GE2234 Social Networks Lecture Note 1 PDF

GE2234 Social Networks for Media, Business and Technological Applications Lecture Note 1: Introduction to Social Networks & Course By Prof. Wang Xiaohui, Vincent Prof. Wang Xiaohui, Vincent Assistant Professor Department of Media and Communication [email protected] Office: CMC M5-056...

GE2234 Social Networks for Media, Business and Technological Applications Lecture Note 1: Introduction to Social Networks & Course By Prof. Wang Xiaohui, Vincent Prof. Wang Xiaohui, Vincent Assistant Professor Department of Media and Communication [email protected] Office: CMC M5-056 Office hour (or by appointment): Monday 9:30am-12:30pm Wednesday 9:30am-12:30pm Teaching Assistant Mr. Cong MA [email protected] Office hour (by appointment) RESEARCH & TEACHING INTERESTS Health Informatics Survey Social Media Content Analysis Computational Social Science Social Network Analysis Informetrics Computational Methods Social Network Analysis Network Science Sociology Mathematics Computer Science Statistical Physics Economics Bioinformatics We live in a connected world! “To speak of social life is to speak of the association between people –their associating in work and in play, in love and in war, to trade or to worship, to help or to hinder. It is in the social relations men establish that their interests find expression and their desires become realized.” by Peter M. Blau Exchange and Power in Social Life, 1964 However.... Standard social science analysis methods do not take this connection/structure into account. The assumption of traditional statistic analysis is independence of the data. Network Perspective: Another Paradigm to Understand the World Traditional research method (e.g., survey) – Independent – Individual attributes Network Perspective – Dependent – Relationship as the building block of analysis Complimentary rather than competitive!!! Social relations Social relations can be thought of as dyadic attributes. Whereas mainstream social science is concerned with monadic attributes (e.g., income, age, sex, etc.), network analysis is concerned with attributes of pairs of individuals, of which binary relations are the main kind. Some examples of relation attributes: Kinship: brother of, father of Social Roles: boss of, teacher of, friend of Affective: likes, respects, hates Cognitive: opinion as similar Actions: has lunch with, attacks, talks to Flows: number of cars moving between, information flow Distance: number of miles between Co-occurrence: is in the same club as, has the same color hair as What Is Networking Perspectives Everything is networked (= related/relational to everything else) It is not enough to consider unilateral relationship (A → B); even not enough to consider bilateral relationship (A →B); it is necessary to consider multilateral relationship (A →B→C) It’s not about what you know; not about who you know; even not who knows you; it’s about who you know knows who (knows who...) 10 Network = Graph Nodes = Vertices; Actors Edges = Links; Relations Clusters = Communities Examples: Facebook Friendship image by Paul Butler, 2010  Examples: Food Industry Flavor network and the principles of food pairing: http://www.nature.com/articles/srep00196 Examples: high school dating network Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks Bearman, Moody, & Stovel, (2004) page12image24480608 “The Spread of Obesity in a Large Social Network over 32 Years” N. Christakis , J. Fowler 2007 Examples: Transportation-London bike share Network Example: Biological Networks http://internet-map.net/ Why study social media ? Main sources of big data analytics for Governments and Organizations Source: https://unstats.un.org/ 1. social media are interactive Web 2.0 Internet-based applications, 2. user-generated content such as text posts or comments, digital photos or videos, as well as data generated through all online interactions, are the lifeblood of the social media organism, 3. users create service-specific profiles for the website or app, that are designed and maintained by the social media organization, 4. social media facilitate the development of online social networks by connecting a user's profile with those of other individuals and/or groups. Two dominant characteristics of social media User-Generated Content Read Web -> Read & Write Web Networking One-way communication -> Multi-way connection/communication  What can we learn from social media - Romance https://cdn.theatlantic.com/assets/media/img/mt/2014/02/1780919_10152219518868415_432315498_n/lead_large.png?1430159474 Source: https://www.theatlantic.com/technology/archive/2014/02/when-you-fall-in-love-this-is-what-facebook-sees/283865/  What can we learn from social media - Men and Women Difference Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M.,... & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one, 8(9), e73791. Age Difference: Drunk – Work - Family Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M.,... & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one, 8(9), e73791. What can we learn from social media - Trump Sentiment Score of Trump’s Tweets (By hour, Oct 2016 - Mar 2017) 20 15 10 5 0 0 1 2 3 4 5 6 7 10 11 12 13 14 15 16 17 18 19 20 21 22 23 -5 -10 -15 -20 -25 -30 Source: Vincent Wang What can we learn more from social media? ---- human behavior pattern Timestamp + Behavior: Messages sent by hour on Facebook Source: Golder et al., 2007 red-conservative blogs, blue -liberal, orange links from liberal to conservative, purple from conservative to liberal image from L. Adamic, N. Glance, 2005 Will Social Media Perspectives Make a Difference? Will socio-technological structures of media influence social interactions? How to Study Social Media? Focus on: Technological Infrastructure Business Models User Participation Social Impact What is this course about? Course Features Interdisciplinary Science and engineering Social science Business studies Quantitative Data analysis Graphic visualization Applied e.g.: web traffic, communication network, rumor spreading, influenza transmission, commodity trade, etc. Course Objectives No. CILOs# 1. To describe and explain basic theoretical concepts and research methods of social and complex networks 2. To collect, analyse, interpret, discover, and visualize social network data for real life problems 3. To apply theoretical perspectives and methodological approaches in social, business, or engineering contexts 4. To present research findings, discoveries, and case studies in professional quality and style Class Participation: Please come to class on time and be ready to participate. In order to be able to meaningfully contribute to class discussion, read the assigned readings before class. E-learning Platform: CANVAS will be used for email messages, sharing of readings, submitting assignments. Please check it regularly. Plagiarism: Plagiarism in any form will not be tolerated and will result in a failing in your final score. Late Drop: After the add/drop deadline, requests for late drop of courses will only be approved under exceptional circumstances So, Basically, NO LATE DROP Readings Required textbooks: David Easley and John Kleinberg (2010). Networks, Crowds, and Markets: Reasoning about a Highly Connected World. (EK) (https://www.cs.cornell.edu/home/kleinber/networks-book/) Robert Hanneman and Mark Riddle (2005). Introduction to Social Network Methods. Riverside, CA: University of California, Riverside. (HR) Additional references Course materials The readings put down for each week do not necessarily overlap with my lecture content and they could be supplementary to my lectures. It is essential that you read them all before each week’s meeting as some of the in-class quiz questions cover the readings. Course Structure Lectures: 1.5-2.0 hours In-class exercises or group discussions: 1.0-1.5 hours Lecture Notes All lectures will be delivered through MS Power Point files, which will be made available to you before lecture. You can download the files of lecture notes from CANVAS. Important: The notes ONLY summarize some points of the lecture. Several parts require you to take notes in class. You are encouraged to print the lectures and bring them to class with you for note-taking so that you will have a complete lecture notes. The instructor may make last-minute changes to the lecture notes. You need to attend the lectures and take down the changes if there are any. Your course grade is determined based on the following components: Assessment Methods Weighting CILOs Descriptions Continuous assessment 10% 1-3 Continuous assessments include class attendance and discussion, which are designed to measure how well the students have learned the concepts and techniques of the course Individual Assignments 30% 2-4 Students will work independently to solve 2 sets of analytical or empirical questions (e.g., structure and characteristics of given social networks), to demonstrate ability to apply networking theories and methods. Midterm quiz 30% 1,3 - Quiz will test students’ facility with theoretical concepts and analytical skills. Final Group Project 30% 1-4 Students will work together to a) identify a real life problem in which social networks play an important role; b) collect necessary and relevant data to test possible effects of networking factors on the problem; c) produce a research report based on the case study, and d) present the results to class. Total 100% Midterm Quiz (30%) All quizzes are closed-book, closed-notes unless I state otherwise. The questions will be drawn from both my lectures (80%) and the readings (20%). The quizzes will contain multiple choice and True/False questions. If an emergency should cause you to miss a quiz, you must notify me prior to the quiz to schedule a make-up. I will require documentation of the emergency situation. No early quizzes will be given. Missed quizzes will earn 0 points. As mentioned earlier, unexpected things might influence your study and quiz. Group Projects (30% in total) The group project requires you to work in a group to design and conduct a survey project. The project can NOT be completed in just a few weeks—it is something you are expected to be thinking about and working on throughout the semester. A group should be composed of 5-6 students.  Group Projects: Potential problems Free riders Do NOT file complaints when the projects are completed. Consult me earlier. Labor divide table is required when submitting the group report. Extra Points Extra points may be rewarded to students who actively participate in class activities. You may also earn extra points from research participation during the semester. Extra credit points may not exceed 3% of the overall grade points. Tips for Doing Well Keep up with readings Attend every lecture Take all exams Start on research project early Keep in touch with the instructor and the TA Week Content Due Tutorial 1 Introduction and Basic Concepts 2 Centrality Measures Gephi 3 Weak Ties Exercise 1 (W1-2) Gephi 4 Cohesive Subgroups Gephi 5 Homophily and Small-World R Basics 6 Scale-Free Network Exercise 2 (W4-5) igraph/R 7 Reading Week 8 Collecting Network Data Midterm (W1-7) 9 Case study: News Networks Group Info (4-6) R 10 Case study: Similarity Networks R 11 Case study: Co-occurrence Network R 12 Group Presentation 13 Group Presentation Group Project Analytics Tools NoSQL The Use of ChatGPT in class For R code: encouraged For brainstorming and background check: encouraged For writing report: forbidden For QA without processing: NO For data analysis: impossible