans in each Page’s biggest cluster were start nodes is that starters tend to be more frequent users of Facebook
(for Pages of under 1000 nodes, the effect is also present, (evidenced by their increased content production), so they
though variance increases). are more familiar with the interface and more likely to
Each of these fans arrived independently (presumably by search for new Pages to fan. Non-starters, on the other
searching for the Page via Facebook Search or from an ad- hand, are more passive users of Facebook and are thus less
vertisement) and started their own chains, which eventually likely to start diffusion chains.
merged together as the rest of the fan base took shape.
These patterns hold fairly consistently for Pages with a few Analysis of Start Nodes
thousand fans and for those with more than 50,000: for
For each of the 179,010 start nodes in our data, we cal-
Pages with 5000 fans or more, the average is 14.9% (SD
culate all the chains of diffusion and find each user’s
6.4%), and for Pages with 50,000 fans or more, the average
maximum-length chain. This value, max_chain, is the re-
rises to 17.1% (SD 4.8%).
sponse variable in our data. For the Pages in our dataset,
Chains merge frequently because nodes in the graph
values range from 0 to 56. The data are heavily skewed to
typically have more than one parent. For all Pages with at
the right, indicating the presence of many short chains. Se-
least 100 fans, the average node in the largest cluster for
lected percentiles of max_chain are given below.
each Page has a degree of 2.676 (SD 0.607). This figure
increases to roughly 3 when the number of fans increases
0%-67% 68% 75% 90% 95% 98%
beyond 1000.
0 1 1 3 5 10
The rest of this paper investigates the aforementioned
start nodes that begin chains of diffusion.
However, we know for a fact that there are excess zeros
in our max_chain data: if a user fans a Page and immedi-
150
6. ately deletes it from their profile feed, the story will no 1 2 3 4 5 6 7 8
longer be eligible for broadcasting to their friends’ News 1. max_chain 1.00 -0.07 0.00 0.05 0.00 0.17 0.28 0.04
Feeds, regardless of how popular the user is. Thus,
2. age -0.07 1.00 -0.06 0.11 0.07 -0.15 0.07 0.07
max_chain will always be exactly zero in this scenario.
Data on excess zeros are not available, so for illustrative 3. gender 0.00 -0.06 1.00 -0.03 -0.08 0.06 -0.01 -0.15
purposes we present selected percentiles of max_chain 4. facebook_age 0.05 0.11 -0.03 1.00 0.10 0.33 0.37 0.19
where all zeros have been deleted: 5. activity_count 0.00 0.07 -0.08 0.10 1.00 0.20 0.12 0.34
6. friend_count 0.17 -0.15 0.06 0.33 0.20 1.00 0.45 0.51
25% 50% 60% 75% 95% 98%
7. feed_exposure 0.28 0.07 -0.01 0.37 0.12 0.45 1.00 0.32
1 2 3 3 11 18
8. popularity 0.04 0.07 -0.15 0.19 0.34 0.51 0.32 1.00
Typically, Poisson regression is used to model count re- Table 3. Correlation matrix for model variables.
sponse variables. However, Poisson random variables are
expected to have a mean equal to its variance, which is We run a standard zero-inflation negative binomial
clearly not the case here (where the variance far exceeds (“ZINB”) model with starting values estimated by the ex-
the mean). Instead, we use negative binomial regression, pectation maximization (EM) algorithm. Standard errors
which is appropriate when variance >> mean. To correct are derived numerically using the Hessian matrix.
for the excess zeros, we use a zero-inflation correction. The ZINB coefficients for the pooled model (all 10
This procedure allows us, in a single regression, to select Pages) are shown in Table 4.
variables that contribute to the true content in the response
variable (“count model coefficients”) and also a (poten- Count model coefficients (negbin with log link):
tially different) set of variables that contribute to the excess Estimate Std. Error Pr(>|z|)
zeros in the response (“zero-inflation model coefficients”). (Intercept) 2.346007 0.083646 < 2e-16 ***
The response variables for the count model are: age -0.814130 0.016249 < 2e-16 ***
• log age gender==male -0.084873 0.010606 1.22e-15 ***
• gender Facebook_age -0.379611 0.010994 < 2e-16 ***
• log Facebook_age (number of days the user has been a activity_count -0.056424 0.007047 1.18e-15 ***
member of Facebook) friend_count 0.067955 0.008139 < 2e-16 ***
• log activity_count (messages sent + photos uploaded + feed_exposure 0.929996 0.005766 < 2e-16 ***
Facebook wall posts sent) popularity -0.206341 0.005110 < 2e-16 ***
• log friend_count (number of Facebook friends) Log(theta) -0.960615 0.007173 < 2e-16 ***
• log feed_exposure (number of friends who saw the News
Feed story broadcasting the user’s fanning action) Zero-inflation model coefficients (binomial with logit link):
• log popularity (number of friends that “care about” the Estimate Std. Error Pr(>|z|)
start node high enough that the News Feed algorithm con- (Intercept) 24.42513 1.91443 < 2e-16 ***
siders broadcasting the start node’s Page fanning story) age -0.06867 0.20232 0.73430
gender==male -0.18038 0.14023 0.19834
The variables age, gender, Facebook-age, and activity-
count are used for the zero-inflation model. We assume Facebook_age -5.31638 0.33802 < 2e-16 ***
that the number of friends and level of News Feed expo- activity_count -0.51408 0.17925 0.00413 **
sure would not impact the probability of deleting the Page ---
fanning story from a user’s profile feed. Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Table 3 presents the correlation matrix for these vari- Theta = 0.3827
ables. There is a fairly high correlation between Number of iterations in BFGS optimization: 1
friend_count, feed_exposure, and popularity, but it may Log-likelihood: -2.045e+05 on 14 Df
still be useful to include these in the model. Table 4. Coefficients for the ZINB model.
To ensure significance of our model, we run a likelihood
ratio test:
Df LogLik Df Chisq Pr(>Chisq)
1 14 -204532
2 3 -224739 -11 40414 < 2.2e-16 ***
151
7. Here, Model 1 is the ZINB model calculated earlier, and coefficient on log activity_count is -0.056424, which im-
Model 2 is max_chain regressed only on a constant term. plies that a 1% increase in activity_count is only expected
The p-value is < 0.001, ensuring that our model is signifi- to decrease the log of max_chain by 0.056%. We might
cant. However, we also wish to confirm that the ZINB expect that highly active users are more likely to have their
model is an improvement over a standard negative bino- News Feed actions ignored, but this is a trivial decrease
mial regression model with the same coefficients (that is, that is not realistically meaningful.
without the zero-inflation correction). When comparing the results from the pooled model with
This can be accomplished by running a standard nega- results from the individual Page models, we see that the
tive binomial regression (not shown here) and comparing only consistently significant effect is for feed_exposure,
the two models with the Vuong test (Vuong 1989). The which is a control for the number of friends who saw the
Vuong test gives a small p-value if the zero-inflated nega- News Feed story broadcasting the user’s fanning action.
tive binomial regression is a statistically significant im- This coefficient consistently hovers around 1, which indi-
provement over a standard negative binomial regression. cates that if the News Feed algorithm decides to publish
the user’s action to 1% more people, we would expect a
Vuong Non-Nested Hypothesis Test-Statistic:
7.602584 1% longer max_chain to result. This result holds even after
(test-statistic is asymptotically distributed controlling for distribution (via the popularity variable)
N(0,1) under the null that the models are and for the number of friends.
indistinguishible)
model1 > model2, with p-value 1.454392e-14
The most interesting finding seems to be that after con-
trolling for feed_exposure, the log friend_count variable
Model 1 is the ZINB model; model 2 is the regular nega- does not have a realistically meaningful coefficient
tive binomial model. The Vuong test reports a p-value < (0.067955, meaning that a 1% increase in friend_count is
0.001, so we conclude that the ZINB model is significant. only expected to increase the log of max_chain by
In Table 5, we present the count model coefficients for 0.068%). That is, after controlling for News Feed exposure
ZINB models on a selection of individual Pages from our variables, neither demographic characteristics nor number
chains dataset (coefficients significant at the 5% level are of Facebook friends seems to play an important role in the
in bold). For all of these regressions, the likelihood ratio prediction of maximum diffusion chain length.
test and Vuong tests were significant at the 5% level.
Variable Goon. Fudd Cruise Bolt Zidane Conclusions and Future Work
(Intercept) -0.097 2.574 1.419 0.596 -0.105 Our examination of Facebook Pages shows that large-
age 0.509 -0.537 -1.006 -0.386 -0.981 scale diffusion networks play a significant role in the
gender==male 0.186 -0.011 -0.076 -0.144 0.116 spread of Pages through Facebook’s social network. For
many Pages with large followings, the majority of fans oc-
Facebook_age -0.654 -0.522 -0.052 -0.415 0.153
cur in a single connected cluster of diffusion chains that
activity_count 0.019 -0.102 -0.142 -0.064 -0.100
merge together to form a global cascade. The structure of
friend_count -0.480 -0.220 0.087 -0.023 0.009 these clusters reveals several important aspects of the em-
feed_exposure 1.379 1.279 1.008 0.860 1.053 pirical nature of global cascades. First, we find that within
popularity 0.084 -0.014 -0.245 0.021 -0.120 most clusters, roughly 14-18% of the nodes are chain ini-
Table 5. Count model coefficients for selected Pages. tiators, which differs from the more restricted start condi-
tions generally assumed in the theoretical literature. While
Analysis of Chains Regressions it is easier to discuss theoretical underpinnings of a global
The zero-inflation model coefficients represent the in- cascade by disallowing exogenous diffusion, we find that
creased probabilities of zero-inflation; that is, the probabil- this may not lead to a completely accurate conceptualiza-
ity that a user immediately removes the Page fanning story tion of the media diffusion observed in our studies.
from their profile and constrains their max_chain to zero. We also find that because of the connectivity of the
Facebook network and the ease of Page fanning, the
Since this is not a focus of this paper, we concentrate in-
maximum length of diffusion chains from initiator nodes
stead on the count model coefficients.
can sometimes be extremely long, especially in comparison
We note that in the pooled model, all count model coef- to the diffusion chains that have been observed in other
ficients are highly statistically significant. However, this is empirical studies of real-world phenomena. In fact, we
not surprising given the large sample sizes observed here. have observed chains of up to 82 levels in our complete
If we run the same ZINB model using data from each Page dataset. It may be interesting for marketers and practitio-
separately, we find that the signs on every variable fre- ners to note that when compared to real-life studies of dif-
quently flip from positive to negative, with the exception fusion, Facebook chains of Page fanning tend to be longer-
of feed_exposure. Furthermore, our coefficients from the lasting and involve more people: in a study of word-of-
pooled model are generally quite small: for example, the mouth diffusion of piano teachers, Brown and Reingen
152
8. (1987) found that only 38% of paths involved at least four structure and dynamics of the Pages diffusion network to
individuals. Using the same definition on our data, we find other viral features on Facebook, such as Notes (as illus-
that for a random sample of 82,280 Pages, 86.4% of paths trated by the recent “25 Things About Myself” meme, for
of Page diffusion involve at least four individuals. This re- example) and Groups, where diffusion may occur both due
sult may be useful for potential advertisers considering a to News Feed and due to active propagation (i.e., users
Facebook marketing campaign versus more traditional may send invitations to join a group).
word-of-mouth methods.
The properties of these diffusion clusters on Facebook
suggest a new characterization of global cascades: whereas References
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