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Basics of Social Network Analysis
 
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Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: “actor” or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral – below average centrality, e.g. C. Central connector – above average centrality, e.g. D. Broker – above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.
Views: 35400 Alexandra Ott
Social Network Analysis with R | Examples
 
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Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 15589 Bharatendra Rai
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 1894 TEDx Talks
Opinion Mining For Social Networking Site
 
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Get the project at http://nevonprojects.com/opinion-mining-for-social-networking-site/ An innovative opinion mining system that rates social network posts by extracting user sentiments from user comments on posts.
Views: 8441 Nevon Projects
Social Network Mining
 
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Social Network Mining Using R tool. termDocMatrix.rdata link:http://www.rdatamining.com/data If you are not able to install package using r tool then you can directly download the r package from below link. R data mining packages link:http://cran.r-project.org/web/packages/available_packages_by_name.html From this site download the .zip file of the package and after downloading the package open R tool and click on "packages" and select "install packages from local zip file". After successful installation you need to load the package.For loading the package click on "packages" and select "load packages" and then select the package you want to load. Get Great Deals on Amazon: https://goo.gl/jgZR7W Get Great Deals on Flipkart : https://goo.gl/MwgBfS Get Great Deals on Paytm : https://goo.gl/1XBQHr
Views: 5748 LetsGetGyan
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 523 The Audiopedia
Social Media Mining & Scrapping with Python
 
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Social media crawler/scrapper that dumps images, tweets, captions, external links and hashtags from Instagram and Twitter in an organized form. It also shows the most relevant hashtags with their frequency of occurrence in the posts. Github Link https://github.com/the-javapocalypse/Social-Media-Scrapper/blob/master/README.md Twitter App https://apps.twitter.com/ Please Subscribe! And like. And comment. That's what keeps me going. Follow Me Facebook: https://www.facebook.com/javapocalypse Instagram: https://www.instagram.com/javapocalypse
Views: 575 Javapocalypse
Social Networks in Data Mining - Keynote Bart Baesens
 
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Brooke Fortson interviews Bart Baesens about his keynote address at Analytics 2011. Baesens discusses social networks are being incorporated into analytical models. To learn more about Analytics 2011, visit http://www.sas.com/analyticsseries/us .
Views: 1980 SAS Software
[OREILLY] Social Web Mining - Github - Constructing Interest Graphs   Part 1
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 13 Freemium Courses
A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
 
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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining in Python To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs. python machine learning projects Python Python ieee projects Python ieee projects 2018 python student projects python academic projects machine learning ieee papers machine learning papers 2018 python final year project machine learning final year project
Views: 223 jpinfotechprojects
Social Media Analytics - Twitter Analysis in R (Example @realDonaldTrump)
 
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Case Study: Donald Trump Twitter (@realDonaldTrump) Analysis Click here to see how to link to Twitter database: https://www.youtube.com/watch?v=ebutXE4MJ3Y (UPDATED) Twitter Analytics in R codes Powerpoint can be downloaded at https://drive.google.com/open?id=0Bz9Gf6y-6XtTNDE5a2V0dXBjWVU How to process tweets with emojis in R? What if there is a gsub utf-8 invalid error? (Example Solution) 1. Use gsub to replace the emojis (utf-8 coding) codes. 2. See slide 7 in the Powerpoint file above.
Views: 5070 The Data Science Show
[OREILLY] Social Web Mining - Github - Constructing Interest Graphs   Part 2
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 7 Freemium Courses
Visualizing Social Network Data based on Twitter #Hashtag using NodeXL
 
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In the live demo you will learn how to visualizing social network data using NodeXL. NodeXL is an open source addon to Excel great to download social network data from Twitter, Facebook or flicker. In this example I am using twitter #Hashtag to collect social network data. NodeXL: http://nodexl.codeplex.com/ My Blog: http://cloudcelebrity.wordpress.com/
Views: 5199 Avkash Chauhan
Data Mining Open Flights Social Networking Presentation INFS770
 
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This is a presentation created for my Final Assignment in my social networking class. It contains a social networking presentation that I analyzed in gephi.
Views: 212 Tom Austin
New Features of NetMiner 4.1
 
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We are very pleased to announce that new version of NetMiner 4 has been released. Version 4.1 features enhanced 3D map, recording visual exploration, and mining for network data. - Mining Modules based on Machine Learning & New SNA Modules - Visualization with Enhanced Actual 3D Network Map - Recording Visual Exploration for Presentation For more information, please visit NetMiner website (www.netminer.com)!
Views: 4548 CyramNetminer
Raytheon's Riot Program Mines Social Network Data Like a Google for Spies
 
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02/10//2013 A multinational security firm has secretly developed software capable of tracking people's movements and predicting future behaviour by mining data from social networking websites. A video obtained by the Guardian reveals how an "extreme-scale analytics" system created by Raytheon, the world's fifth largest defence contractor, can gather vast amounts of information about people from websites including Facebook, Twitter and Foursquare. More: http://leaksource.wordpress.com/2013/02/10/raytheons-riot-program-mines-social-network-data-like-a-google-for-spies/ http://twitter.com/LeakSourceNews
Views: 33049 LeakSourceNews
Intro - Mining Data from Social Media with Python
 
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Intro to video tutorial series for Mining Data from Social Media with Python ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 9799 Sukhvinder Singh
Analyzing social media data with Python
 
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Fletcher Heisler http://pyvideo.org/video/2850/analyzing-social-media-data-with-python http://pyohio.org/schedule/presentation/100/ What does the perfect tweet or a viral blog post look like? When should it be posted? We'll introduce various tools for working with data in terms of collecting (requests), exploring (IPython, pandas), analyzing (NLTK, scikit-learn) and visualizing (matplotlib). In the process, we will uncover some surprising strategies for getting content shared across social media.
Views: 7318 Next Day Video
A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
 
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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining IEEE PROJECTS 2018-2019 TITLE LIST Call Us: +91-7806844441,9994232214 Mail Us: [email protected] Website: : http://www.nextchennai.com : http://www.ieeeproject.net : http://www.projectsieee.com : http://www.ieee-projects-chennai.com : http://www.24chennai.com WhatsApp : +91-7806844441 Chat Online: https://goo.gl/p42cQt Support Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Video Tutorials * Supporting Softwares Support Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * Remote Connectivity * Document Customization * Live Chat Support
Ben Chamberlain - Real time association mining in large social networks
 
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PyData London 2016 Social media can be used to perceive the relationships between individuals, companies and brands. Understanding the relationships between key entities is of vital importance for decision support in a swathe of industries. We present a real-time method to query and visualise regions of networks that could represent an industries, sports or political parties etc. There is a growing realisation that to combat the waning effectiveness of traditional marketing, social media platform owners need to find new ways to monetise their data. Social media data contains rich information describing how real world entities relate to each other. Understanding the allegiances, communities and structure of key entities is of vital importance for decision support in a swathe of industries that have hitherto relied on expensive, small scale survey data. We present a real-time method to query and visualise regions of networks that are closely related to a set of input vertices. The input vertices can define an industry, political party, sport etc. The key idea is that in large digital social networks measuring similarity via direct connections between nodes is not robust, but that robust similarities between nodes can be attained through the similarity of their neighbourhood graphs. We are able to achieve real-time performance by compressing the neighbourhood graphs using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines to milliseconds on standard laptops. Our method allows analysts to interactively explore strongly associated regions of large networks in real time. Our work has been deployed in Python based software and uses the scipy stack (specifically numpy, pandas, scikit-learn and matplotlib) as well as the python igraph implementation. Slides available here: https://docs.google.com/presentation/d/1-NkcPM3XYn-7jk6233MvvFJiC5Abi3e2nGkF_NSFuFA/edit?usp=sharing Additional information: http://krondo.com/in-which-we-begin-at-the-beginning/
Views: 733 PyData
Social Media Social Data and Python: 4 - Social Media Mining Techniques
 
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In this video we will briefly discuss the overall process for building a social media mining application, before digging into the details. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 2041 Sukhvinder Singh
Datasets : How to Download?
 
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Datasets : How to Download?
Views: 4738 Social Networks
Improving Fraud Detection Techniques Using Social Network Analytics
 
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Bart Baesens and Véronique Van Vlasselaer of KU Leuven talk to Inside Analytics host Maggie Miller about using social network algorithms to stay ahead of fraudsters. For more information about the Analytics 2014 conference, visit http://www.sas.com/events/analytics/europe/
Views: 2553 SAS Software
SOCIAL NETWORKING SITES USING R TOOL
 
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MSC.IT PART 1 SEM 1 SUBJECT: DATA MINING Aim: Using R-Tool , show the analysis for social networking sites.
Views: 678 Priyanka Jadhav
Social Media Mining and Analytics Presentation
 
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Recorded with http://screencast-o-matic.com
Views: 185 Jennifer Paiotti
Uncovering Meaning in Twitter Networks Using NodeXL Pro
 
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This walkthrough video demonstrates one variety of user-friendly social media data mining -- how to uncover meaning in Twitter search networks using the software package NodeXL Pro. Three applications: NodeXL's ready-made Top Ten function, working with word pairs in the text of Tweets to generate semantic networks, and characterizing hashtag overlaps for a more concentrated account of meaning in network form. I work with the example of the hashtag #Paris. Created for the undergraduate social network analysis course at the University of Maine at Augusta.
Views: 5139 James Cook
Talk Data to Me: Let's Analyze Social Media Data with Tableau
 
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Social media data is hot stuff—but it sure can be tricky to understand. In this session, Michelle from Tableau's social media team will share how they analyze social media data from multiple sources. We'll compare methods for collecting data, and discuss tips for ensuring that it answers new questions as they arise. Whether you're new to social media analysis or have already started diving into your data, this session will provide key tips, tricks, and examples to help you achieve your goals.
Views: 11400 Tableau Software
Analyzing Social Networks on Twitter
 
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Twitter is rapidly becoming a "common carrier" of social media, a wire that transmits a variety of content rather then a social network in itself. Yet, Twitter data is rich in elements that yield to Social Network Analysis techniques and can produce unique insights into information diffusion. This course will cover: Harvesting data from Twitter via search and streaming APIs Decomposing Tweets into constituent parts Fast-and-frugal content analysis of tweets Deriving and analyzing social network data found in tweets. Analyzing friends and followers About Maksim Tsvetovat: Maksim Tsvetovat is an interdisciplinary scientist, a software engineer, and a jazz musician. He has received his doctorate from Carnegie Mellon University in the field of Computation, Organizations and Society, concentrating on computational modeling of evolution of social networks, diffusion of information and attitudes, and emergence of collective intelligence. Currently, he teaches social network analysis at George Mason University. He is also a co-founder of DeepMile Networks, a startup company concentrating on mapping influence in social media. Maksim also teaches executive seminars in social network analysis, including "Social Networks for Startups" and "Understanding Social Media for Decisionmakers".
Views: 4080 O'Reilly
The Logic of Data Mining in Social Research
 
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This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 331 James Cook
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Abstract—Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences. Index Terms—Education, computers and education, social networking, web text analysis
Gephi Tutorial: Visualizing Facebook Network
 
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A short introduction to Gephi through the visualization of my Facebook network from the Data J Lab at Tilburg University
Views: 119128 Data J Lab
[OREILLY] Social Web Mining - Github - Using The GitHub API
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 18 Freemium Courses
Sensitive Labels in Social Network Data Anonymization
 
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IEEE Java project in Data Mining
Views: 50 sanjeev kumar
Visual Text Mining in Social Media
 
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In today’s world of data dominance, social networking websites and especially microblogging platforms, form the largest share in current unstructured textual data. If the proper tools, such as opinion mining and sentiment analysis are applied to that data, valuable information would be produced. That information in turn could offer insights from understanding market trends to interpreting social phenomena.The purpose of this thesis is the design and implementation of a system that deals with Network Analysis algorithms and visualisation of social networking data. Such a system consists of the following modules: Data retrieval is responsible for collecting data from social networking platforms. Data preprocessing methods cleans data of irrelevant information and prepares them for the application of the sentiment analysis method. Sentiment Analysis applies a model to the data in order to classify them according to their sentiment. Data Reprocessing prepares the data for the visualization process. Topic Modeling applies specific algorithms that identify topics in text corpora. Visualization process represents data in a graph, taking into account the results of all previous processes.
Views: 2566 Manolis Maragoudakis
Why Twitter is blocking the government from using a data-mining tool (CNET Update)
 
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Watch more CNET Upate: http://bit.ly/1M6Q5xn The social network is banning US spy agencies from accessing an analytics service used by news agencies. Meanwhile, Facebook wins a trademark battle in China. Sorry, you won't be able to taste "Face Book" the drink. Subscribe to CNET: http://bit.ly/17qqqCs Watch more CNET videos: http://www.cnet.com/video Follow CNET on Twitter: http://twitter.com/CNET Follow CNET on Facebook: http://www.facebook.com/cnet
Views: 10545 CNET
Introduction to Social Network Analysis
 
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This workshop provides a broad overview of Social Network Analysis. In the first part of the workshop, a concise overview of theoretical concepts is provided, together with examples of data collection methods. The second section discusses network data analysis - network measurements (i.e. density, reciprocity, etc.) and node level measurements (i.e. degree centrality, betweenness centrality, etc.). The last part of the workshop introduces participants to UCINET and NetDraw, software packages used for data management, analysis and visualization.
Data mining, social media, kids and security
 
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Matt Kelly chats to Rihanna Patrick on ABC Radio Brisbane about the recent revelations that Google is tracking the activities of kids online, and using this data to target advertising.
Views: 118 justmediadesign
Online surveillance software / data mining
 
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A look at how monitoring patterns of behavior online can be construed as subversive behavior. Will this become the truncheon of a world police state?
Views: 36959 germanjournal
Data Mining in Unstructured Textual Environments - Technology Demo - NetAlign
 
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This is a demonstration of the technology developed in NetAlign, a small technology-focused startup that I have co-founded in 2008. NetAlign specialized in data mining algorithms in unstructured textual environments, of the kind found in social networks, blogs and online-forums. We have developed fast algorithms that make the data on such a network accessible for a large set of applications. Applications demonstrated: 1- Page clearance for ads: spam, profanity, adult content and extreme slang. 2- Contextual page clearance for ads: find the trolls. 3- Topical categorization and keywords extraction. 4- Building a user profile based on his/her interactions and their friends'. 5- Clustering the social graph to associate users whom we know very little about to users that are clearly classified.
Views: 71 Ethan Ram
Advanced Technologies - Social Media Data Mining  - Video Log #01
 
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First video to kicking off my social media data mining task. For this one I am aiming to use the github api, using user profile data to craft/contribute towards a game
Views: 57 Daniel Weston
Text Mining Social Media Sentiment Analytics in  R-11th June 2016
 
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Analytics Accelerator Program- May 2016-July 2016 Batch
evaluation of predictive data mining algorithms in soil data classification for optimized crop
 
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evaluation of predictive data mining algorithms in soil data classification for optimized crop recom - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS 1. RRPhish Anti-Phishing via Mining Brand Resources Request 2. Confidence-interval Fuzzy Model-based Indoor Localization COMPUTER-BASED MEDICAL SYSTEMS (CBMS) 1. Population Health Management exploiting Machine Learning Algorithms to identify High-Risk Patients (23 July 2018) PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1. Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition ( April 1 2018 ) 2. Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection 3. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search SOFTWARE ENGINEERING,COMPUTER GRAPHICS 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events 3. Performance Specification and Evaluation with Unified Stochastic Probes and Fluid Analysis 4. Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links 5. Amorphous Slicing of Extended Finite State Machines 6. Test Case-Aware Combinatorial Interaction Testing 7. Using Timed Automata for Modeling Distributed Systems with Clocks: Challenges and Solutions 8. EDZL Schedulability Analysis in Real-Time Multicore Scheduling 9. Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler 10. Locating Need-to-Externalize Constant Strings for Software Internationalization with Generalized String-Taint Analysis 11. Systematic Elaboration of Scalability Requirements through Goal-Obstacle Analysis 12. Centroidal Voronoi Tessellations- A New Approach to Random Testing 13. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm 14. Pair Programming and Software Defects--A Large, Industrial Case Study 15. Automated Behavioral Testing of Refactoring Engines 16. An Empirical Evaluation of Mutation Testing for Improving the Test Quality of Safety-Critical Software 17. Self-Management of Adaptable Component-Based Applications 18. Elaborating Requirements Using Model Checking and Inductive Learning 19. Resource Management for Complex, Dynamic Environments 20. Identifying and Summarizing Systematic Code Changes via Rule Inference 21. Generating Domain-Specific Visual Language Tools from Abstract Visual Specifications 22. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers 23. On Fault Representativeness of Software Fault Injection 24. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud 25. Coverage Estimation in Model Checking with Bitstate Hashing 26. Synthesizing Modal Transition Systems from Triggered Scenarios 27. Using Dependency Structures for Prioritization of Functional Test Suites
Views: 4 MICANS VIDEOS
Enhanced instant message security and privacy protection scheme for mobile social network systems
 
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Enhanced instant message security and privacy protection scheme for mobile social network systems- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications 31. Emotion Recognition on Twitter: Comparative Study and Training a Unison Model 32. Search Result Diversity Evaluation based on Intent Hierarchies 33. A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining 34. Automated Phrase Mining from Massive Text Corpora 35. Automatic Segmentation of Dynamic Network Sequences with Node Labels
Views: 2 Micans Infotech
Mini Lecture: Social Network Analysis for Fraud Detection
 
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In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
Views: 14481 Bart Baesens
Community Detection in Social Networks and Performance Evaluation of Algorithms
 
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using community detection toolbox in matlab for your own datasets and observing the clustering pattern. Reference: http://www.mathworks.com/matlabcentral/fileexchange/45867-community-detection-toolbox
Views: 2726 Rishu Sharma
Lecture 9 - Analyzing Big Data with Twitter: Twitter's Social Network by Aneesh Sharma
 
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http://blogs.ischool.berkeley.edu/i290-abdt-s12/ Aneesh Sharma on Twitter's Social Graph Course: Information 290. Analyzing Big Data with Twitter School of Information UC Berkeley Prof. Marti Hearst Course description: How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered. This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.

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