Searching for "algorithm"

social media algorithms

How algorithms impact our browsing behavior? browsing history?
What is the connection between social media algorithms and fake news?
Are there topic-detection algorithms as they are community-detection ones?
How can I change the content of a [Google] search return? Can I? 

Larson, S. (2016, July 8). What is an Algorithm and How Does it Affect You? The Daily Dot. Retrieved from
Berg, P. (2016, June 30). How Do Social Media Algorithms Affect You | Forge and Smith. Retrieved September 19, 2017, from
Oremus, W., & Chotiner, I. (2016, January 3). Who Controls Your Facebook Feed. Slate. Retrieved from
Lehrman, R. A. (2013, August 11). The new age of algorithms: How it affects the way we live. Christian Science Monitor. Retrieved from
Johnson, C. (2017, March 10). How algorithms affect our way of life. Desert News. Retrieved from
Understanding algorithms and their impact on human life goes far beyond basic digital literacy, some experts said.
An example could be the recent outcry over Facebook’s news algorithm, which enhances the so-called “filter bubble”of information.
personalized search (
Kounine, A. (2016, August 24). How your personal data is used in personalization and advertising. Retrieved September 19, 2017, from
Hotchkiss, G. (2007, March 9). The Pros & Cons Of Personalized Search. Retrieved September 19, 2017, from
Magid, L. (2012). How (and why) To Turn Off Google’s Personalized Search Results. Forbes. Retrieved from
Nelson, P. (n.d.). Big Data, Personalization and the No-Search of Tomorrow. Retrieved September 19, 2017, from


Massanari, A. (2017). #Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society19(3), 329-346. doi:10.1177/1461444815608807

community detection algorithms:

Bedi, P., & Sharma, C. (2016). Community detection in social networks. Wires: Data Mining & Knowledge Discovery6(3), 115-135.

CRUZ, J. D., BOTHOREL, C., & POULET, F. (2014). Community Detection and Visualization in Social Networks: Integrating Structural and Semantic Information. ACM Transactions On Intelligent Systems & Technology5(1), 1-26. doi:10.1145/2542182.2542193

Bai, X., Yang, P., & Shi, X. (2017). An overlapping community detection algorithm based on density peaks. Neurocomputing2267-15. doi:10.1016/j.neucom.2016.11.019

topic-detection algorithms:

Zeng, J., & Zhang, S. (2009). Incorporating topic transition in topic detection and tracking algorithms. Expert Systems With Applications36(1), 227-232. doi:10.1016/j.eswa.2007.09.013

topic detection and tracking (TDT) algorithms based on topic models, such as LDA, pLSI (, etc.

Zhou, E., Zhong, N., & Li, Y. (2014). Extracting news blog hot topics based on the W2T Methodology. World Wide Web17(3), 377-404. doi:10.1007/s11280-013-0207-7

The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks.  (

ethics of algorithm

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.


Malyarov, N. (2016, October 18). Journalism in the age of algorithms, platforms and newsfeeds | News | Retrieved September 19, 2017, from

more on algorithms in this IMS blog

see also

Library Technology Conference 2019


Intro to XR in Libraries from Plamen Miltenoff

keynote: equitable access to information

keynote spaker
the type of data: wikipedia. the dangers of learning from wikipedia. how individuals can organize mitigate some of these dangers. wikidata, algorithms.
IBM Watson is using wikipedia by algorythms making sense, AI system
youtube videos debunked of conspiracy theories by using wikipedia.

semantic relatedness, Word2Vec
how does algorithms work: large body of unstructured text. picks specific words

lots of AI learns about the world from wikipedia. the neutral point of view policy. WIkipedia asks editors present as proportionally as possible. Wikipedia biases: 1. gender bias (only 20-30 % are women).

conceptnet. debias along different demographic dimensions.

citations analysis gives also an idea about biases. localness of sources cited in spatial articles. structural biases.

geolocation on Twitter by County. predicting the people living in urban areas. FB wants to push more local news.

danger (biases) #3. wikipedia search results vs wkipedia knowledge panel.

collective action against tech: Reddit, boycott for FB and Instagram.

Mechanical Turk  algorithmic / human intersection

data labor: what the primary resources this companies have. posts, images, reviews etc.

boycott, data strike (data not being available for algorithms in the future). GDPR in EU – all historical data is like the CA Consumer Privacy Act. One can do data strike without data boycott. general vs homogeneous (group with shared identity) boycott.

the wikipedia SPAM policy is obstructing new editors and that hit communities such as women.


Twitter and Other Social Media: Supporting New Types of Research Materials

Nancy Herther Cody Hennesy

twitter librarieshow to access at different levels. methods and methodological concerns. ethical concerns, legal concerns,

tweetdeck for advanced Twitter searches. quoting, likes is relevant, but not enough, sometimes screenshot

engagement option

social listening platforms: crimson hexagon, parsely, sysomos – not yet academic platforms, tools to setup queries and visualization, but difficult to algorythm, the data samples etc. open sources tools (Urbana, Social Media microscope: SMILE (social media intelligence and learning environment) to collect data from twitter, reddit and within the platform they can query Twitter. create trend analysis, sentiment analysis, Voxgov (subscription service: analyzing political social media)

graduate level and faculty research: accessing SM large scale data web scraping & APIs Twitter APIs. Jason script, Python etc. Gnip Firehose API ($) ; Web SCraper Chrome plugin (easy tool, Pyhon and R created); Twint (Twitter scraper)

Facepager (open source) if not Python or R coder. structure and download the data sets.

TAGS archiving google sheets, uses twitter API. anything older 7 days not avaialble, so harvest every week.

social feed manager (GWUniversity) – Justin Litman with Stanford. Install on server but allows much more.

legal concerns: copyright (public info, but not beyond copyrighted). fair use argument is strong, but cannot publish the data. can analyize under fair use. contracts supercede copyright (terms of service/use) licensed data through library.

methods: sampling concerns tufekci, 2014 questions for sm. SM data is a good set for SM, but other fields? not according to her. hashtag studies: self selection bias. twitter as a model organism: over-represnted data in academic studies.

methodological concerns: scope of access – lack of historical data. mechanics of platform and contenxt: retweets are not necessarily endorsements.

ethical concerns. public info – IRB no informed consent. the right to be forgotten. anonymized data is often still traceable.

table discussion: digital humanities, journalism interested, but too narrow. tools are still difficult to find an operate. context of the visuals. how to spread around variety of majors and classes. controversial events more likely to be deleted.

takedowns, lies and corrosion: what is a librarian to do: trolls, takedown,

++++++++++++++vr in library

Crague Cook, Jay Ray

the pilot process. 2017. 3D printing, approaching and assessing success or failure.

development kit circulation. familiarity with the Oculus Rift resulted in lesser reservation. Downturn also.

An experience station. clean up free apps.

question: spherical video, video 360.

safety issues: policies? instructional perspective: curating,WI people: user testing. touch controllers more intuitive then xbox controller. Retail Oculus Rift

app Scatchfab. 3modelviewer. obj or sdl file. Medium, Tiltbrush.

College of Liberal Arts at the U has their VR, 3D print set up.
Penn State (Paul, librarian, kiniseology, anatomy programs), Information Science and Technology. immersive experiences lab for video 360.

CALIPHA part of it is xrlibraries. libraries equal education. content provider LifeLiqe STEM library of AR and VR objects.


Access for All:

accessibilityLeah Root

bloat code (e.g. cleaning up MS Word code)

ILLiad Doctype and Language declaration helps people with disabilities.



A Seat at the Table: Embedding the Library in Curriculum Development

embedded librarianembed library resources.

libraians, IT staff, IDs. help faculty with course design, primarily online, master courses. Concordia is GROWING, mostly because of online students.

solve issues (putting down fires, such as “gradebook” on BB). Librarians : research and resources experts. Librarians helping with LMS. Broadening definition of Library as support hub.

Machine Learning and the Cloud Rescue IT

How Machine Learning and the Cloud Can Rescue IT From the Plumbing Business


By Andrew Barbour     Feb 19, 2019

Many educational institutions maintain their own data centers.  “We need to minimize the amount of work we do to keep systems up and running, and spend more energy innovating on things that matter to people.”

what’s the difference between machine learning (ML) and artificial intelligence (AI)?

Jeff Olson: That’s actually the setup for a joke going around the data science community. The punchline? If it’s written in Python or R, it’s machine learning. If it’s written in PowerPoint, it’s AI.
machine learning is in practical use in a lot of places, whereas AI conjures up all these fantastic thoughts in people.

What is serverless architecture, and why are you excited about it?

Instead of having a machine running all the time, you just run the code necessary to do what you want—there is no persisting server or container. There is only this fleeting moment when the code is being executed. It’s called Function as a Service, and AWS pioneered it with a service called AWS Lambda. It allows an organization to scale up without planning ahead.

How do you think machine learning and Function as a Service will impact higher education in general?

The radical nature of this innovation will make a lot of systems that were built five or 10 years ago obsolete. Once an organization comes to grips with Function as a Service (FaaS) as a concept, it’s a pretty simple step for that institution to stop doing its own plumbing. FaaS will help accelerate innovation in education because of the API economy.

If the campus IT department will no longer be taking care of the plumbing, what will its role be?

I think IT will be curating the inter-operation of services, some developed locally but most purchased from the API economy.

As a result, you write far less code and have fewer security risks, so you can innovate faster. A succinct machine-learning algorithm with fewer than 500 lines of code can now replace an application that might have required millions of lines of code. Second, it scales. If you happen to have a gigantic spike in traffic, it deals with it effortlessly. If you have very little traffic, you incur a negligible cost.

more on machine learning in this IMS blog

Education and Ethics

4 Ways AI Education and Ethics Will Disrupt Society in 2019

By Tara Chklovski     Jan 28, 2019

In 2018 we witnessed a clash of titans as government and tech companies collided on privacy issues around collecting, culling and using personal data. From GDPR to Facebook scandals, many tech CEOs were defending big data, its use, and how they’re safeguarding the public.

Meanwhile, the public was amazed at technological advances like Boston Dynamic’s Atlas robot doing parkour, while simultaneously being outraged at the thought of our data no longer being ours and Alexa listening in on all our conversations.

1. Companies will face increased pressure about the data AI-embedded services use.

2. Public concern will lead to AI regulations. But we must understand this tech too.

In 2018, the National Science Foundation invested $100 million in AI research, with special support in 2019 for developing principles for safe, robust and trustworthy AI; addressing issues of bias, fairness and transparency of algorithmic intelligence; developing deeper understanding of human-AI interaction and user education; and developing insights about the influences of AI on people and society.

This investment was dwarfed by DARPA—an agency of the Department of Defence—and its multi-year investment of more than $2 billion in new and existing programs under the “AI Next” campaign. A key area of the campaign includes pioneering the next generation of AI algorithms and applications, such as “explainability” and common sense reasoning.

Federally funded initiatives, as well as corporate efforts (such as Google’s “What If” tool) will lead to the rise of explainable AI and interpretable AI, whereby the AI actually explains the logic behind its decision making to humans. But the next step from there would be for the AI regulators and policymakers themselves to learn about how these technologies actually work. This is an overlooked step right now that Richard Danzig, former Secretary of the U.S. Navy advises us to consider, as we create “humans-in-the-loop” systems, which require people to sign off on important AI decisions.

3. More companies will make AI a strategic initiative in corporate social responsibility.

Google invested $25 million in AI for Good and Microsoft added an AI for Humanitarian Action to its prior commitment. While these are positive steps, the tech industry continues to have a diversity problem

4. Funding for AI literacy and public education will skyrocket.

Ryan Calo from the University of Washington explains that it matters how we talk about technologies that we don’t fully understand.




Tackling Data in Libraries

Tackling Data in Libraries: Opportunities and Challenges in Serving User Communities

Submit proposals at

Deadline is Friday, March 1, 2019

Submissions are invited for the IOLUG Spring 2019 Conference, to be held May 10th in Indianapolis, IN. Submissions are welcomed from all types of libraries and on topics related to the theme of data in libraries.

Libraries and librarians work with data every day, with a variety of applications – circulation, gate counts, reference questions, and so on. The mass collection of user data has made headlines many times in the past few years. Analytics and privacy have, understandably, become important issues both globally and locally. In addition to being aware of the data ecosystem in which we work, libraries can play a pivotal role in educating user communities about data and all of its implications, both favorable and unfavorable.

The Conference Planning Committee is seeking proposals on topics related to data in libraries, including but not limited to:

  • Using tools/resources to find and leverage data to solve problems and expand knowledge,
  • Data policies and procedures,
  • Harvesting, organizing, and presenting data,
  • Data-driven decision making,
  • Learning analytics,
  • Metadata/linked data,
  • Data in collection development,
  • Using data to measure outcomes, not just uses,
  • Using data to better reach and serve your communities,
  • Libraries as data collectors,
  • Big data in libraries,
  • Privacy,
  • Social justice/Community Engagement,
  • Algorithms,
  • Storytelling, (
  • Libraries as positive stewards of user data.

Facial Recognition issues

Chinese Facial Recognition Will Take over the World in 2019

Michael K. Spencer Jan 14, 2018
The best facial recognition startups are in China, by a long-shot. As their software is less biased, global adoption is occurring via their software. This is evidenced in 2019 by the New York Police department in NYC for example, according to the South China Morning Post.
The mass surveillance state of data harvesting in real-time is coming. Facebook already rates and profiles us.

The Tech Wars come down to an AI-War

Whether the NYC police angle is true or not (it’s being hotly disputed), Facebook and Google are thinking along lines that follow the whims of the Chinese Government.

SenseTime and Megvii won’t just be worth $5 Billion, they will be worth many times that in the future. This is because a facial recognition data-harvesting of everything is the future of consumerism and capitalism, and in some places, the central tenet of social order (think Asia).

China has already ‘won’ the trade-war, because its winning the race to innovation. America doesn’t regulate Amazon, Microsoft, Google or Facebook properly, that stunts innovation and ethics in technology where the West is now forced to copy China just to keep up.

more about facial recognition in schools

ELI webinar AI and teaching

ELI Webinar | How AI and Machine Learning Shape the Future of Teaching

1/23/2019 Wed
12:00 PM – 1:00 PM
Centennial Hall – 100
Lecture Room
Anyone interested in
new methods for teaching


  • Explore what is meant by AI and how it relates to machine learning and data science
  • Identify relevant uses of AI and machine learning to advance education
  • Explore opportunities for using AI and machine learning to transform teaching
  • Understand how technology can shape open educational materials

Kyle Bowen, Director, Teaching and Learning with Technology

Jennifer Sparrow, Senior Director of Teaching and Learning With Tech,

Malcolm Brown, Director, Educause, Learning Initiative

more in this IMB blog on Jennifer Sparrow and digital fluency:


Feb 5, 2018 webinar notes

creating a jazz band of one: ThoughSourus

Eureka: machine learning tool, brainstorming engine. give it an initial idea and it returns similar ideas. Like Google: refine the idea, so the machine can understand it better. create a collection of ideas to translate into course design or others.


influencers and microinfluencers, pre- and doing the execution

place to start explore and generate content.

a machine can construct a book with the help of a person. bionic book. machine and person working hand in hand. provide keywords and phrases from lecture notes, presentation materials. from there recommendations and suggestions based on own experience; then identify included and excluded content. then instructor can construct.

Design may be the least interesting part of the book for the faculty.

multiple choice quiz may be the least interesting part, and faculty might want to do much deeper assessment.

use these machine learning techniques to build assessment. how to more effectively. inquizitive is the machine learning


students engagements and similar prompts

presence in the classroom: pre-service teachers class. how to immerse them and practice classroom management skills

First class: marriage btw VR and use of AI – an environment headset: an algorithm reacts how teachers are interacting with the virtual kids. series of variables, oppty to interact with present behavior. classroom management skills. simulations and environments otherwise impossible to create. apps for these type of interactions

facilitation, reflection and research

AI for more human experience, allow more time for the faculty to be more human, more free time to contemplate.

Jason: Won’t the use of AI still reduce the amount of faculty needed?

Christina Dumeng: @Jason–I think it will most likely increase the amount of students per instructor.

Andrew Cole (UW-Whitewater): I wonder if instead of reducing faculty, these types of platforms (e.g., analytic capabilities) might require instructors to also become experts in the various technology platforms.

Dirk Morrison: Also wonder what the implications of AI for informal, self-directed learning?

Kate Borowske: The context that you’re presenting this in, as “your own jazz band,” is brilliant. These tools presented as a “partner” in the “band” seems as though it might be less threatening to faculty. Sort of gamifies parts of course design…?

Dirk Morrison: Move from teacher-centric to student-centric? Recommender systems, AI-based tutoring?

Andrew Cole (UW-Whitewater): The course with the bot TA must have been 100-level right? It would be interesting to see if those results replicate in 300, 400 level courses

Recording available here

music literacy

The Tragic Decline of Music Literacy (and Quality)

Jon Henschen | August 16, 2018 |  529,478

Both jazz and classical art forms require not only music literacy, but for the musician to be at the top of their game in technical proficiency, tonal quality and creativity in the case of the jazz idiom. Jazz masters like John Coltrane would practice six to nine hours a day, often cutting his practice only because his inner lower lip would be bleeding from the friction caused by his mouth piece against his gums and teeth. His ability to compose and create new styles and directions for jazz was legendary. With few exceptions such as Wes Montgomery or Chet Baker, if you couldn’t read music, you couldn’t play jazz.


can you read music?

Besides the decline of music literacy and participation, there has also been a decline in the quality of music which has been proven scientifically by Joan Serra, a postdoctoral scholar at the Artificial Intelligence Research Institute of the Spanish National Research Council in Barcelona. Joan and his colleagues looked at 500,000 pieces of music between 1955-2010, running songs through a complex set of algorithms examining three aspects of those songs:

1. Timbre- sound color, texture and tone quality

2. Pitch- harmonic content of the piece, including its chords, melody, and tonal arrangements

3. Loudness- volume variance adding richness and depth

In an interview, Billy Joel was asked what has made him a standout. He responded his ability to read and compose music made him unique in the music industry, which as he explained, was troubling for the industry when being musically literate makes you stand out. An astonishing amount of today’s popular music is written by two people: Lukasz Gottwald of the United States and Max Martin from Sweden, who are both responsible for dozens of songs in the top 100 charts. You can credit Max and Dr. Luke for most the hits of these stars:

Katy Perry, Britney Spears, Kelly Clarkson, Taylor Swift, Jessie J., KE$HA, Miley Cyrus, Avril Lavigne, Maroon 5, Taio Cruz, Ellie Goulding, NSYNC, Backstreet Boys, Ariana Grande, Justin Timberlake, Nick Minaj, Celine Dion, Bon Jovi, Usher, Adam Lambert, Justin Bieber, Domino, Pink, Pitbull, One Direction, Flo Rida, Paris Hilton, The Veronicas, R. Kelly, Zebrahead

more on metaliteracies in this IMS blog

shaping the future of AI

Shaping the Future of A.I.

Daniel Burrus

Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come.

Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.

Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.

Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised LearningUnsupervised Learningand/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.

Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement. For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.

Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.

Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans. 

more on AI in this IMS blog

more on deep learning in this IMS blog

Does AI favor tyranny

Why Technology Favors Tyranny

Artificial intelligence could erase many practical advantages of democracy, and erode the ideals of liberty and equality. It will further concentrate power among a small elite if we don’t take steps to stop it.


Ordinary people may not understand artificial intelligence and biotechnology in any detail, but they can sense that the future is passing them by. In 1938 the common man’s condition in the Soviet Union, Germany, or the United States may have been grim, but he was constantly told that he was the most important thing in the world, and that he was the future (provided, of course, that he was an “ordinary man,” rather than, say, a Jew or a woman).

n 2018 the common person feels increasingly irrelevant. Lots of mysterious terms are bandied about excitedly in ted Talks, at government think tanks, and at high-tech conferences—globalizationblockchaingenetic engineeringAImachine learning—and common people, both men and women, may well suspect that none of these terms is about them.

Fears of machines pushing people out of the job market are, of course, nothing new, and in the past such fears proved to be unfounded. But artificial intelligence is different from the old machines. In the past, machines competed with humans mainly in manual skills. Now they are beginning to compete with us in cognitive skills.

Israel is a leader in the field of surveillance technology, and has created in the occupied West Bank a working prototype for a total-surveillance regime. Already today whenever Palestinians make a phone call, post something on Facebook, or travel from one city to another, they are likely to be monitored by Israeli microphones, cameras, drones, or spy software. Algorithms analyze the gathered data, helping the Israeli security forces pinpoint and neutralize what they consider to be potential threats.

The conflict between democracy and dictatorship is actually a conflict between two different data-processing systems. AI may swing the advantage toward the latter.

As we rely more on Google for answers, our ability to locate information independently diminishes. Already today, “truth” is defined by the top results of a Google search. This process has likewise affected our physical abilities, such as navigating space.

So what should we do?

For starters, we need to place a much higher priority on understanding how the human mind works—particularly how our own wisdom and compassion can be cultivated.

more on SCSU student philosophy club in this IMS blog

1 2 3 5

Skip to toolbar