Predictive Analytics of Social Networks
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From Factors to Actors: Computational Sociology and Agent-Based Modeling1
From Factors to Actors: Computational Sociology and Agent-Based Modeling1 Michael W. Macy Robert Willer Cornell University August, 2001 Contents: 73 citations (2.5 published pages), 1 figure (1 page), 8522 main-text words (19.5 published pages) Total length: 23 published pages 1 The first author expresses gratitude to the National Science Foundation (SES-0079381) for support during the period in which this review was written. We also thank James Kitts, Andreas Flache, and Noah Mark for helpful comments and suggestions. From Factors to Actors: Computational Sociology and Agent-Based Modeling Introduction: Agent-Based Models and Self-Organizing Group Processes Consider a flock of geese flying in tight formation. Collectively, they form the image of a giant delta-shaped bird that moves as purposively as if it were a single organism. Yet the flock has no “group mind” nor is there a “leader bird” choreographing the formation (Resnick 1994). Rather, each bird reacts to the movement of its immediate neighbors who in turn react to it. The result is the graceful dance-like movement of the flock whose hypnotic rhythm is clearly patterned yet also highly non-linear. If we tried to model the global elegance of the flock, the task would be immensely difficult because of the extreme complexity in its movement. Yet the task turns out to be remarkably easy if instead we model the dynamics of local interaction. This was demonstrated by Craig Reynolds (1987) when he modeled the movement of a population of artificial “boids” based on three simple rules: • Separation: Don't get too close to any object, including other boids. -
A Study on Social Network Analysis Through Data Mining Techniques – a Detailed Survey
ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 IJARCCE International Journal of Advanced Research in Computer and Communication Engineering ICRITCSA M S Ramaiah Institute of Technology, Bangalore Vol. 5, Special Issue 2, October 2016 A Study on Social Network Analysis through Data Mining Techniques – A detailed Survey Annie Syrien1, M. Hanumanthappa2 Research Scholar, Department of Computer Science and Applications, Bangalore University, India1 Professor, Department of Computer Science and Applications, Bangalore University, India2 Abstract: The paper aims to have a detailed study on data collection, data preprocessing and various methods used in developing a useful algorithms or methodologies on social network analysis in social media. The recent trends and advancements in the big data have led many researchers to focus on social media. Web enabled devices is an another important reason for this advancement, electronic media such as tablets, mobile phones, desktops, laptops and notepads enable the users to actively participate in different social networking systems. Many research has also significantly shows the advantages and challenges that social media has posed to the research world. The principal objective of this paper is to provide an overview of social media research carried out in recent years. Keywords: data mining, social media, big data. I. INTRODUCTION Data mining is an important technique in social media in scrutiny and analysis. In any industry data mining recent years as it is used to help in extraction of lot of technique based reports become the base line for making useful information, whereas this information turns to be an decisions and formulating the decisions. This report important asset to both academia and industries. -
Social Media Analytics
MEDIA DEVELOPMENT Social media analytics A practical guidebook for journalists and other media professionals Imprint PUBLISHER EDITORS Deutsche Welle Dr. Dennis Reineck 53110 Bonn Anne-Sophie Suntrop Germany SCREENSHOTS RESPONSIBLE Timo Lüge Carsten von Nahmen Helge Schroers Petra Berner PUBLISHED AUTHOR June 2019 Timo Lüge © DW Akademie MEDIA DEVELOPMENT Social media analytics A practical guidebook for journalists and other media professionals INTRODUCTION Introduction Having a successful online presence is becoming more and In part 2, we will look at some of the basics of social media more important for media outlets all around the world. In analysis. We’ll explore what different social media metrics 2018, 435 million people in Africa had access to the Internet mean and which are the most important. and 191 million of them were using social media.1 Today, Africa is one of the fastest growing regions for Internet access and Part 3 looks briefly at the resources you should have in place social media use. to effectively analyze your online communication. For journalists, this means new and exciting opportunities to Part 4 is the main part of the guide. In this section, we are connect with their › audiences. Passive readers, viewers and looking at Facebook, Twitter, YouTube and WhatsApp and will listeners are increasingly becoming active participants in a show you how to use free analytics tools to find out more dialogue that includes journalists and other community mem- about your communication and your audience. Instagram is bers. At the same time, social media is consuming people’s not covered in this guide because, at the time of writing, only attention: Time that used to be spent listening to the radio very few DW Akademie partners in Africa were active on the is now spent scrolling through Facebook, Twitter, Instagram, platform. -
Westminsterresearch Sociology and Non-Equilibrium Social Science Anzola, D., Barbrook-Johnson, P., Salgado, M. and Gilbert, N
WestminsterResearch http://www.westminster.ac.uk/westminsterresearch Sociology and Non-Equilibrium Social Science Anzola, D., Barbrook-Johnson, P., Salgado, M. and Gilbert, N. This is a copy of the final version of a copy published in: Non-Equilibrium Social Science and Policy, Springer, pp. 59-69. ISBN 9783319424224 Available from the publisher, Springer via: https://dx.doi.org/10.1007/978-3-319-42424-8_4 © The Author(s) 2017. This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The WestminsterResearch online digital archive at the University of Westminster aims to make the research output of the University available to a wider audience. Copyright and Moral Rights remain with the authors and/or copyright owners. Whilst further distribution of specific materials from within this archive is forbidden, you may freely distribute the URL of WestminsterResearch: ((http://westminsterresearch.wmin.ac.uk/). In case of abuse or copyright appearing without permission e-mail [email protected] Sociology and Non-Equilibrium Social Science David Anzola, Peter Barbrook-Johnson, Mauricio Salgado, and Nigel Gilbert Abstract This chapter addresses the relationship between sociology and Non- Equilibrium Social Science (NESS). Sociology is a multiparadigmatic discipline with significant disagreement regarding its goals and status as a scientific discipline. Different theories and methods coexist temporally and geographically. -
Mining Social Networking Sites: a Specific Area of Facebook
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019 Mining Social Networking Sites: A Specific area of Facebook Sonam, Surjeet Kumar Abstract: Now a days facebook is the specific social network site for communicating more people, to develop learning process II. BACKGROUND AND RELATED WORK & research area. Although mining and analysis are needed area of social network so, we are showing useful part in this paper We studied several literatures and got techniques that are that are related to communication with people, collection of data useful for collecting and analyze specific information. We and uses of social network’s tool and techniques .During our are discussing some techniques such as: research work we found several tools are available for collecting A. OSMNs Dataset: Mining related data from the Web data and analyzing fan pages. For the purpose of our research mining platform by using tools such as OSMN's Web work, we used social network site such as Facebook, in this platform we created one page related to blog who did the panacea extraction technology. Since the OSMN dataset resides in for us. In this research paper we have applied online social media the backend server, it is not available in the public domain network for extracting data. Uniform node detection has used for and can therefore only be accessed through the web finding common properties among audience as well as Regular interface. FB uses many access algorithms, such as Random equivalence nodes are using for calculating uniformity. Effective Walk or BFS [25]. -
Social Theory and Social Computing Workshop– Honolulu, Hawaii – May 22 - 23, 2010
Social Theory and Social Computing Workshop– Honolulu, Hawaii – May 22 - 23, 2010 Presentation abstracts and bio sketches Day One CHOICE-THEORETIC MODELS Dennis Chong, Northwestern University, Department of Political Science Dynamic Public Preferences I will discuss how the sensitivity of political evaluations to framing affects our understanding of individual preferences. In particular, the effects of framing appear to undermine the assumption that preferences are consistent. Some researchers have suggested that democratic competition can strengthen preferences and reduce framing effects. I will present some experimental tests of how competition over time between alternative frames affects public opinion depending on how individuals process information. Dennis Chong is the John D. and Catherine T. MacArthur Professor of Political Science at Northwestern University. He studies American national politics and has published extensively on issues of decision-making, political psychology, social norms, rationality, tolerance, and collective action. Professor Chong is the author of Rational Lives: Norms and Values in Politics and Society, a study of value formation and change, group identification, and conflict over social norms and values. He also wrote Collective Action and the Civil Rights Movement, a theoretical study of the dynamics of collective action as well as a substantial study of the American civil rights movement and the local and national politics that surrounded it. This book won the William H. Riker Prize given by the Political Economy Section of the American Political Science Association. Professor Chong's current research on the influence of information and framing in competitive electoral contexts has received several awards, including the APSA's Franklin L. Burdette/Pi Sigma Alpha Prize. -
How to Prove Your Social Media Works
HOW TO PROVE YOUR SOCIAL MEDIA WORKS Ariana Torres, PhD Marketing Specialist Purdue University Agenda § Is your business getting anything out of its social media engagement? § Some key words in social media ROI § Measuring your social media ROI 1. Define why you want to do social media for your business 2. Set measurable goals 3. Track goals 4. Track social media expenses (per campaign) 5. Calculate campaign-specific ROI § How to improve your ROI § An argument for using both organic and paid social media Is your business getting anything out of its social media engagement? § Measuring return on investment (ROI) is the number one challenge of social marketers § What does ROI really mean? It is brand awareness, number of followers, engagement rate, customer satisfaction, lead to conversion, or… § Not everything you do in social media has an immediate effect into $ § Yet, it is important to track impact of time and resources that go into your social media efforts § Your social media goals will determine what you will measure à metrics • Every campaign needs a goal, and every goal needs a metric § Metrics will prove if your campaign is being successful, if you have to change or pivot something § Each social media platform has its analytics • Facebook à Insights • Twitter à Twitter analytics • Instagram à Insights Some key words § Engagement: likes, shares, followers, comments § Reach: how many people have viewed your content § Impressions: how many times your posts show up in a feeds/timeline and may connect to your audience § Conversions: lead conversion rate is how many people “buy” something § Social proofing: people copy others behavior, i.e. -
Social Media Monitoring During Elections
SOCIAL MEDIA MONITORING DURING ELECTIONS CASES AND BEST PRACTICE TO INFORM ELECTORAL OBSERVATION MISSIONS AUTHORS Rafael Schmuziger Goldzweig Bruno Lupion Michael Meyer-Resende FOREWORD BY Iskra Kirova and Susan Morgan EDITOR Ros Taylor © 2019 Open Society Foundations uic b n dog. This publication is available as a PDF on the Open Society Foundations website under a Creative Commons license that allows copying and distributing the publication, only in its entirety, as long as it is attributed to the Open Society Foundations and used for noncommercial educational or public policy purposes. Photographs may not be used separately from the publication. Cover photo: © Geert Vanden Wijngaert/Bloomberg/Getty opensocietyfoundations.org Experiences of Social Media Monitoring During Elections: Cases and Best Practice to Inform Electoral Observation Missions May 2019 CONTENTS 3 FOREWORD 5 EXECUTIVE SUMMARY 7 INTRODUCTION 9 CONTEXT 9 Threats to electoral integrity in social media 9 The framework of international election observation 10 The Declaration of Principles of International Election Observation 12 WHAT ARE INTERNATIONAL OBSERVER MISSIONS DOING ON SOCIAL MEDIA DISCOURSE? 14 WHAT ARE EUROPEAN GOVERNMENTS DOING? 17 WHAT ARE CIVIL SOCIETY ORGANIZATIONS DOING? 17 European civil society 19 A glimpse beyond Europe 21 Non-government initiatives: Europe & beyond 23 Topics/objects of analysis 24 Platforms 26 Monitoring tools 1 Experiences of Social Media Monitoring During Elections: Cases and Best Practice to Inform Electoral Observation Missions May 2019 27 GUIDELINES FOR EOMS 27 Monitoring of social media vs traditional media 29 Cooperation between actors and information exchange partnerships 29 Dealing with data 29 Working in different contexts 31 SWOT analysis for EOMs in social media monitoring 32 RECOMMENDATIONS 35 ANNEX 1: SURVEY OF INTERNATIONAL ELECTION OBSERVATION ORGANIZATIONS 36 ANNEX 2: BRIEF DESCRIPTION OF THE INITIATIVES ANALYSED (based on interviews) 36 a. -
Modelling Communities and Populations: an Introduction to Computational Social Science
STUDIA METODOLOGICZNE NR 39 • 2019, 123–152 DOI: 10.14746/sm.2019.39.5 Andrzej Jarynowski, Michał B. Paradowski, Andrzej Buda Modelling communities and populations: An introduction to computational social science Abstract. In sociology, interest in modelling has not yet become widespread. However, the methodology has been gaining increased attention in parallel with its growing popularity in economics and other social sciences, notably psychology and political science, and the growing volume of social data being measured and collected. In this paper, we present representative computational methodologies from both data-driven (such as “black box”) and rule-based (such as “per analogy”) approaches. We show how to build simple models, and discuss both the greatest successes and the major limitations of modelling societies. We claim that the end goal of computational tools in sociology is providing meaningful analyses and calculations in order to allow making causal statements in sociological ex- planation and support decisions of great importance for society. Keywords: computational social science, mathematical modelling, sociophysics, quantita- tive sociology, computer simulations, agent-based models, social network analysis, natural language processing, linguistics. 1. One model of society, but many definitions Social reality has been a fascinating area for mathematical description for a long time. Initially, many mathematical models of social phenomena resulted in simplifications of low application value. However, with their grow- ing validity (in part due to constantly increasing computing power allowing for increased multidimensionality), they have slowly begun to be applied in prediction and forecasting (social engineering), given that the most interesting feature of social science research subjects – people, organisations, or societies – is their complexity, usually based on non-linear interactions. -
The Fundamentals of Social Media Analytics Table of Contents
The Fundamentals of Social Media Analytics Table of Contents Introduction 3 Part 1: What is Social Media Analytics? 4 Social Media Analytics vs. Social Media Monitoring vs. Social Media Intelligence 6 Understanding Unsolicited Conversations 8 Social Media Engagement 9 Part 2: Dealing with Unstructured Data 10 Defining Unstructured Data 11 Machine-Automated NLP and Machine-Learning 13 Part 3: Deriving Insights from Social Metrics 18 Quantitative Analysis: The What 20 Qualitative Analysis: The Why 21 In Summary 24 2 Introduction Social media has completely transformed the way people interact with each other, but it’s had an equally significant (though more easily overlooked) impact on businesses as well. There are now billions of unsolicited posts directly from consumers that brands, agencies and other organizations can use to better understand their target customer, industry landscape or brand perception. In light of all this data, we come to a natural question: How can marketers, strategists, executives and analysts make sense of it all? What tools and resources are there out there to help? Crimson Hexagon has been analyzing social data since 2007, and we’ve learned a lot during that time about how to approach, understand, and leverage social media data. This guide aims to provide a framework for this discussion. Whether you’re a CMO, an analyst, or a Category Director, this guide will help you learn the fundamentals of social media analytics and how you can apply it to your organization to make smarter, more data-backed decisions. Part One: What is Social Media Analytics? Part One: What is Social Media Analytics? Social media platforms—such as Facebook, Twitter, Instagram, and Tumblr—are a public ‘worldwide forum for expression’, where billions of people connect and share their experiences, personal views, and opinions about everything from vacations to live events. -
Social Networking: a Guide to Strengthening Civil Society Through Social Media
Social Networking: A Guide to Strengthening Civil Society Through Social Media DISCLAIMER: The author’s views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government. Counterpart International would like to acknowledge and thank all who were involved in the creation of Social Networking: A Guide to Strengthening Civil Society through Social Media. This guide is a result of collaboration and input from a great team and group of advisors. Our deepest appreciation to Tina Yesayan, primary author of the guide; and Kulsoom Rizvi, who created a dynamic visual layout. Alex Sardar and Ray Short provided guidance and sound technical expertise, for which we’re grateful. The Civil Society and Media Team at the U.S. Agency for International Development (USAID) was the ideal partner in the process of co-creating this guide, which benefited immensely from that team’s insights and thoughtful contributions. The case studies in the annexes of this guide speak to the capacity and vision of the featured civil society organizations and their leaders, whose work and commitment is inspiring. This guide was produced with funding under the Global Civil Society Leader with Associates Award, a Cooperative Agreement funded by USAID for the implementation of civil society, media development and program design and learning activities around the world. Counterpart International’s mission is to partner with local organizations - formal and informal - to build inclusive, sustainable communities in which their people thrive. We hope this manual will be an essential tool for civil society organizations to more effectively and purposefully pursue their missions in service of their communities. -
Arxiv:1403.5206V2 [Cs.SI] 30 Jul 2014
What is Tumblr: A Statistical Overview and Comparison Yi Chang‡, Lei Tang§, Yoshiyuki Inagaki† and Yan Liu‡ † Yahoo Labs, Sunnyvale, CA 94089, USA § @WalmartLabs, San Bruno, CA 94066, USA ‡ University of Southern California, Los Angeles, CA 90089 [email protected],[email protected], [email protected],[email protected] Abstract Traditional blogging sites, such as Blogspot6 and Living- Social7, have high quality content but little social interac- Tumblr, as one of the most popular microblogging platforms, tions. Nardi et al. (Nardi et al. 2004) investigated blogging has gained momentum recently. It is reported to have 166.4 as a form of personal communication and expression, and millions of users and 73.4 billions of posts by January 2014. showed that the vast majority of blog posts are written by While many articles about Tumblr have been published in ordinarypeople with a small audience. On the contrary, pop- major press, there is not much scholar work so far. In this pa- 8 per, we provide some pioneer analysis on Tumblr from a va- ular social networking sites like Facebook , have richer so- riety of aspects. We study the social network structure among cial interactions, but lower quality content comparing with Tumblr users, analyze its user generated content, and describe blogosphere. Since most social interactions are either un- reblogging patterns to analyze its user behavior. We aim to published or less meaningful for the majority of public audi- provide a comprehensive statistical overview of Tumblr and ence, it is natural for Facebook users to form different com- compare it with other popular social services, including blo- munities or social circles.