DOCSLIB.ORG

Virality Prediction and Community Structure in Social Networks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Virality Prediction and Community Structure in Social Networks Virality Prediction and Community Structure in Social Networks Yong-Yeol “YY” Ahn @yy Most populated countries 1,300,000,000+ 1,200,000,000+ 300,000,000+ Most populated countries 1,300,000,000+ 1,200,000,000+ 1,100,000,000+ 500,000,000+ 300,000,000+ Social media can amplify messages. “Casually pepper spraying everything cop” meme Companies are desperately trying to leverage social media to make their products and ads viral. Original, useful ideas - hard “viral marketing” ‘Astroturfing’ may change election results. “1/3 of online reviews may be fake.” - Bing Liu (UIC) How can we understand virality? Can we predict viral memes? Clue #1: Complex contagion Memes = Infectious diseases? Germs spread through the “social” network Memes, ideas and behaviors also spread through social network. Memes = Infectious diseases? Maybe not. “Large” world “Small” world D. Centola, Science 2010 Which network is better at spreading information quickly? “Large” world “Small” world D. Centola, Science 2010 Multiple exposures are crucial. Social reinforcement Complex Contagion 三人成虎 三人成虎 three 三人成虎 three people construct 三人成虎 three people construct 三人成虎 three people tiger ? ? ! Complex Contagion needs multiple exposures. & Social contagion seems to be complex contagions. Clustering should be important. (B) Social Reinforcement Multiple Exposures Multiple Exposures A High Clustering Low Clustering (D) Retweet Network (E) Follower Network Figure 1: The importance of community structure in the spreading of social contagions. (A) Structural trapping: dense communities with few outgoing links naturally trap informa- tion flow. (B) Social reinforcement: people who have adopted a meme (black nodes) trigger multiple exposures to others (red nodes). In the presence of high clustering, any additional adoption is likely to produce more multiple exposures than in the case of low clustering, in- ducing cascades of additional adoptions. (C) Homophily: people in the same community (same color nodes) are more likely to be similar and to adopt the same ideas. (D) Diffusion structure based on retweets among Twitter users sharing the hashtag #USA. Blue nodes represent English users and red nodes are Arabic users. Node size and link weight are proportional to retweet ac- tivity. (E) Community structure among Twitter users sharing the hashtags #BBC and #FoxNews. Blue nodes represent #BBC users, red nodes are #FoxNews users, and users who have used both hashtags are green. Node size is proportional to usage (tweet) activity, links represent mutual following relations. 10 Highly clustered structure? Communities! Newman, 2006 Communities should enhance the spread of complex contagion. Clue #2: Homophily “Birds of a feather flock together.” Eric Fischer, Race and ethnicity Adamic & Glance, 2005 You are likely to share similar interests with your friends. You are more likely to adopt something from your social circles. Again, communities become important. A community ~ a common characteristic or shared interests Adamic & Glance, 2005 (C) Homophily (E) Follower Network Figure 1: The importance of community structure in the spreading of social contagions. (A) Structural trapping: dense communities with few outgoing links naturally trap informa- tion flow. (B) Social reinforcement: people who have adopted a meme (black nodes) trigger multiple exposures to others (red nodes). In the presence of high clustering, any additional adoption is likely to produce more multiple exposures than in the case of low clustering, in- ducing cascades of additional adoptions. (C) Homophily: people in the same community (same color nodes) are more likely to be similar and to adopt the same ideas. (D) Diffusion structure based on retweets among Twitter users sharing the hashtag #USA. Blue nodes represent English users and red nodes are Arabic users. Node size and link weight are proportional to retweet ac- tivity. (E) Community structure among Twitter users sharing the hashtags #BBC and #FoxNews. Blue nodes represent #BBC users, red nodes are #FoxNews users, and users who have used both hashtags are green. Node size is proportional to usage (tweet) activity, links represent mutual following relations. 10 Trivial example: Language and Country (E) Follower Network English #usa BBC News #bbc Fox News #foxnews Arabic #usa FigureFigureRetweet 1: 1: The The importance importance Network of community of community structure structure in theFollower in spreading the spreading of social Network of contagions. social contagions. (A) Structural trapping: dense communities with few outgoing links naturally trap informa- (A) Structural trapping: dense communities with few outgoing links naturally trap informa- tion flow. (B) Social reinforcement: people who have adopted a meme (black nodes) trigger tion flow. (B) Social reinforcement: people who have adopted a meme (black nodes) trigger multiple exposures to others (red nodes). In the presence of high clustering, any additional multiple exposures to others (red nodes). In the presence of high clustering, any additional adoption is likely to produce more multiple exposures than in the case of low clustering, in- ducingadoption cascades is likely of additional to produce adoptions. more (C) multipleHomophily exposures: people than in the in the same case community of low clustering, (same in- colorducing nodes) cascades are more of likely additional to be similar adoptions. and to (C) adoptHomophily the same: people ideas. in(D) the Diffusion same community structure (same basedcolor on retweets nodes) are among more Twitter likely users to be sharing similar the and hashtag to adopt#USA the. Blue same nodes ideas. represent (D) Diffusion English structure usersbased and red on retweets nodes are among Arabic Twitter users. Node users size sharing and link the hashtagweight are#USA proportional. Blue nodes to retweet represent ac- English tivity.users (E) and Community red nodes structure are Arabic among users. Twitter Node users size sharing and link the weight hashtags are#BBC proportionaland #FoxNews to retweet. ac- Bluetivity. nodes (E) represent Community#BBC users, structure red among nodes are Twitter#FoxNews usersusers, sharing and the users hashtags who have#BBC usedand both#FoxNews. hashtagsBlue nodesare green. represent Node#BBC size isusers, proportional red nodes to usage are #FoxNews (tweet) activity,users, and links users represent who have mutual used both followinghashtags relations. are green. Node size is proportional to usage (tweet) activity, links represent mutual following relations. 10 10 Both clues indicate that Communities should play a crucial role in complex contagion. Communities weakly trap simple contagions. Communities strongly trap complex contagions. Communities: traps for random walkers Rosvall, Bergstrom, Lambiotte, ... (A) Structural Trapping Exposures (D) Retweet Network Affects both simple and complex contagions. Figure 1: The importance of community structure in the spreading of social contagions. (A) Structural trapping: dense communities with few outgoing links naturally trap informa- tion flow. (B) Social reinforcement: people who have adopted a meme (black nodes) trigger multiple exposures to others (red nodes). In the presence of high clustering, any additional adoption is likely to produce more multiple exposures than in the case of low clustering, in- ducing cascades of additional adoptions. (C) Homophily: people in the same community (same color nodes) are more likely to be similar and to adopt the same ideas. (D) Diffusion structure based on retweets among Twitter users sharing the hashtag #USA. Blue nodes represent English users and red nodes are Arabic users. Node size and link weight are proportional to retweet ac- tivity. (E) Community structure among Twitter users sharing the hashtags #BBC and #FoxNews. Blue nodes represent #BBC users, red nodes are #FoxNews users, and users who have used both hashtags are green. Node size is proportional to usage (tweet) activity, links represent mutual following relations. 10 (B) Social Reinforcement Multiple Exposures Multiple Exposures A High Clustering Low Clustering (D) Retweet Network (E) Follower Network Affects complex contagions. Figure 1: The importance of community structure in the spreading of social contagions. (A) Structural trapping: dense communities with few outgoing links naturally trap informa- tion flow. (B) Social reinforcement: people who have adopted a meme (black nodes) trigger multiple exposures to others (red nodes). In the presence of high clustering, any additional adoption is likely to produce more multiple exposures than in the case of low clustering, in- ducing cascades of additional adoptions. (C) Homophily: people in the same community (same color nodes) are more likely to be similar and to adopt the same ideas. (D) Diffusion structure based on retweets among Twitter users sharing the hashtag #USA. Blue nodes represent English users and red nodes are Arabic users. Node size and link weight are proportional to retweet ac- tivity. (E) Community structure among Twitter users sharing the hashtags #BBC and #FoxNews. Blue nodes represent #BBC users, red nodes are #FoxNews users, and users who have used both hashtags are green. Node size is proportional to usage (tweet) activity, links represent mutual following relations. 10 (C) Homophily (E) Follower Network Affects complex contagions. Figure 1: The importance of community structure in the spreading of social contagions. (A) Structural trapping: dense communities with few outgoing links naturally trap
Load more

Recommended publications
  • Homophily and Polarization in the Age of Misinformation
    Eur. Phys. J. Special Topics 225, 2047–2059 (2016) © EDP Sciences, Springer-Verlag 2016 THE EUROPEAN DOI: 10.1140/epjst/e2015-50319-0 PHYSICAL JOURNAL SPECIAL TOPICS Regular Article Homophily and polarization in the age of misinformation Alessandro Bessi1,2, Fabio Petroni3, Michela Del Vicario2, Fabiana Zollo2, Aris Anagnostopoulos3, Antonio Scala4, Guido Caldarelli2,and Walter Quattrociocchi1,a 1 IUSS, Pavia, Italy 2 IMT Institute for Advanced Studies, Lucca, Italy 3 Sapienza University, Roma, Italy 4 ISC-CNR, Roma, Italy Received 1 December 2015 / Received in final form 30 January 2016 Published online 26 October 2016 Abstract. The World Economic Forum listed massive digital misin- formation as one of the main threats for our society. The spreading of unsubstantiated rumors may have serious consequences on public opin- ion such as in the case of rumors about Ebola causing disruption to health-care workers. In this work we target Facebook to characterize information consumption patterns of 1.2 M Italian users with respect to verified (science news) and unverified (conspiracy news) contents. Through a thorough quantitative analysis we provide important in- sights about the anatomy of the system across which misinformation might spread. In particular, we show that users’ engagement on veri- fied (or unverified) content correlates with the number of friends hav- ing similar consumption patterns (homophily). Finally, we measure how this social system responded to the injection of 4, 709 false information. We find that the frequent (and selective) exposure to specific kind of content (polarization) is a good proxy for the detection of homophile clusters where certain kind of rumors are more likely to spread.
    [Show full text]
  • Perceptions of Social Support Within the Context of Religious Homophily: A
    Louisiana State University LSU Digital Commons LSU Master's Theses Graduate School 2003 Perceptions of social support within the context of religious homophily: a social network analysis Sally Robicheaux Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_theses Part of the Sociology Commons Recommended Citation Robicheaux, Sally, "Perceptions of social support within the context of religious homophily: a social network analysis" (2003). LSU Master's Theses. 166. https://digitalcommons.lsu.edu/gradschool_theses/166 This Thesis is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Master's Theses by an authorized graduate school editor of LSU Digital Commons. For more information, please contact [email protected]. PERCEPTIONS OF SOCIAL SUPPORT WITHIN THE CONTEXT OF RELIGIOUS HOMOPHILY: A SOCIAL NETWORK ANALYSIS A Thesis Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Master of Arts in The Department of Sociology by Sally Robicheaux B.A., University of Southwestern Louisiana, 1998 May 2003 ACKNOWLEDGEMENTS I would like to take this opportunity to thank several people who accompanied me through this process. First, I am deeply indebted to the members of my examining committee, Jeanne S. Hurlbert, John J. Beggs, and Yang Cao, for their keen insight, direction, and contributions to this thesis. I especially wish to thank my committee chair, and advisor, Jeanne S. Hurlbert, for all the invaluable guidance, instruction, and encouragement in helping me design and carry out this project.
    [Show full text]
  • Semantic Homophily in Online Communication: Evidence from Twitter
    A Semantic homophily in online communication: evidence from Twitter SANjA Sˇ CEPANOVI´ C´ , Aalto University IGOR MISHKOVSKI, University Ss. Cyril and Methodius BRUNO GONC¸ALVES, New York University NGUYEN TRUNG HIEU, University of Tampere PAN HUI, Hong Kong University of Science and Technology People are observed to assortatively connect on a set of traits. This phenomenon, termed assortative mixing or sometimes homophily, can be quantified through assortativity coefficient in social networks. Uncovering the exact causes of strong assortative mixing found in social networks has been a research challenge. Among the main suggested causes from sociology are the tendency of similar individuals to connect (often itself referred as homophily) and the social influence among already connected individuals. Distinguishing between these tendencies and other plausible causes and quantifying their contribution to the amount of assortative mixing has been a difficult task, and proven not even possible from observational data. However, another task of similar importance to researchers and in practice can be tackled, as we present here: understanding the exact mechanisms of interplay between these tendencies and the underlying social network structure. Namely, in addition to the mentioned assortativity coefficient, there are several other static and temporal network properties and substructures that can be linked to the tendencies of homophily and social influence in the social network and we herein investigate those. Concretely, we tackle a computer-mediated communication network (based on Twitter mentions) and a particular type of assortative mixing that can be inferred from the semantic features of communication content that we term semantic homophily. Our work, to the best of our knowledge, is the first to offer an in-depth analysis on semantic homophily in a communication network and the interplay between them.
    [Show full text]
  • Identifying the Roles of Race-Based Choice and Chance in High School Friendship Network Formation
    Identifying the roles of race-based choice and chance in high school friendship network formation Sergio Currarinia, Matthew O. Jacksonb,c,1, and Paolo Pind aDipartimento di Scienze Economiche, Università Ca′ Foscari di Venezia, and School for Advanced Studies in Venice, 30123 Venice, Italy; bDepartment of Economics, Stanford University, Stanford, CA 94305; cSanta Fe Institute, Santa Fe, NM 87501; and dDipartimento di Economia Politica, Università degli Studi di Siena, 53100 Siena, Italy Edited* by Christos Papadimitriou, University of California, Berkeley, CA, and approved January 13, 2010 (received for review October 12, 2009) Homophily, the tendency of people to associate with others similar preferences of the individual by careful observation of the choices to themselves, is observed in many social networks, ranging from that they make based on the opportunities that they have. We adapt friendships to marriages to business relationships, and is based on a these techniques for the analysis of social behavior and friendship variety of characteristics, including race, age, gender, religion, and formation. Here we infer students’ preferences by observing how the education. We present a technique for distinguishing two primary number of friendships they have changes with the racial composition sources of homophily: biases in the preferences of individuals over of their school. We employ the friendship formation model (3), here the types of their friends and biases in the chances that people meet extended to allow for differentbiases across race in both preferences individuals of other types. We use this technique to analyze racial and opportunities. Using a parameterized version of this model, we patterns in friendship networks in a set of American high schools are able to distinguish between the two primary sources of homo- from the Add Health dataset.
    [Show full text]
  • Social Network Analysis: Homophily
    Social Network Analysis: homophily Donglei Du ([email protected]) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 Donglei Du (UNB) Social Network Analysis 1 / 41 Table of contents 1 Homophily the Schelling model 2 Test of Homophily 3 Mechanisms Underlying Homophily: Selection and Social Influence 4 Affiliation Networks and link formation Affiliation Networks Three types of link formation in social affiliation Network 5 Empirical evidence Triadic closure: Empirical evidence Focal Closure: empirical evidence Membership closure: empirical evidence (Backstrom et al., 2006; Crandall et al., 2008) Quantifying the Interplay Between Selection and Social Influence: empirical evidence—a case study 6 Appendix A: Assortative mixing in general Quantify assortative mixing Donglei Du (UNB) Social Network Analysis 2 / 41 Homophily: introduction The material is adopted from Chapter 4 of (Easley and Kleinberg, 2010). "love of the same"; "birds of a feather flock together" At the aggregate level, links in a social network tend to connect people who are similar to one another in dimensions like Immutable characteristics: racial and ethnic groups; ages; etc. Mutable characteristics: places living, occupations, levels of affluence, and interests, beliefs, and opinions; etc. A.k.a., assortative mixing Donglei Du (UNB) Social Network Analysis 4 / 41 Homophily at action: racial segregation Figure: Credit: (Easley and Kleinberg, 2010) Donglei Du (UNB) Social Network Analysis 5 / 41 Homophily at action: racial segregation Credit: (Easley and Kleinberg, 2010) Figure: Credit: (Easley and Kleinberg, 2010) Donglei Du (UNB) Social Network Analysis 6 / 41 Homophily: the Schelling model Thomas Crombie Schelling (born 14 April 1921): An American economist, and Professor of foreign affairs, national security, nuclear strategy, and arms control at the School of Public Policy at University of Maryland, College Park.
    [Show full text]
  • Sex, Race, Religion, Age, and Education Homophily Among Confidants, 1985 to 2004 Jeffrey A
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Sociology Department, Faculty Publications Sociology, Department of 4-2014 Social Distance in the United States: Sex, Race, Religion, Age, and Education Homophily among Confidants, 1985 to 2004 Jeffrey A. Smith University of Nebraska-Lincoln, [email protected] Miller McPherson Duke University, [email protected] Lynn Smith-Lovin Duke University, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/sociologyfacpub Part of the Demography, Population, and Ecology Commons, Race and Ethnicity Commons, Social Psychology and Interaction Commons, and the Social Statistics Commons Smith, Jeffrey A.; McPherson, Miller; and Smith-Lovin, Lynn, "Social Distance in the United States: Sex, Race, Religion, Age, and Education Homophily among Confidants, 1985 to 2004" (2014). Sociology Department, Faculty Publications. 246. http://digitalcommons.unl.edu/sociologyfacpub/246 This Article is brought to you for free and open access by the Sociology, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Sociology Department, Faculty Publications by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Published in American Sociological Review 79:3 (2014), pp. 432-456. doi: 10.1177/0003122414531776 Copyright © 2014 American Sociological Review. Used by permission. digitalcommons.unl.edu Published online April 24, 2014. Social Distance in the United States: Sex, Race, Religion, Age, and Education Homophily among Confidants, 1985 to 2004 Jeffrey A. Smith,1 Miller McPherson,2 and Lynn Smith-Lovin2 1. University of Nebraska-Lincoln 2. Duke University Corresponding author – Jeffrey A. Smith, Department of Sociology, University of Nebraska-Lincoln, 711 Oldfather Hall, Lincoln, NE 68588-0324, email [email protected] Abstract Homophily, the tendency for similar actors to be connected at a higher rate than dissimi- lar actors, is a pervasive social fact.
    [Show full text]
  • Homophily in Social Networks and Labor Market Outcomes
    Homophily in Social Networks and Labor Market Outcomes Emmanuel Valat1 Université Panthéon-Assas Paris II & ESSEC Business School Version: April 2010 We develop a labor matching model where information about vacant jobs is partly obtained through contacts. From this framework we study the correlation between some charateristics of the structure of social networks and labor market outcomes. Workers are embeded in di¤erent network structures according to their type -or origin. Following recent theoretical and empirical …ndings, we consider that two characteristics of network composition are endogenous for each type: homophily -the tendency to associate with individuals of the same type- and network size. These two characteristics are a¤ected by changes in three key elements, the relative size of each group in the population, average preferences for same-type ties and biases in meetings. Our numerical simulations suggest a positive relation between network size and labor market outcomes, which is in line with the literature. Simulations however suggest something less expected, a positive correlation between homophily and labor market outcomes for members of the minority group. The relation is reversed for the majority group. We discuss conditions and explainations for this correlation. We furthermore notice that the way the three key elements we mentioned above a¤ect labor market outcomes for each group is in line with the growing empirical literature on the subject. Key Words: Social Networks, Information Transmission, Homophily, Unemploy- ment. JEL Classi…cation: J15 ; J64 ; D83. 1 ERMES (EAC-7181 CNRS) Université Panthéon-Assas Paris II and ESSEC Business School. Ad- dress: Emmanuel Valat, Université Panthéon-Assas Paris 2 (ERMES), 12 Place du Panthéon, 75230 Paris Cedex 05.
    [Show full text]
  • Homophily and Online Networks: Young Adult Relationships in Myspace Jennifer Turchi Clemson University, [email protected]
    Clemson University TigerPrints All Theses Theses 5-2007 Homophily and online networks: Young adult relationships in MySpace Jennifer Turchi Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Part of the Family, Life Course, and Society Commons Recommended Citation Turchi, Jennifer, "Homophily and online networks: Young adult relationships in MySpace" (2007). All Theses. 131. https://tigerprints.clemson.edu/all_theses/131 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. HOMOPHILY AND ONLINE NETWORKS: YOUNG ADULT RELATIONSHIPS IN MYSPACE _________________________________________________________ A Thesis Presented to the Graduate School of Clemson University _________________________________________________ _________ In Partial Fulfillment of the Requirements for the Degree Master of Science Applied Sociology ___________________________________________________________ by Jennifer Ann Turchi May 2007 __________________________________________________________ __ Accepted by: Dr. James Witte, Committee Chair Dr. Melinda Denton Dr. Ellen Granberg ABSTRACT This paper examines the characteristics of online social networks. The researcher collected the data used in the study between January 3, 2007 and Janu ary 10, 2007. 150 networks were collected from MySpace.com, an online social networking site, along with six features from the profiles of those in the networks —race/ethnicity, education, religion, reasons for joining, profile background, and music. Two r egression models were used to determine the effects of homophily on network density and network embeddedness. The regression models show that homophily for race/ethnicity, religion, music, and reasons for joining did not significantly affect network embedd edness or network density.
    [Show full text]
  • Interest Dynamics with Social Constraints and Exogenous Drivers
    Interest Dynamics with Social Constraints and Exogenous Drivers H. Van Dyke Parunak, Sven A. Brueckner, Elizabeth Downs, Andrew Yinger, Vector Research Center, Jacobs Engineering Group, 3520 Green Court, Suite 250, Ann Arbor, MI 48105 USA {Van.Parunak, Sven.Brueckner, Liz.Downs, Andrew.Yinger}@jacobs.com Abstract. An important dynamic among interacting knowledge workers is the transition from cognitive convergence (a positive group phenomenon) to collapse (which can lead to overlooking critical information). This paper extends previous studies of this subject in two ways. 1) We place agents in distinct social groups and vary within-group affinity. 2) We provide exogenous drivers of interest. We exhibit a metastable configuration of this system with three phases, and show how to distinguish convergence from collapse. Then we use this metric to explore the system’s dynamics, over the space defined by social affinity and precision of queries, and under a range of different functions for the influence that an interaction partner has on an agent. Keywords: Interest Dynamics, Emergent Behavior, Agent-Based Modeling. 1 Introduction Humans form interests by interactions with peers (collaboration) and with information sources (search). The pattern of interactions emerges from a person’s environment (social network, access to information sources) and current interests. In turn, interactions shape the environment in a complex feedback process. Such dynamics can lead to emergent collective cognitive effects at the system level (across groups of people) that can dominate individuals’ interest evolution without a person’s being aware of them. One common phenomenon is alignment of interests, sometimes called “consensus formation” [6] or “collective cognitive convergence” [12,13].
    [Show full text]
  • Queerying Homophily 2018
    Repositorium für die Medienwissenschaft Wendy Hui Kyong Chun Queerying Homophily 2018 https://doi.org/10.25969/mediarep/12350 Veröffentlichungsversion / published version Sammelbandbeitrag / collection article Empfohlene Zitierung / Suggested Citation: Chun, Wendy Hui Kyong: Queerying Homophily. In: Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer u.a. (Hg.): Pattern Discrimination. Lüneburg: meson press 2018, S. 59–97. DOI: https://doi.org/10.25969/mediarep/12350. Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer Creative Commons - This document is made available under a creative commons - Namensnennung - Nicht kommerziell 4.0 Lizenz zur Verfügung Attribution - Non Commercial 4.0 License. For more information gestellt. Nähere Auskünfte zu dieser Lizenz finden Sie hier: see: https://creativecommons.org/licenses/by-nc/4.0 https://creativecommons.org/licenses/by-nc/4.0 [ 3 ] Queerying Homophily Wendy Hui Kyong Chun To recap, in Pattern Discrimination: 1. YOU is always singular plural: • Recognition is never at the level of the individual • You = YOUS value 2. Machines engage in deep dreaming, creating patterns from noise. • Crab in = crap out • As with the gibbering muses, interpretation and herme- neutics enter through pattern discrimination, but now through the “back door” • We live in mythic times, but without knowing we do 3. The singularity of the market = the crapularity of the world: • the dumbing down of humans • the integration of subjectivity into information technologies • the reality of paranoia 60 4. To come out, we have to come in: • we are inside when we think we are outside. • Open societies need enemies to be “open” This chapter continues these points by examining homophily—the axiom that similarity breeds connection—which grounds contem- porary network science.
    [Show full text]
  • Modeling Memetic Diffusion: Toward an Integrative Predictive Model
    MODELING MEMETIC DIFFUSION: TOWARD AN INTEGRATIVE PREDICTIVE MODEL SDSU/HDMA Lightning Talk 30 Oct 2013 Brian H. Spitzberg, Senate Distinguished Professor (School of Communication, San Diego State Univ.) [email protected] Project supported by grant from the National Science Foundation, Division of Computer & Network Systems, NSF Program CDI-Type II (# 1028177). Opinions expressed are those of the author and not necessarily those of the NSF. MEMES AND EVOLUTION Meme: A meme is an act or meaning structure that is capable of replication, which means imitation (Dawkins, 1976). Adaptive cooperation & competition: “selfishness beats altruism within groups. Altruistic groups beat selfish groups. Everything else is commentary” (Wilson & Wilson, 2007) Multilevel Model of Meme Diffusion MEME(S) MEME LEVEL ‘SELFISHNESS’ FACTORS: Distinctiveness/Entropy Redundancy Simplicity/Trialability Media Convergence Media Expressivity Multilevel Model of Meme Diffusion INDIVIDUAL LEVEL COMPETENCE FACTORS: CMC Motivation COMPETENCE Knowledge Skills Message/Media Adaptation Attributed Source Ethos Actor Centrality/Propinquity Multilevel Model of Meme Diffusion NETWORK LEVEL NETWORK LEVEL ‘ALTRUISM’ FACTORS: ‘ALTRUISM’ FACTORS: OBJECTIVE/STRUCTURAL SUBJECTIVE/RECEPTIVENESS N past memes (e.g., tweets) Counter-Memes & Frames N nodes (communicators) Frame/Narrative Fidelity Network homophily Subjective Homophily N/Centrality of Influencers Niche: Relative Advantage Task Interdependence Cascade Threshold(s) SOCIAL CONTEXT/NETWORK(S) Multilevel Model of Meme Diffusion
    [Show full text]
  • Homophily Or Influence? an Empirical Analysis of Purchase Within a Social Network
    Homophily or Influence? An Empirical Analysis of Purchase within a Social Network Liye Ma, Ramayya Krishnan, and Alan Montgomery Carnegie Mellon University iLab, Heinz College and Tepper School of Business 5000 Forbes Ave Pittsburgh, Pennsylvania June 2010 Liye Ma (email: [email protected]) is a doctoral candidate, and Alan Montgomery (email: [email protected]) is an Associate Professor, both at the Carnegie Mellon University’s Tepper School of Business, and Ramayya Krishnan (email: [email protected]) is Dean of Carnegie Mellon’s Heinz College and William W. and Ruth F. Cooper Professor of Management Science and Information Systems. The authors are affiliated with the iLab at the Heinz College and would like to acknowledge support of the iLab at Heinz College and our anonymous project sponsor for providing the data used in this project. All opinions expressed in this paper are our own and do not reflect those of our project sponsor. Homophily or Influence? An Empirical Analysis of Purchase within a Social Network Abstract Consumers that are close to one another in a social network are known to have similar behaviors. The focus of this study is the extent to which such observed similarity is driven by homophily or social influence. Homophily refers to the similarity in product preferences between individuals who are connected. Social influence is the dependence of consumers’ purchase decisions on their communication with others. We construct a hierarchical Bayesian model to study both the timing and choice of consumer purchases within a social network. Our model is estimated using a unique social network dataset obtained from a large Indian telecom operator for the purchase of caller ringer-back tones.
    [Show full text]