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.

more on algorithms in this IMS blog


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

bots, big data and the future

Computational Propaganda: Bots, Targeting And The Future

February 9, 201811:37 AM ET

Combine the superfast calculational capacities of Big Compute with the oceans of specific personal information comprising Big Data — and the fertile ground for computational propaganda emerges. That’s how the small AI programs called bots can be unleashed into cyberspace to target and deliver misinformation exactly to the people who will be most vulnerable to it. These messages can be refined over and over again based on how well they perform (again in terms of clicks, likes and so on). Worst of all, all this can be done semiautonomously, allowing the targeted propaganda (like fake news stories or faked images) to spread like viruses through communities most vulnerable to their misinformation.

According to Bolsover and Howard, viewing computational propaganda only from a technical perspective would be a grave mistake. As they explain, seeing it just in terms of variables and algorithms “plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it.”

Computational propaganda is a new thing. People just invented it. And they did so by realizing possibilities emerging from the intersection of new technologies (Big Compute, Big Data) and new behaviors those technologies allowed (social media). But the emphasis on behavior can’t be lost.

People are not machines. We do things for a whole lot of reasons including emotions of loss, anger, fear and longing. To combat computational propaganda’s potentially dangerous effects on democracy in a digital age, we will need to focus on both its howand its why.

more on big data in this IMS blog

more on bots in this IMS blog

more on fake news in this IMS blog

Tinder dating privacy

I asked Tinder for my data. It sent me 800 pages of my deepest, darkest secrets

The dating app knows me better than I do, but these reams of intimate information are just the tip of the iceberg. What if my data is hacked – or sold?

Every European citizen is allowed to do so under EU data protection law, yet very few actually do, according to Tinder.

With the help of privacy activist Paul-Olivier Dehaye from and human rights lawyer Ravi Naik, I emailed Tinder requesting my personal data and got back way more than I bargained for.

Some 800 pages came back containing information such as my Facebook “likes”, links to where my Instagram photos would have been had I not previously deleted the associated account, my education, the age-rank of men I was interested in, how many Facebook friends I had, when and where every online conversation with every single one of my matches happened … the list goes on.

Reading through the 1,700 Tinder messages I’ve sent since 2013, I took a trip into my hopes, fears, sexual preferences and deepest secrets. Tinder knows me so well. It knows the real, inglorious version of me who copy-pasted the same joke to match 567, 568, and 569; who exchanged compulsively with 16 different people simultaneously one New Year’s Day, and then ghosted 16 of them.

“What you are describing is called secondary implicit disclosed information,” explains Alessandro Acquisti, professor of information technology at Carnegie Mellon University. “Tinder knows much more about you when studying your behaviour on the app. It knows how often you connect and at which times; the percentage of white men, black men, Asian men you have matched; which kinds of people are interested in you; which words you use the most; how much time people spend on your picture before swiping you, and so on. Personal data is the fuel of the economy. Consumers’ data is being traded and transacted for the purpose of advertising.”.

In May, an algorithm was used to scrape 40,000 profile images from the platform in order to build an AI to “genderise” faces. A few months earlier, 70,000 profiles from OkCupid (owned by Tinder’s parent company Match Group) were made public by a Danish researcher some commentators have labelled a “white supremacist”, who used the data to try to establish a link between intelligence and religious beliefs. The data is still out there.


more on social media dating in this IMS blog

topics for IM260

proposed topics for IM 260 class

  • Media literacy. Differentiated instruction. Media literacy guide.
    Fake news as part of media literacy. Visual literacy as part of media literacy. Media literacy as part of digital citizenship.
  • Web design / web development
    the roles of HTML5, CSS, Java Script, PHP, Bootstrap, JQuery, React and other scripting languages and libraries. Heat maps and other usability issues; website content strategy. THE MODEL-VIEW-CONTROLLER (MVC) design pattern
  • Social media for institutional use. Digital Curation. Social Media algorithms. Etiquette Ethics. Mastodon
    I hosted a LITA webinar in the fall of 2016 (four weeks); I can accommodate any information from that webinar for the use of the IM students
  • OER and instructional designer’s assistance to book creators.
    I can cover both the “library part” (“free” OER, copyright issues etc) and the support / creative part of an OER book / textbook
  • Big Data.” Data visualization. Large scale visualization. Text encoding. Analytics, Data mining. Unizin. Python, R in academia.
    I can introduce the students to the large idea of Big Data and its importance in lieu of the upcoming IoT, but also departmentalize its importance for academia, business, etc. From infographics to heavy duty visualization (Primo X-Services API. JSON, Flask).
  • NetNeutrality, Digital Darwinism, Internet economy and the role of your professional in such environment
    I can introduce students to the issues, if not familiar and / or lead a discussion on a rather controversial topic
  • Digital assessment. Digital Assessment literacy.
    I can introduce students to tools, how to evaluate and select tools and their pedagogical implications
  • Wikipedia
    a hands-on exercise on working with Wikipedia. After the session, students will be able to create Wikipedia entries thus knowing intimately the process of Wikipedia and its information.
  • Effective presentations. Tools, methods, concepts and theories (cognitive load). Presentations in the era of VR, AR and mixed reality. Unity.
    I can facilitate a discussion among experts (your students) on selection of tools and their didactically sound use to convey information. I can supplement the discussion with my own findings and conclusions.
  • eConferencing. Tools and methods
    I can facilitate a discussion among your students on selection of tools and comparison. Discussion about the their future and their place in an increasing online learning environment
  • Digital Storytelling. Immersive Storytelling. The Moth. Twine. Transmedia Storytelling
    I am teaching a LIB 490/590 Digital Storytelling class. I can adapt any information from that class to the use of IM students
  • VR, AR, Mixed Reality.
    besides Mark Gill, I can facilitate a discussion, which goes beyond hardware and brands, but expand on the implications for academia and corporate education / world
  • IoT , Arduino, Raspberry PI. Industry 4.0
  • Instructional design. ID2ID
    I can facilitate a discussion based on the Educause suggestions about the profession’s development
  • Microcredentialing in academia and corporate world. Blockchain
  • IT in K12. How to evaluate; prioritize; select. obsolete trends in 21 century schools. K12 mobile learning
  • Podcasting: past, present, future. Beautiful Audio Editor.
    a definition of podcasting and delineation of similar activities; advantages and disadvantages.
  • Digital, Blended (Hybrid), Online teaching and learning: facilitation. Methods and techniques. Proctoring. Online students’ expectations. Faculty support. Asynch. Blended Synchronous Learning Environment
  • Gender, race and age in education. Digital divide. Xennials, Millennials and Gen Z. generational approach to teaching and learning. Young vs old Millennials. Millennial employees.
  • Privacy, [cyber]security, surveillance. K12 cyberincidents. Hackers.
  • Gaming and gamification. Appsmashing. Gradecraft
  • Lecture capture, course capture.
  • Bibliometrics, altmetrics
  • Technology and cheating, academic dishonest, plagiarism, copyright.

digital darwinism

We Need New Rules for the Internet Economy

Antitrust laws only go so far when addressing companies that don’t produce any physical goods. It is time to negotiate a new set of rules. Otherwise, our future economy will be dominated by just a few companies.

A DER SPIEGEL Editorial by Armin Mahler  November 03, 2017  06:12 PM

There are still people out there who think that Amazon is nothing more than an online version of a department store. But it’s much more than that: It is a rapidly growing, global internet giant that is changing the way we shop, conquering more and more markets, using Alexa to suck up our personal data straight out of our living rooms and currently seeking access to our front door keys so it can deliver packages even when nobody’s home.

It wasn’t that long ago that EU efforts to limit the power of Google and Amazon on the European market were decried in the U.S. as protectionism, as an attempt by the Europeans to protect their own inferior digital economy. Now, though, politicians and economists in the U.S. have even begun discussing the prospect of breaking up the internet giants. The mood has shifted.

The digital economy, by contrast, is based on algorithms and its most powerful companies don’t produce any physical products. Customers receive their services free of charge, paying only with their data. The more customers a service provider attracts, the more attractive it becomes to new customers, who then deliver even more data – which is why Google and Facebook need not fear new competition.

first of all, the power of a company, and the abuse of that power, must be redefined. We cannot allow a situation in which these extremely large companies can swallow up potential rivals before they can even begin to develop. As such, company acquisitions must be monitored much more strictly than they currently are and, if need be, blocked.

Second, it must be determined who owns the data collected – whether, for example, it should also be made available to competitors or whether consumers should receive more in exchange than simply free internet search results.

Third, those disseminating content cannot be allowed to reject responsibility for that content. Demonstrably false claims and expressions of hate should not be tolerated.

And finally, those who earn lots of money must also pay lots of taxes – and not just back home but in all the countries where they do business.

more on net neutrality in this IMS blog


It is a name for a premise that, quietly, has come to regulate all we practise and believe: that competition is the only legitimate organising principle for human activity.

we now live in Hayek’s world, as we once lived in Keynes’s.
He begins by assuming that nearly all (if not all) human activity is a form of economic calculation, and so can be assimilated to the master concepts of wealth, value, exchange, cost – and especially price. Prices are a means of allocating scarce resources efficiently, according to need and utility, as governed by supply and demand. For the price system to function efficiently, markets must be free and competitive. Ever since Smith imagined the economy as an autonomous sphere, the possibility existed that the market might not just be one piece of society, but society as a whole. Within such a society, men and women need only follow their own self-interest and compete for scarce rewards. Through competition, “it becomes possible”, as the sociologist Will Davies has written, “to discern who and what is valuable”.

Hayek built into neoliberalism the assumption that the market provides all necessary protection against the one real political danger: totalitarianism.

To prevent this, the state need only keep the market free.

This last is what makes neoliberalism “neo”. It is a crucial modification of the older belief in a free market and a minimal state, known as “classical liberalism”. In classical liberalism, merchants simply asked the state to “leave us alone” – to laissez-nous faire. Neoliberalism recognised that the state must be active in the organisation of a market economy. The conditions allowing for a free market must be won politically, and the state must be re-engineered to support the free market on an ongoing basis.

Even his conservative colleagues at the University of Chicago – the global epicentre of libertarian dissent in the 1950s – regarded Hayek as a reactionary mouthpiece, a “stock rightwing man” with a “stock rightwing sponsor”, as one put it.

Milton Friedman who helped convert governments and politicians to the power of Hayek’s Big Idea. But first he broke with two centuries of precedent and declared that economics is “in principle independent of any particular ethical position or normative judgments” and is “an ‘objective’ science, in precisely the same sense as any of the physical sciences”.

The internet is personal preference magnified by algorithm; a pseudo-public space that echoes the voice already inside our head. Rather than a space of debate in which we make our way, as a society, toward consensus, now there is a mutual-affirmation apparatus banally referred to as a “marketplace of ideas”.

“A taste is almost defined as a preference about which you do not argue,” the philosopher and economist Albert O Hirschman once wrote. “A taste about which you argue, with others or yourself, ceases ipso facto being a taste – it turns into a value.”


podcast at 2x

Speeding Up Your Podcasts Won’t Solve Your Problems

My note: sometimes around 2011, the Chronicle had a report on Berkeley students listening to coursecasts at 2X (can’t find the reference). Here some other sources about #speedlistening:

Stop listening to podcasts at 1.5x

and the opposite opinion:

Lots of Us Listen to Podcasts Faster Than “Normal.” Join Us!

Aisha Harris Oct. 6 2016 1:58 PM

Watching lectures at increased speed? Discussion in ‘Medical Students – MD‘ started by kimbosliced, Dec 24, 2010.

The Rise of ‘Speed-Listening’
Books can be places for intellectual wandering. They can also be mined of precious information with ruthless efficiency.

Megan Garber

the introduction of Overcast, a podcast-playback app designed by the creator of the text-bookmaking app Instapaper. One of Overcast’s key selling points is a feature called Smart Speed. Smart Speed isn’t about simply playing audio content at 150 or 200 percent of the standard rate; it instead tries to remove, algorithmically, the extraneous things that can bulk up the play time of audio content: dead air, pauses between sentences, intros and outros, that kind of thing.
Here is also the general tendency of podcast use until 2015 from previous IMS blog

more on podcast in education in this IMS blog

anonymous browsing data

‘Anonymous’ browsing data can be easily exposed, researchers reveal

A similar strategy was used in 2008, Dewes said, to deanonymise a set of ratings published by Netflix to help computer scientists improve its recommendation algorithm: by comparing “anonymous” ratings of films with public profiles on IMDB, researchers were able to unmask Netflix users – including one woman, a closeted lesbian, who went on to sue Netflix for the privacy violation.

A hacker explains the best way to browse the internet anonymously. 

more on privacy in this IMS blog

fake news and video

Computer Scientists Demonstrate The Potential For Faking Video

As a team out of the University of Washington explains in a new paper titled “Synthesizing Obama: Learning Lip Sync from Audio,” they’ve made several fake videos of Obama.



Fake news: you ain’t seen nothing yet

Generating convincing audio and video of fake events, July 1, 2017

took only a few days to create the clip on a desktop computer using a generative adversarial network (GAN), a type of machine-learning algorithm.

Faith in written information is under attack in some quarters by the spread of what is loosely known as “fake news”. But images and sound recordings retain for many an inherent trustworthiness. GANs are part of a technological wave that threatens this credibility.

Amnesty International is already grappling with some of these issues. Its Citizen Evidence Lab verifies videos and images of alleged human-rights abuses. It uses Google Earth to examine background landscapes and to test whether a video or image was captured when and where it claims. It uses Wolfram Alpha, a search engine, to cross-reference historical weather conditions against those claimed in the video. Amnesty’s work mostly catches old videos that are being labelled as a new atrocity, but it will have to watch out for generated video, too. Cryptography could also help to verify that content has come from a trusted organisation. Media could be signed with a unique key that only the signing organisation—or the originating device—possesses.

more on fake news in this IMS blog

industry 4.0

A Strategist’s Guide to Industry 4.0. Global businesses are about to integrate their operations into a seamless digital whole, and thereby change the world.
Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II.
Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative.
The term Industry 4.0 refers to the combination of several major innovations in digital technology
These technologies include advanced robotics and artificial intelligence; sophisticated sensors; cloud computing; the Internet of Things; data capture and analytics; digital fabrication (including 3D printing); software-as-a-service and other new marketing models; smartphones and other mobile devices; platforms that use algorithms to direct motor vehicles (including navigation tools, ride-sharing apps, delivery and ride services, and autonomous vehicles); and the embedding of all these elements in an interoperable global value chain, shared by many companies from many countries.
Companies that embrace Industry 4.0 are beginning to track everything they produce from cradle to grave, sending out upgrades for complex products after they are sold (in the same way that software has come to be updated). These companies are learning mass customization: the ability to make products in batches of one as inexpensively as they could make a mass-produced product in the 20th century, while fully tailoring the product to the specifications of the purchaser

adoption industry 4.0 by sector

Three aspects of digitization form the heart of an Industry 4.0 approach.

• The full digitization of a company’s operations

•  The redesign of products and services

•  Closer interaction with customers

Making Industry 4.0 work requires major shifts in organizational practices and structures. These shifts include new forms of IT architecture and data management, new approaches to regulatory and tax compliance, new organizational structures, and — most importantly — a new digitally oriented culture, which must embrace data analytics as a core enterprise capability.

Klaus Schwab put it in his recent book The Fourth Industrial Revolution (World Economic Forum, 2016), “Contrary to the previous industrial revolutions, this one is evolving at an exponential rather than linear pace.… It is not only changing the ‘what’ and the ‘how’ of doing things, but also ‘who’ we are.”

This great integrating force is gaining strength at a time of political fragmentation — when many governments are considering making international trade more difficult. It may indeed become harder to move people and products across some national borders. But Industry 4.0 could overcome those barriers by enabling companies to transfer just their intellectual property, including their software, while letting each nation maintain its own manufacturing networks.
more on the Internet of Things in this IMS blog

also Digital Learning

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