Search results “Data mining social networks”
Basics of Social Network Analysis
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: 40506 Alexandra Ott
How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY
With the 2016 presidential election only 27 days away, we’re taking a look at how the campaigns are taking to social media in the hopes of trying to win the all-important millennial vote and how data mining on Facebook and other social platforms is influencing your view of the election. NBC News’ Jo Ling Kent reports for TODAY. Red, White and You is brought to you by Amazon. » Subscribe to TODAY: http://on.today.com/SubscribeToTODAY » Watch the latest from TODAY: http://bit.ly/LatestTODAY About: TODAY brings you the latest headlines and expert tips on money, health and parenting. We wake up every morning to give you and your family all you need to start your day. If it matters to you, it matters to us. We are in the people business. Subscribe to our channel for exclusive TODAY archival footage & our original web series. Connect with TODAY Online! Visit TODAY's Website: http://on.today.com/ReadTODAY Find TODAY on Facebook: http://on.today.com/LikeTODAY Follow TODAY on Twitter: http://on.today.com/FollowTODAY Follow TODAY on Google+: http://on.today.com/PlusTODAY Follow TODAY on Instagram: http://on.today.com/InstaTODAY Follow TODAY on Pinterest: http://on.today.com/PinTODAY How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY
Views: 6089 TODAY
Mining Online Data Across Social Networks
Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow. Learn more: http://scpd.stanford.edu/
Views: 20487 stanfordonline
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: 1542 The Audiopedia
Social Media Mining & Scrapping with Python
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: 3132 Javapocalypse
Big Data Analytics | Tutorial #28 | Mining Social Network Graphs
There is much information to be gained by analyzing the large-scale data that is derived from social networks. The best-known example of a social network is the “friends” relation found on sites like Facebook. However, as we shall see there are many other sources of data that connect people or other entities. #RanjiRaj #BigData #SocialNetworkGraph Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj ويستند هذا الفيديو على مفاهيم مثل الحافة بينغريس وجريفان نيومان خوارزمية في الرسوم البيانية الاجتماعية Este video se basa en conceptos como Edge entreess y el algoritmo de Grivan Newman en los gráficos sociales Это видео основано на таких понятиях, как Edge interess и Grivan Newman Algorithm в социальных графах Cette vidéo est basée sur des concepts tels que interess et Girvan bord Newman algorithme dans les graphiques sociaux Dieses Video basiert auf Konzepten wie Edge zwischeness und Grivan-Newman Algorithmus in den sozialen Graphen Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter 👉https://twitter.com/iamRanjiRaj Like TheStudyBeast on Facebook 👉https://www.facebook.com/thestudybeast/ For more videos LIKE SHARE SUBSCRIBE
Views: 3637 Ranji Raj
Introduction to Social Networks
The network of friendships on Facebook, road connections, terrorist networks and disease spreading networks are today available as a graph G(V,E). Social Network Analysis involves discerning this graph data and making sense out of it. The course will revolve around the study of some well-known theories of social and information networks and their applications on real world datasets.
Views: 4110 Social Networks
Social Network Datasets
Social Network Datasets
Views: 4386 Social Networks
Network Analysis. Lecture 18. Link prediction.
Link prediction problem. Proximity measures. Scoring algorithms. Prediction by supervised learning. Performance evaluation. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture18.pdf
Views: 5887 Leonid Zhukov
Mining Social Networks
This video is part of a series showcasing the use of the ProM process mining framework. Each video focusses on a specific process mining task or algorithm. ProM is open-source and freely available at: http://www.promtools.org In this video we discuss the mining of social networks in order to gain insights into the organizational perspective of a process. This is possible in ProM using the social network mining plug-ins. The theory behind discovering social networks from event logs is described in detail in: http://dx.doi.org/10.1007/s10606-005-9005-9 For more information on process mining, please visit: http://www.processmining.org/ Created by: Niek Tax Special Thanks: Elham Ramezani
Views: 2560 P2Mchannel
Analysis for Social Networking Sites - Data Mining.
in this video I will show you how to analyse the Social networking sites.
Views: 104 HashTech Coders
Opinion Mining For Social Networking Site
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: 9439 Nevon Projects
Social Networks for Fraud Analytics
Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analysis offers new insights in the propagation of fraud through a network. Indeed, fraud is not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although analyses focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
Views: 9309 Bart Baesens
Data mining in social media
I used screencast-o-matic to record my presentation.
Views: 431 Bryan Russowsky
Data Mining Social Network Analysis
Data Mining Social Network Analysis 55113369 นางสาวธันย์ชนก ชักแสง
Views: 262 Tawan K.
Enterprise Connectors - Social Media Data Mining
This is a replay of the webinar covering using the CData Enterprise Connectors for FireDAC to connect to Twitter and Facebook to mine social media data. The examples are in Delphi, but they could also easily be adaptable for C++Builder too.
An introduction to Social Media Analytics
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 16984 Jigsaw Academy
CMPE 239 Social Media Data Mining
CMPE 239- Preventing Foodborne Illness by Data Mining Social Media Source:
Views: 28 Romin Oushana
Social Network Analysis
An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 5224 Microsoft Research
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
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: 2212 TEDx Talks
Data Mining and New Patterns Discovered Through Social Networking
Interview conducted on 28 July 2009 by Jordan Brown for a documentary. Film recording and editing by Jordan Brown.
Views: 135 Katina Michael
Social Network Analysis with R | Examples
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: 23695 Bharatendra Rai
Fiance Visa Denial due to Social Media Data Mining
http://www.visacoach.com/visa-denial-social-media-data-mining.html USCIS recently hired contractors to research social media to provide additional data for the extreme vetting of Fiance, Spouse and other visa applicants. Expect one’s social media “skeletons” to lead to denial. To Schedule your Free Consultation with Fred Wahl, the Visa Coach visit http://www.visacoach.com/talk.html or Call - 1-800-806-3210 ext 702 or 1-213-341-0808 ext 702 Bonus eBook “5 Things you Must Know before Applying for your Visa” get it at http://www.visacoach.com/five.html Fiancee or Spouse visa, Which one is right for you? http://imm.guru/k1vscr1 What makes VisaCoach Special? Ans: Personally Crafted Front Loaded Presentations. Front Loaded Fiance Visa Petition http://imm.guru/front Front Loaded Spouse Visa Petition http://imm.guru/frontcr1 K1 Fiancee Visa http://imm.guru/k1 K1 Fiance Visa Timeline http://imm.guru/k1time CR1 Spousal Visa http://imm.guru/cr1 CR1 Spouse Visa Timeline http://imm.guru/cr108 Green Card /Adjustment of Status http://imm.guru/gc USCIS recently announced new contracts given to companies to search through social media to collect data on Fiance and other visa applicants. Collection starts October 18. If you have any "suspect" exposure, you have only a few more days to take it down. One of VisaCoach's clients has already experienced denial due to his Facebook presence. This couple's case was as near perfect as we have seen. They were young and in love. They had known each other for a few years and had met more than once. They were evenly matched by age, values and religion. Their "front loaded" petition was awesome and included many solid evidences of their bona fides. The American sponsor even accompanied his fiancé to the interview to demonstrate his sincerity and support for the petition. After a brief interview where the sponsor was not allowed to join in nor asked any questions before, during or after, the consular officer, denied the case. The couple was devastated and confused. What could have gone wrong? A consular officer who exhibits professionalism will state the reasons for denial in writing. And provide this to the rejected applicant immediately, often at the close of the interview itself. You may not agree with the decision, but at least know what it was and then have a starting point for renewed efforts. The officer refused to provide any verbal or written explanation. All the couple had was the fiancee's memory of the interview. I asked her to write a transcript of what happened, to recall exactly what was said and even what the body language was, so that we could study this in an attempt to reconstruct what MIGHT have been in the consular officer’s mind. What seemed odd and out of context, was the consular officer made some comments about "conservative values" and what is a "woman's role in society and in the home". Those comments seemed rather strange at the time and the foreign born fiancé had no idea where those comments came from. Eventually it dawned on us. The American sponsor is active on FaceBook. He is outspoken and his views are somewhat "anti feminist". He had posted on his social media pages, and entered into many online debates, his ideas on conservative values, and HIS ideas about a women's role in the home and society. He is not a bad guy. Not a bad husband. He was just expressing his free speech. He just had some strong views that are not popular, that are not considered "politically correct". The consular officer did her own internet search, found his activity and "Was NOT amused", and denied, putting this loving couple's life's on hold. Was it fair or reasonable that they were denied?. No, I don't think so. Happy end to the story. We took down his Facebook account, reapplied, and six months later they had their visa and began their married life together in Alaska. One random consular officer searching on Facebook ended in a denial. What will happen when ALL Fiance and Spouse applications are accompanied by a detailed dossier of one's online statements, comments jokes, embarrassments, positive and negative feedback from friends or trolls? Expect disaster. Expect many more denials, simply due to exercising a US Citizen's right to free speech. In Conclusion: "Freedom of Speech", doesn't mean freedom to get your visa. The prudent path is prior to applying for a Fiance or Spouse visa to make sure there are no skeletons in your online closet. Clean or temporarily remove, or make private, potentially controversial aspects of your online and public presence before proceeding with your visa application.
Views: 16093 Visa Coach
Social Media Analytics - Twitter Analysis in R (Example @realDonaldTrump)
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: 6970 The Data Science Show
BigDataX: Clustering social networks
Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
Ben Chamberlain - Real time association mining in large social networks
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: 759 PyData
A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
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: 859 jpinfotechprojects
Social Media Data Mining With Raspberry Pi (Part 3: Operating Systems)
This video is third in a series that walks through all the steps necessary to mine and analyze social media data using the inexpensive computer called a Raspberry Pi. Part 3 describes the two operating system environments of the Raspberry Pi: the Windows-like graphic user interface and the Linux text-based terminal environment.
Views: 1377 James Cook
Data mining, social media, kids and security
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: 122 justmediadesign
Social Media Analytics Introduction
Please view the full copyright statement at: http://public.dhe.ibm.com/software/data/sw-library/services/legalnotice.pdf
Analyzing social media data with Python
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: 8241 Next Day Video
Visual Text Mining in Social Media
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: 2629 Manolis Maragoudakis
Intro - Mining Data from Social Media with Python
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: 12974 Sukhvinder Singh
Social Media Mining
Hundreds of millions of people spending countless hours on social media to share, communicate, connect, interact, and create user-generated data. Using data mining, machine learning, text mining, social network analysis, and information retrieval, we could mine valuable knowledge for social science researches and business marketing proposes. This project was our graduation project. we used a real data from Facebook to give a proper recommendation for users about movies and series due to the social group that our users belongs to, we also managed to recommend friends to a user due to interests similarity.
Social Media Data Mining with Raspberry Pi (Part 2: Raspbian OS Setup)
This video is the second in a series that walks through all necessary steps for social media data mining and analysis with Raspberry Pi. Part 2 briefly describes installation of the operating system Raspbian from a NOOBS micro SD card and initial login. Part 3 will outline the Raspian operating system. Recorded for the University of Maine at Augusta.
Views: 1335 James Cook
Social Media Data Mining & Analysis with Raspberry Pi (Part 1: Setup)
This video is the first in a series that walks through all necessary steps for social media data mining and analysis with Raspberry Pi. Part 1 describes all the necessary hardware for the project and how to set up that hardware in just five minutes. Recorded for the University of Maine at Augusta.
Views: 2557 James Cook
Social Network Mining
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: 5810 LetsGetGyan
A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
2018 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2018 and 2019 IEEE [email protected] TMKS Infotech,Bangalore
Views: 381 manju nath
Encyclopedia of Social Network Analysis and Mining
Learn more at: http://www.springer.com/978-1-4614-6169-2 Explains fundamental concepts of social networks and data mining across the disciplines in readable, authoritative entries. Addresses privacy, security, ethical, and civil liberty issues for social networks, and the application of social network methodologies to other domains. Includes methodologies for analysis of constructed networks, data mining techniques and research directions.
Views: 276 SpringerVideos
Mining Social Media Data for Understanding Students’ Learning Experiences
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 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.
Jonathan Ronen - Social Networks and Protest Participation: Evidence from 130 Million Twitter Users
Description Data mining social networks for evidence of political participation. A demonstration of python being used to data mine the twitter conversations around the #JeSuisCharlie hashtag, and analyzing it to learn about real world protest behavior. Abstract Pinning down the role of social ties in the decision to protest has been notoriously elusive, largely due to data limitations. The era of social media and its global use by protesters offers an unprecedented opportunity to observe real-time social ties and online behavior, though often without an attendant measure of real-world behavior. We collect data on Twitter activity during the 2015 Charlie Hebdo protest in Paris which, unusually, record real-world protest attendance and high-resolution network structure. We draw on a theory of participation in which protest decisions depend on exposure to others' intentions, and network position determines exposure. Our findings are strong and consistent with this theory, showing that, relative to comparable Twitter users, protesters are significantly more connected to one another via direct, indirect, triadic, and reciprocated ties. These results offer the first large-scale empirical support for the claim that social network structure has consequences for protest participation. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 342 PyData
Data mining from social media for discovering trends in prescription medication abuse
Abeed Sarker, PHD, Research Associate , Informatics, Health Language Processing Lab,The University of Pennsylvania, presents at the Healthcare Informatics Presentation Series, hosted by The Department of Biomedical and Health Informatics at Children's Hospital of Philadelphia.
Views: 26 CHOP DBHi
Explanation of "Social Network Analysis" for Hong Kong IT Job Advertisement Data Mining Report
Explanation what is the Social Network Analysis. What is IT Term Matrix Social Network, and explains the Analytic Analogy. How to play the Interactive IT Term Matrix Social Network. http://itjobanalysis.data-hk.com/
Views: 320 Cyrus Wong
Social Media Data Mining with Raspberry Pi (Part 7: Saving Data as CSV)
This video is seventh in a series for **absolute beginners** who would like to use an inexpensive, accessible computer called the Raspberry Pi in order to carry out social media data mining and analysis. In this installment, I walk through the process for storing social media data you've collected in the universally-accessible delimited format called CSV. We use the Python library CSV and consider ways to make a CSV format better organized and more useful. Coming up in installment number 8: working with Twitter and the csv.writer command to form data into appropriate shapes to characterize links, hashtags and relationships.
Views: 1055 James Cook