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How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY
 
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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: 4930 TODAY
Mining Online Data Across Social Networks
 
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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: 19847 stanfordonline
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: 1843 TEDx Talks
Social Media Data - Computerphile
 
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If you're not the customer you are the product. Dr Max Wilson on the third party apps embedded in social media. EXTRA BITS: https://youtu.be/ZAHA1MYudvo Tracing Stolen Bitcoin: Coming Soon nb. "Piggie-Share" is a made-up app. If you go off and create it, best of luck to you :) (just remember us when you're a billionaire on a beach!) Dr. Wilson: https://scholar.google.com/citations?user=WkLZuJsAAAAJ&hl=en https://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Views: 57659 Computerphile
Facebook creating data-mining ‘monsters’
 
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A former official with President Barack Obama's re-election campaign says an app, that let Facebook users connect with the campaign, allowed the social media giant to pull user profile data and data from that user's friends. Meanwhile, Facebook stock is diving, following a scandal with data firm Cambridge Analytica. RT America’s Ed Schultz speaks with Georgetown journalism professor Chris Chambers for more. Find RT America in your area: http://rt.com/where-to-watch/ Or watch us online: http://rt.com/on-air/rt-america-air/ Like us on Facebook http://www.facebook.com/RTAmerica Follow us on Twitter http://twitter.com/RT_America
Views: 3712 RT America
Big Data Analytics | Tutorial #28 |  Mining Social Network Graphs
 
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This video is based on concepts like Edge betweeness and Grivan- Newman Algorithm in the social Graphs 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: 2309 Ranji Raj
Fiance Visa Denial due to Social Media Data Mining
 
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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: 10979 Visa Coach
Data mining in social media
 
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I used screencast-o-matic to record my presentation.
Views: 346 Bryan Russowsky
Social Media Data Privacy Awareness
 
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Learn more about how social media platforms, businesses, and marketers, use your personal information and posts to social media to build profiles about you.
Social Media Data Mining with Raspberry Pi (Part 7: Saving Data as CSV)
 
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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: 919 James Cook
Social Media Data Mining & Analysis with Raspberry Pi (Part 1: Setup)
 
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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: 2277 James Cook
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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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.
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.
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
Mining social media data of people with rare diseases - Laia Subirats
 
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My proposal is divided into in 3 sections. The first is to analyze Facebook data related to rare diseases. The second analyzes data from Twitter during the World Day of Rare Diseases 2017. Finally, the third explains how ontologies help to represent knowledge and shows a use case in the context of rare diseases. ------------------------- Laia Subirats (1985, Barcelona) is a data science researcher at Eurecat - Technology Centre of Catalonia and a part-time lecturer at Open University of Catalonia. She holds a PhD in Computer Science from Autonomous University of Barcelona and she has more than 10 years of experience in R&D projects in disciplines such as artificial intelligence, social networks and ontologies; both in national and international centers. She loves working in innovative, interdisciplinary and international environments and she is very passionate about improving the quality of life of people using technology. ------------------------------ Todos los videos de WTM Barcerlona https://www.youtube.com/playlist?list=PLKxa4AIfm4pUzhTXXJxFTMxSG-sA482zf Suscríbete a nuestra newsletter; https://goo.gl/5jc6uP Facebook; https://goo.gl/o8HrWX Twitter; https://goo.gl/MU5pUQ LinkedIn https://goo.gl/2On7Fj/
Views: 87 Autentia
Shedding Light on Social Media with Text Analytics
 
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Unleash the insights from social media data with text analysis. SAS shares examples of value delivered to organizations. To learn more go to: http://www.sas.com/voice
Views: 2959 SAS Software
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: 9174 Sukhvinder Singh
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: 11033 Tableau Software
Integrating & Mining Social Media Data at Scale with DataSift
 
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Companies that can harness and respond to signals present in social media can better identify company events that mightlead to an opportunity, measure consumer intent signals to identify product interest, or build custom applications to mine market-level insights at scale. Join us as we show how DataSift?s big-data platform provides one API to access multiple social networks, sophisticated social data mining tools to extract actionable data, and classification engines to enable you integrate social data into your custom applications. We?ll even send you away with your own login!
Views: 512 Dreamforce Video
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: 4613 The Data Science Show
Enterprise Connectors - Social Media Data Mining
 
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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.
Social Media Data Mining with Raspberry Pi (Part 5: Twitter, Tweepy, Python)
 
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This video is fifth in a series for absolute beginners who would like to learn how to mine and analyze social media data using an inexpensive, accessible computer called the Raspberry Pi. In this installment, I show Raspberry Pi owners how to install Tweepy, write a script in the programming language Python, and collect basic user and communication data from the social media platform Twitter. Coming up in installment number Six: how to STORE social media data you've collected into a permanent, well-organized database.
Views: 3008 James Cook
Social Media Analytics - Explained
 
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Social media analytics combines traditional business data with social media data in meaningful ways results in analytics innovation. For example, if a buyer in a major retail chain, does not fully understand the optimal time to stock soccer merchandise in key cities across Asia. He or She can turn to the social data streams, to see when people in the store locales are tweeting or posting about soccer or their favourite teams. Monitoring social media helps businesses find breakout products. Social media data is found data, and provides business and governments with an ability to observe naturally occurring conversations in message boards online, social networks and blogs to get answers about their key demographics. Data mining the social media streams of such large numbers of passive participants in natural settings, helps bring to bear the expertise, technologies, and solutions of the next generation recommender systems. These systems build on big data psychology and mental heuristics to help consumers overcome the tsunami of information. Reference: http://www.analytics.uts.edu.au/ Created at http://www.b2bwhiteboard.com
Views: 26947 B2Bwhiteboard
Social Media Data Mining With Raspberry Pi (Part 3: Operating Systems)
 
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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: 1248 James Cook
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
Data Mining Social Network Analysis
 
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Data Mining Social Network Analysis 55113369 นางสาวธันย์ชนก ชักแสง
Views: 217 Tawan K.
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: 2549 Manolis Maragoudakis
Mining Social Networks
 
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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: 1917 P2Mchannel
Social Networks for Fraud Analytics
 
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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: 8231 Bart Baesens
Social Media Data Mining with Raspberry Pi (Part 9: Custom Input)
 
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This video is ninth in a series for beginners in the use of an inexpensive, accessible Raspberry Pi computer to carry out social media data mining and analysis. In this installment, I explain and show how to modify your previous programs to allow for custom input -- this lets you run data-gathering programs very quickly and easily. To accomplish this, you only need to learn two simple commands related to user input and concatenation (the act of putting two pieces of text together).
Views: 504 James Cook
How to analyse Social Media data from Twitter in Tableau
 
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Learn how to analyse Social Media data from Twitter in Tableau Complete playlist: https://www.youtube.com/playlist?list=PLm6YM4S6arkfVaiTCF16VSZSmGiJ7kHic Further reading: http://alexloth.com/2017/09/12/social-media-customer-centric-data-strategy-data17-resources/
Views: 2198 Alexander Loth
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: 34358 Alexandra Ott
[Data on the Mind 2017] Social media analysis
 
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Abstract: People love to talk on the web. How can we listen to what they're telling us, and why would we want to? This workshop will discuss some methods of collecting social media data to construct larger---and in some cases, more naturalistic---datasets than laboratory-based experiments yield. We'll cover methods for building datasets through Python-accessible Twitter APIs, and structuring both the search query and the experimental question to obtain data that is appropriate in both content and amount. We'll also discuss connections between data and metadata, with a focus on geolocation, as well as ways to collect online conversations and interactions. Instructor: Gabriel Doyle (Stanford University) --- Before running this tutorial, you'll need to sign up with the Twitter API. Follow the instructions here: https://github.com/Data-on-the-Mind/2017-summer-workshop/blob/master/doyle-twitter/README.md --- Part of the Data on the Mind 2017 summer workshop: http://www.dataonthemind.org/2017-workshop Funded by the Estes Fund: http://www.psychonomic.org/page/estesfund Organized in collaboration with Data on the Mind: http://www.dataonthemind.org Videography by DeNoise Studios: http://www.denoise.com Workshop hashtag: #dataonthemind
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: 429 Javapocalypse
Social Media Marketing and Management - Data Mining - Text Mining - Sentimental Analysis
 
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-Explanation: A Social Media Marketing and Management Project -Lesson: Data Mining -Subject: Sentimental Analysis ( Emotional Analysis ) of Text Mining ----------- -Açıklama: Sosyal Medya ve Pazarlama Uygulaması Projesi -Ders: Veri Madenciliği -Konu: Duygusal Metin Analizi
Views: 164 Egemen Kayalidere
Social Media Data Mining with Raspberry Pi (Part 2: Raspbian OS Setup)
 
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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: 1207 James Cook
Social Media Mining
 
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Social Media Mining
Views: 364 WMAR-2 News
Social Media Mining
 
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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 8: Extracting Hashtags, URLs, Mentions)
 
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This video is eighth in a series for beginners in the use of an inexpensive, accessible Raspberry Pi computer to carry out social media data mining and analysis. In this installment, I walk through the process for extracting hashtag, URL (web address), and mentioning data from Twitter posts ("Tweets") and saving them in CSV files that are linked by a common reference to Tweet ID. Coming up in installment #9: using input commands to customize searches without changing the underlying Python code.
Views: 801 James Cook
Social Media Data Mining with Raspberry Pi (Part 6: Debugging)
 
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So you've written that conceptually brilliant program... but it just won't run. Time to debug -- to find and fix the errors. But how? There are many complicated technical methods for professional debugging, but for the program-writing neophyte, seven basic tips can really help with most debugging challenges. This video is part 6 in a YouTube series for absolute beginners among non-STEM students on taking the Raspberry Pi and using it as a cheap and robust platform for social media data mining and analysis.
Views: 830 James Cook
FDA Moving Data to the Cloud, Data Mining Social Media
 
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Taha A. Kass-Hout, MD, MS, chief health informatics officer at the Food and Drug Administration (FDA), discusses the migration of clinical research data to the cloud and how the cloud model serves as an enabler of collaboration. Dr. Kass-Hout also talks about FDA's efforts to data mine social media for possible indicators of drug safety and counterfeiting. For more information, visit http://www.npcnow.org/issues/comparative-effectiveness-research.
Views: 190 npcnow
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: 14137 Bharatendra Rai
Social Media Social Data and Python: 2 - Opportunities
 
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The key opportunity of developing data mining systems is to extract useful insights from data. The aim of the process is to answer interesting and sometimes difficult questions using data mining techniques to enrich our knowledge about a particular domain. ------ 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: 2072 Sukhvinder Singh

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