This document discusses using social network analysis and data from the IPEDS to analyze interstate student mobility patterns. It provides an overview of social network analysis techniques like sociomatrices and network graphs that can be used to visualize migration between states. The document then presents analysis of IPEDS data on first-time undergraduate enrollments between 2008-2018 to generate visualizations like choropleth maps and spider graphs of migration networks between states. It concludes by acknowledging limitations of the analysis and providing references for further research on social network analysis and student migration modeling.
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...JAYAPRAKASH JPINFOTECH
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Mining Social Media with Linked Open Data, Entity Recognition, and Event Extr...Leon Derczynski
Presented at the 4th DEOS workshop, http://diadem.cs.ox.ac.uk/deos13/
Social media presents itself as a context-rich source of big data, readily exhibiting volume, velocity and variety. Mining information from microblogs and other social media is a challenging, emerging research area. Unlike carefully authored news text and other longer content, social media text poses a number of new challenges, due to the short, noisy, context-dependent, and dynamic nature.
This talk will discuss firstly how Linked Open Data (LOD) vocabularies (namely DBpedia and YAGO) have been used to help entity recognition and disambiguation in such content. We will introduce LODIE, the LOD-based extension of the widely used ANNIE open-source entity recognition system. LODIE includes also entity disambiguation (including products, as well as names of persons, locations, and organisations) and has been developed as part of the TrendMiner and uComp projects. Quantitative evaluation results will be shown, including a comparison against other state-of-the-art methods and an analysis of how errors in upstream linguistic pre-processing (i.e. tokenisation and POS tagging) can affect disambiguation performance. Our results demonstrate the importance of adjusting approaches for this genre.
The second half of the talk will focus on fine-grained events in tweets. Awareness of temporal context in social media enables many interesting applications. We identify events using the TimeML schema, focusing on occurrences and actions. Challenges of event annotation will be discussed, as well as the development of a supervised event extractor specifically for social media. We evaluate this against traditional event annotation approaches (e.g. Evita, TIPSem).
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...JAYAPRAKASH JPINFOTECH
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Mining Social Media with Linked Open Data, Entity Recognition, and Event Extr...Leon Derczynski
Presented at the 4th DEOS workshop, http://diadem.cs.ox.ac.uk/deos13/
Social media presents itself as a context-rich source of big data, readily exhibiting volume, velocity and variety. Mining information from microblogs and other social media is a challenging, emerging research area. Unlike carefully authored news text and other longer content, social media text poses a number of new challenges, due to the short, noisy, context-dependent, and dynamic nature.
This talk will discuss firstly how Linked Open Data (LOD) vocabularies (namely DBpedia and YAGO) have been used to help entity recognition and disambiguation in such content. We will introduce LODIE, the LOD-based extension of the widely used ANNIE open-source entity recognition system. LODIE includes also entity disambiguation (including products, as well as names of persons, locations, and organisations) and has been developed as part of the TrendMiner and uComp projects. Quantitative evaluation results will be shown, including a comparison against other state-of-the-art methods and an analysis of how errors in upstream linguistic pre-processing (i.e. tokenisation and POS tagging) can affect disambiguation performance. Our results demonstrate the importance of adjusting approaches for this genre.
The second half of the talk will focus on fine-grained events in tweets. Awareness of temporal context in social media enables many interesting applications. We identify events using the TimeML schema, focusing on occurrences and actions. Challenges of event annotation will be discussed, as well as the development of a supervised event extractor specifically for social media. We evaluate this against traditional event annotation approaches (e.g. Evita, TIPSem).
A brief history of plagiarism and how to avoid itpolishedpaper
Polished Paper provides our clients with on-demand professional English language editing and proofreading services designed to meet all of your needs. Whether your goals are academic, professional, or creative, you can rest assured that Polished Paper will be with you as your partner in success, today, tomorrow, and beyond.
Know more: https://polishedpaper.com/
Good Riddance: Academic Publishers are Abandoning PublishingBjörn Brembs
Talk at RIOT science club on the myriad ways in which science would do so much better if scholarly institutions took their money and spent it on modern information technology instead of antiquated and counter-productive journals.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
A brief history of plagiarism and how to avoid itpolishedpaper
Polished Paper provides our clients with on-demand professional English language editing and proofreading services designed to meet all of your needs. Whether your goals are academic, professional, or creative, you can rest assured that Polished Paper will be with you as your partner in success, today, tomorrow, and beyond.
Know more: https://polishedpaper.com/
Good Riddance: Academic Publishers are Abandoning PublishingBjörn Brembs
Talk at RIOT science club on the myriad ways in which science would do so much better if scholarly institutions took their money and spent it on modern information technology instead of antiquated and counter-productive journals.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
From Mehlenbacher, B., McKone, S., Grant, C., Bowles, T., Peretti, S. & Martin, P. (2010). Social media for sustainable engineering communication. SIGDOC’10: The 28th ACM International Conference on Design of Communication Proceedings. São Carlos-São Paulo, Brazil: ACM, 65-72.
This paper provides an overview of current research on social media applications, including user demographics and how social media websites define themselves. The paper also describes user activities using social media and suggests known strengths and weaknesses of social media, and concludes by outlining several recommendations for developing strong online communities.
Invited talk at Session on Semantic Knowledge for Commodity Computing, at Microsoft Research Faculty Summit 2011, July 19-20, 2011, Redmond, WA. http://research.microsoft.com/en-us/events/fs2011/default.aspx
Associated video at: https://youtu.be/HKqpuLiMXRs
MidAIR 2013 Keynote - Lighting the Path to Action: Realizing the Power of Sto...Matthew Hendrickson
Invited talk presented at the Mid-America Association for Institutional Research's 2013 conference in Kansas City, Missouri.
This talk focused upon using storytelling techniques as a means to present data and gain consensus among constituents.
UPCEA New England 2013 - Non-Traditional Student Retention Model CreationMatthew Hendrickson
Presented at the University Professional & Continuing Education Association - New England's 2013 Conference with Rachael Denison.
This presentation provides context and instruction on the creation of a non-traditional student retention model.
14. The reason we are here..... Data: IPEDS 2008 Fall Enrollment Survey – Migration Institutional Characteristics - Directory Information Procedure Public/Private not-for-profit (4-year) Degree Granting Continental 48 states + DC Pset4flg = 1 Removed Students who enrolled in home state Military schools
28. References and Links Andre, M., Ijaz, K., Tilinghas, J.D., Krebs, V.E., Diem, L.A., Metchock, B., Crisp, T., & McElroy, P.D. (2006). Transmission Network Analysis to Complement Routing Tuberculosis Contact Investigations. American Journal of Public Health, 96. Cahill, Mark. (November 2009). Basics of Network Theory for Social Network Analysis (SNA) Part 1. Retrieved November 11, 2010, http://www.consultmark.ie/blog/wp-content/uploads/2009/11/basic-social-network.jpg Cheng, A. (April 2010). In Six Degrees of Separation, Twitter Style. Retrieved November 11, 2010, http://www.sysomos.com/insidetwitter/sixdegrees/ Doyle, S. (2007). The Role of Social Networks in Marketing. Journal of Database Marketing & Customer Strategy Management, 15, 60-64. DOI: 10.1057/palgrave.dbm.3250070. Hsu, W.H., King, A.L., Paradesi, M.S.R., Pydimarri, T., & Weninger, T. (2006). Collaborative and Structural Recommendation of Friends using Weblog-based Social Network Analysis. AAAI Spring Symposia 2006 on Computational Approaches to Analysing Weblogs. Maier, G. (2005). Internal Migration between US States – A Social Network Analysis. Retrieved November 13, 2010, http://iir-hp.wu-wien.ac.at/sre-disc/sre-disc-2005_04.pdf Muenchen, R.A. (2010). In The Popularity of Data Analysis Software. Retrieved November 11, 2010, http://www.r4stats.com/popularity ntamas (User of Last.fm). (n.d.). In Reconstructing the Structure of the World-Wide Music Scene with Last.fm. Retrieved November 11, 2010, http://sixdegrees.hu/last.fm/index.html Postsecondary.org (April 2010). Interstate Migration of College Freshmen 1986 to 2008. Postsecondary Education Opportunity, 214. Retrieved from http://www.postsecondary.org/last12/214_410pg1_16.pdf Scott, J.P. (2000). Social Network Analysis: A Handbook 2nd Edition. London: Sage Publications. Social Network. (n.d.). In Wikipedia. Retrieved November 11, 2010, http://en.wikipedia.org/wiki/Social_network VosFellman, P. & Wright, R. (2003). Modeling Terrorist Networks – Complex Systems at the Mid-Range. Retrieved November 13, 2010, http://www.psych.lse.ac.uk/complexity/Conference/FellmanWright.pdf
29. Resources Drew Conway ZIA http://drewconway.com/zia/ Quick-R www.statmethods.net R4Stats. http://www.r4stats.com/popularity R for SAS and SPSS Users. http://oit.utk.edu/scc/RforSAS&SPSSusers.pdf R Links for the Beginner on World Statistics Day. http://www.r-bloggers.com/r-links-for-the-beginner-on-world-statistics-day/ R Project for Statistical Computing. http://www.r-project.org/ Simple R – Using R for Introductory Statistics. http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf Social Network Analysis Resources for R. http://jeromyanglim.blogspot.com/2009/08/social-network-analysis-resources-for-r.html Software for Exploratory Data Analysis and Statistical Modeling. http://www.wekaleamstudios.co.uk/ Some Hints for the R Beginner: Avoiding “Blank Screen Syndrome”. http://www.r-bloggers.com/some-hints-for-the-r-beginner-avoiding-blank-screen-syndrome/ StackOverflow (User Forum) www.stackoverflow.com (tag = R) Statistics with R. http://zoonek2.free.fr/UNIX/48_R/all.html The igraph Library. http://igraph.sourceforge.net/ Use R – Invited Lectures. http://user2010.org/Invited/ YouTube http://www.youtube.com/results?search_query=R+statistics
30. Contact Info Brock Tibert Director of Institutional Research 781-891-2715 btibert@bentley.edu @BrockTibert Matt Hendrickson Research Analyst 781-891-2595 mhendrickson@bentley.edu
Most people think of these or similar concepts when considering social networks.They are built upon social networks, but are not social networks in an of themselves.
Have you ever noticed sections of webpages like this one from twitter?They are formulated to suggest friends based on popularity, self-selected interests, and similarity to the user.This is where social networks come into play.
Six Degrees of Separation (aka The Human Web) posits that everyone is on average approximately six steps away form any other person on Earth. This was firstpoularized by FrigyesKarinthy in 1929. It was further popularized by Stanley Milgram as the “Small World” phenomenon. Source: wikipedia.org.
http://www.sysomos.com/insidetwitter/sixdegrees/Recent work by sysomos by way of Twitter suggests otherwise. 79% are within 5 degrees of separation.
How many of you have seen graphs like these? This is SNA. You will see a few more applied to our research in the following slides.
Scott, 2000 – Page 67.Here are a few basic terms.1. Here are the Nodes: The point of the graph representing the observation.2. Here are the Edges/Lines: The line representing the relationship between nodes.Corresponds to the idea of an area of relatively high density in a graph.
Scott, 2000 – Page 67.This is the same SNA graph from the previous page. To the right is its sociomatrix.All social networks are built upon a sociomatrix. The graph is simply a representation of the matrix.This matrix is used for not only social network representation, but also for calculations pertaining to the relational data.MatrixA framework in which raw or coded data can be organized in a more or less efficient way. This is always done in the form of a table. The axes are based on any combination of cases and affiliations (Scott, 2000).
Studies are provided as a way to illustrate the growing use of SNA in multiple disciplines across the globe.Migration – Maier (2005)Friend Recommendations - Hsu, W.H., King, A.L., Paradesi, M.S.R., Pydimarri, T., & Weninger, T. (2006)Marketing – Doyle (2007)Military – VosFellman & Wright (2003)Disease Control - Andre, Ijaz, Tilinghas, Krebs, Diem, Metchock, Crisp, & McElroy (2006)
r4stats provides a link to run an updated graph.This one was run on November 11, 2010.Note:The numbers on the graph reflect how many searches have been done for a particular term, relative to the total number of searches done on Google over time. They don't represent absolute search volume numbers, because the data is normalized and presented on a scale from 0-100. Each point on the graph is divided by the highest point, or 100. When we don't have enough data, 0 is shown. The numbers next to the search terms above the graph are summaries, or totals.
This graph shows plainly that R is becoming a widely used resource if considering monthly e-mail discussion lists.Could the numbers in Figure 1 be the result of a few people doing a lot of talking? When you follow any of these discussion lists, it quickly becomes obvious that a core group of people really keep the lists humming. However the number of people who subscribe to each list shows a similar pattern with R-Help dominating the scene, see Table 1. An early version of this table failed to include subscribers to Statalist's digest version, and so under counted the total by about half.Discussion: Subscribers:R-Help 10,379Statalist 3,692SAS-L 3,253SPSSX-L 2,105Table 1. Number of subscribers for each Internet discussion list on 6/20/2010.
Not only has SNA popularity been on the rise, but so has the popularity of R.Also consider this, since the number of packages in R continue to increase, the possibility that whatever you will need will be available.For instance, once you get past the R learning curve, you can learn and implement many projects that are either not available through SPSS or SAS, but that you may not have a license to use. Just think of a project that you have to do where you only need to use a statistical process once, not providing use enough to purchase the separate software package.
http://nces.ed.gov/ipeds/datacenter/Student Migration/Interstate MobilityThe movement of students who enroll at institutions of higher education outside of their home state (postsecondary.org).1st Time Degree Seeking UG graduating HS in last 12 months (efres02)
http://www.postsecondary.org/last12/214_410pg1_16.pdfText not copyrighted. Permission is granted to make copies from this research letter providing copies are not sold and the source is identified. Copies of research letter charts are available to subscribers in larger sizes at cost. Call for assistance.1:5 leave home state
Order is fairly similar to the Postsecondary study
NJ -> PA = 9300+ studentsMean = 148, median is 28, 2200 plus rows in the datasetCut at 100 is approximately top 25% of the dataset
Main Plot - FR Layout53% of freshmen enroll 100 miles or less
Online sources – simply search for SNA online. Or give us your card/contact info and we can send you a few that we use.