Paper by J. Ramon Gil‐Garcia, Paul Henman, and Martha Alicia Avila‐Maravilla: “In the last two decades, Internet portals have been used by governments around the world as part of very diverse strategies from service provision to citizen engagement. Several authors propose that there is an evolution of digital government reflected in the functionality and sophistication of these portals and other technologies. More recently, scholars and practitioners are proposing different conceptualizations of “government as a platform” and, for some, this could be the next stage of digital government. However, it is not clear what are the main differences between a sophisticated Internet portal and a platform. Therefore, based on an analysis of three of the most advanced national portals, this ongoing research paper explores to what extent these digital efforts clearly represent the basic characteristics of platforms. So, this paper explores questions such as: (1) to what extent current national portals reflect the characteristics of what has been called “government as a platform?; and (2) Are current national portals evolving towards “government as a platform”?…(More)”.
By Michelle Winowatan, Andrew J. Zahuranec, Andrew Young, Stefaan Verhulst, Max Jun Kim
The Living Library Index – inspired by the Harper’s Index – provides important statistics and highlights global trends in governance innovation. This installment focuses on the data universe.
Please share any additional, illustrative statistics on data, or other issues at the nexus of technology and governance, with us at email@example.com
- Percentage of the world’s population that uses the internet: 51.2% (3.9 billion people) – 2018
- Number of search processed worldwide by Google every year: at least 2 trillion – 2016
- Website traffic worldwide generated through mobile phones: 52.2% – 2018
- The total number of mobile subscriptions in the first quarter of 2019: 7.9 billion (addition of 44 million in quarter) – 2019
- Amount of mobile data traffic worldwide: nearly 30 billion GB – 2018
- Data category with highest traffic worldwide: video (60%) – 2018
- Global average of data traffic per smartphone per month: 5.6 GB – 2018
- Time between the creation of each new bitcoin block: 9.27 minutes – 2019
- Total hours of video streamed by Netflix users every minute: 97,222 – 2017
- Hours of YouTube watched per day: over 1 billion – 2018
- Number of tracks uploaded to Spotify every day: Over 20,000 – 2019
- Number of Spotify’s monthly active users: 232 million – 2019
- Spotify’s total subscribers: 108 million – 2019
- Spotify’s hours of content listened: 17 billion – 2019
- Total number of songs on Spotify’s catalog: over 30 million – 2019
- Apple Music’s total subscribers: 60 million – 2019
- Total number of songs on Apple Music’s catalog: 45 million – 2019
- Number of snaps shared by Snapchat users every day: Over 3.5 billion – 2017
- Number of tweets sent every day: 500 million – 2019
- Number of Instagram users: over 700 million – 2017
- Amount of data created by Facebook in a day: 4,000,000 GB – 2014
- Number of LinkedIn members: 645 million – 2019
- LinkedIn sign-up rate: 2 members per second – 2019
- Number of photos and videos shared on Instagram every day: 95 million – 2019
- Tinder dates per week: 1 million – 2019
- Total matches on Tinder: over 30 billion – 2019
- Most popular month on Tinder in the US: August – 2018
- Day: Monday – 2018
- Time of day: 9 PM EST – 2018
Calls and Messaging:
- Estimated robocalls made in the US: 47.8 billion – 2018
- Number of messages sent over WhatsApp each day: 65 billion – 2018
- Minutes of voice and video calls made on WhatsApp each day: 2 billion – 2018
- Top 3 most popular messaging apps worldwide: WhatsApp, Facebook Messenger, WeChat – 2019
- Worldwide email users: 2.943 billion – 2019
- Number of emails sent/received per day: 246.5 billion – 2019
- Number of packages shipped by Amazon in a year: 5 billion – 2017
- Total value of payments processed by Venmo in a year: USD 62 billion – 2019
- Based on an independent analysis of public transactions on Venmo in 2017:
- Based on a non-representative survey of 2,436 US consumers between the ages of 21 and 72 on P2P platforms:
- The average volume of transactions handled by Venmo: USD 64.2 billion – 2019
- The average volume of transactions handled by Zelle: USD 122.0 billion – 2019
- The average volume of transactions handled by PayPal: USD 141.8 billion – 2019
- Platform with the highest percent adoption among all consumers: PayPal (48%) – 2019
Internet of Things:
- Number of connected IoT devices worldwide: 8.3 billion – 2018
- Number of new devices connected to the Internet every second: 127 – 2017
- Number of wearable devices: 526 million – 2017
- Based on aggregated and anonymized data of Fitbit users from January 1, 2018 – 2018
- Total steps taken: 27 trillion – 2018
- Total hours slept: 12 billion – 2018
- Total active minutes: 119 billion – 2018
- Top 5 countries/territories with most steps: Hong Kong, Spain, Ireland, Sweden, Germany – 2018
- Top 5 countries that get the most sleep: Finland, New Zealand, Ireland, Belgium, Netherlands – 2018
- Top 5 US locales with the lowest resting heart rate: Bend, OR; Santa Barbara, Santa Maria, and San Luis Obispo, CA; Twin Falls, ID; Monterey-Salinas, CA; Juneau, AK – 2018
- Amount of data produced by an autonomous car in a one and a half hour of driving: – 4,000 GB
- Al-Heeti, Abrar. “WhatsApp: 65B Messages Sent Each Day, and More than 2B Minutes of Calls.” CNET, May 1, 2018. https://www.cnet.com/news/whatsapp-65-billion-messages-sent-each-day-and-more-than-2-billion-minutes-of-calls/.
- Bhuiyan, Johana. “Uber Powered Four Billion Rides in 2017. It Wants to Do More — and Cheaper — in 2018.” Vox, January 5, 2018. https://www.vox.com/2018/1/5/16854714/uber-four-billion-rides-coo-barney-harford-2018-cut-costs-customer-service.
- Blockchain Staff. “Bitcoin Currency Statistics.” Blockchain.com, August 2019. https://www.blockchain.com/stats.
- Carman, Ashley. “Amazon Shipped over 5 Billion Items Worldwide through Prime in 2017.” The Verge, January 2, 2018. https://www.theverge.com/2018/1/2/16841786/amazon-prime-2017-users-ship-five-billion.
- Cisco®. “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017–2022 White Paper.” Cisco, February 18, 2019. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.html.
- Clement, J. “Mobile Share of Website Visits Worldwide 2018.” Statistica, July 22, 2019. https://www.statista.com/statistics/241462/global-mobile-phone-website-traffic-share/.
- ———. “Most Popular Messaging Apps 2019.” Statistica, August 9, 2019. https://www.statista.com/statistics/258749/most-popular-global-mobile-messenger-apps/.
- Desjardins, Jeff. “How Much Data Is Generated Each Day?” World Economic Forum, April 17, 2019. https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/.
- Do Thi Duc, Hang. “PUBLIC BY DEFAULT – Venmo Stories of 2017.” Public By Default FYI, 2018. https://publicbydefault.fyi.
- Dwyer, Erin. “2017 on Netflix – A Year in Bingeing.” Netflix Media Center, December 11, 2017. https://media.netflix.com/en/press-releases/2017-on-netflix-a-year-in-bingeing.
- Fisher, Christine. “Apple Music Now Has 60 Million Paid Subscribers.” Engadget, June 27, 2019. https://www.engadget.com/2019/06/27/apple-music-60-million-paid-subscribers/.
- Instagram. “700 Million.” Instagram Press (blog), April 26, 2017. https://instagram-press.com/blog/2017/04/26/700-million/.
- Jonsson, Peter, Stephen Carson, Andres Torres, Per Lindberg, Kati Öhman, Athanasios Karapantelakis, Shamil Bajgin, et al. “Ericsson Mobility Report.” Stockholm, Sweden: Ericsson, 2019. https://www.ericsson.com/49d1d9/assets/local/mobility-report/documents/2019/ericsson-mobility-report-june-2019.pdf.
- Lasse Lueth, Knud. “State of the IoT 2018: Number of IoT Devices Now at 7B – Market Accelerating,” August 8, 2018. https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/.
- Levenson, Josh, and Parker Hall. “Apple Music vs. Spotify.” Digital Trends, August 7, 2019. https://www.digitaltrends.com/music/apple-music-vs-spotify/.
- LinkedIn. “About Us.” LinkedIn, 2019. https://news.linkedin.com/about-us.
- Patel, Mark, Jason Shangkuan, and Christopher Thomas. “What’s New with the Internet of Things? | McKinsey.” McKinsey & Company, May 2017. https://www.mckinsey.com/industries/semiconductors/our-insights/whats-new-with-the-internet-of-things.
- Trefis Research Team. “Estimating Lyft’s Valuation.” Trefis, 2019. https://dashboards.trefis.com/no-login-required/zrRBRShU/Estimating-Lyft’s-Valuation.
- Rooney, Kate. “PayPal’s Venmo Had a Break-out Quarter with Payments Surging 80%.” CNBC, January 31, 2019. https://www.cnbc.com/2019/01/31/venmo-had-a-break-out-quarter-but-wont-make-money-for-paypal-until-at-mid-2019–.html.
- Shevlin, Ron. “Fintech Adoption in the US: The Opportunity for Banks and Credit Unions.” Scottsdale, AZ: Cornerstone Advisors, 2018. https://www.q2ebanking.com/wp-content/uploads/2019/01/20181107-Q2-Fintech-Adoption-Index.pdf.
- Smith. “Fitbit’s Fittest: The Countries (And Cities) That Stepped It Up and Slept More In 2018.” Fitbit Blog, January 12, 2019. https://blog.fitbit.com/fitbit-year-in-review-2018/.
- Snap, Inc. “Snap Inc. Reports Fourth Quarter and Full Year 2017 Results.” Snap, February 6, 2018. https://investor.snap.com/~/media/Files/S/Snap-IR/reports-and-presentations/q4-17-earnings-slides.pdf.
- Spotify. “Music – FAQ.” Spotify, 2019. https://artists.spotify.com/faq/music.
- ———. “Spotify Reports Second Quarter 2019 Earnings.” Spotify, July 31, 2019. https://newsroom.spotify.com/2019-07-31/spotify-reports-second-quarter-2019-earnings/.
- Sullivan, Danny. “Google Now Handles at Least 2 Trillion Searches per Year.” Search Engine Land, May 24, 2016. https://searchengineland.com/google-now-handles-2-999-trillion-searches-per-year-250247.
- The Radicati Group, Inc. “Email Statistics Report, 2015-2019: Executive Summary.” The Radicati Group, Inc, March 2015. https://www.radicati.com/wp/wp-content/uploads/2015/02/Email-Statistics-Report-2015-2019-Executive-Summary.pdf.
- Tinder Press Team. “Tinder Press and Brand Assets.” Tinder, 2019. https://tinder.com.
- Tinder Staff. “This Is What Happened On Tinder In 2018.” Swipe Life, December 5, 2018. https://swipelife.tinder.com/post/tinder-2018.
- Twitter, Inc. “Twitter for Business.” Twitter, 2019. https://business.twitter.com/en.html.
- Wiener, Janet, and Nathan Bronson. “Facebook’s Top Open Data Problems.” Facebook Research (blog), October 22, 2014. https://research.fb.com/blog/2014/10/facebook-s-top-open-data-problems/.
- Winter, Kathy. “For Self-Driving Cars, There’s Big Meaning Behind One Big Number: 4 Terabytes.” Intel Newsroom, April 14, 2017. https://newsroom.intel.com/editorials/self-driving-cars-big-meaning-behind-one-number-4-terabytes/.
- YouMail. “YouMail Robocall Index: July 2019 Nationwide Robocall Data.” Robocall Index, 2019. https://robocallindex.com/.
- YouTube Press Team. “Press – YouTube.” YouTube, August 2019. https://www.youtube.com/yt/about/press/.
- Zavazava, Cosmas, Rati Skhirtladze, Vanessa Gray, Esperanza Magpantay, Daniela Pokorna, Martin Schaaper, and Ivan Vallejo. “Measuring the Information Society Report 2018 – Volume 1.” Geneva, Switzerland: International Telecommunication Union, 2018. https://www.itu.int/en/ITU-D/Statistics/Documents/publications/misr2018/MISR-2018-Vol-1-E.pdf.
Report by Beth Noveck and Rod Glover: “Governments of all political stripes are being buffeted by technological and societal change. There is a pervasive sense globally that governments are not doing as well as they ought to solve our biggest policy problems. Pressure has intensified to provide better services and experiences, and deliver measurable results that improve people’s lives. The failure to meet our most pressing challenges help to explain why in Australia, trust in government is at an all-time low. New technologies, however, bring with them the opportunity to rethink how the public sector in Australia might solve public problems by building a workforce with diverse and innovative skills, especially how to use data and actively reach out beyond the public sector itself.
Commissioned by the Australia and New Zealand School of Government (ANZSOG), this report builds on a pioneering survey of almost 400 public servants in Australia and New Zealand, dozens of interviews with senior practitioners, and original research into how governments around the world are training public officials in innovative practices.
The survey findings show that public servants are eager to embrace skills for innovation but receive inadequate training in them….(More)”
Tooran Alizadeh, Somwrita Sarkar and Sandy Burgoyne in Cities: “Social media and online communication have changed the way citizens engage in all aspects of lives from shopping and education, to how communities are planned and developed. It is no longer one-way or two- way communication. Instead, via networked all-to-all communication channels, our citizens engage on urban issues in a complex and more connected way than ever before. So government needs new ways to listen to its citizens. The paper comprises three components. Firstly, we build on the growing discussions in the literature focused on smart cities, on one hand, and social media research, on the other, to capture the diversity of citizen voices and better inform decision-making. Secondly, with the support of the Australian Federal Government and in collaboration with the local government partners, we collect citizen voices from Twitter on selected urban projects. Thirdly, we present preliminary findings in terms of quantity and quality of publicly available online data representing citizen concerns on the urban matters. By analyzing the sentiments of the citizen voices captured online, clustering them into topic areas, and then reevaluating citizen’s sentiments within each cluster, we elaborate the scope and value of technologically-enabled opportunities in terms of enabling participatory local government decision making processes….(More)”.
Article by Priceonomics Data Studio: “For all the talk of how data is the new oil and the most valuable resource of any enterprise, there is a deep dark secret companies are reluctant to share — most of the data collected by businesses simply goes unused.
This unknown and unused data, known as dark data comprises more than half the data collected by companies. Given that some estimates indicate that 7.5 septillion (7,700,000,000,000,000,000,000) gigabytes of data are generated every single day, not using most of it is a considerable issue.
In this article, we’ll look at this dark data. Just how much of it is created by companies, what are the reasons this data isn’t being analyzed, and what are the costs and implications of companies not using the majority of the data they collect.
Before diving into the analysis, it’s worth spending a moment clarifying what we mean by the term “dark data.” Gartner defines dark data as:
“The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).
To learn more about this phenomenon, Splunk commissioned a global survey of 1,300+ business leaders to better understand how much data they collect, and how much is dark. Respondents were from IT and business roles, and were located in Australia, China, France, Germany, Japan, the United States, and the United Kingdom. across various industries. For the report, Splunk defines dark data as: “all the unknown and untapped data across an organization, generated by systems, devices and interactions.”
While the costs of storing data has decreased overtime, the cost of saving septillions of gigabytes of wasted data is still significant. What’s more, during this time the strategic importance of data has increased as companies have found more and more uses for it. Given the cost of storage and the value of data, why does so much of it go unused?
The following chart shows the reasons why dark data isn’t currently being harnessed:
By a large margin, the number one reason given for not using dark data is that companies lack a tool to capture or analyze the data. Companies accumulate data from server logs, GPS networks, security tools, call records, web traffic and more. Companies track everything from digital transactions to the temperature of their server rooms to the contents of retail shelves. Most of this data lies in separate systems, is unstructured, and cannot be connected or analyzed.
Second, the data captured just isn’t good enough. You might have important customer information about a transaction, but it’s missing location or other important metadata because that information sits somewhere else or was never captured in useable format.
Additionally, dark data exists because there is simply too much data out there and a lot of is unstructured. The larger the dataset (or the less structured it is), the more sophisticated the tool required for analysis. Additionally, these kinds of datasets often time require analysis by individuals with significant data science expertise who are often is short supply.
The implications of the prevalence are vast. As a result of the data deluge, companies often don’t know where all the sensitive data is stored and can’t be confident they are complying with consumer data protection measures like GDPR. …(More)”.
Paper by Chris Culnane, Benjamin I. P. Rubinstein, and Vanessa Teague: “The subject of this report is the re-identification of individuals in the Myki public transport dataset released as part of the Melbourne Datathon 2018. We demonstrate the ease with which we were able to re-identify ourselves, our co-travellers, and complete strangers; our analysis raises concerns about the nature and granularity of the data released, in particular the ability to identify vulnerable or sensitive groups…..
This work highlights how a large number of passengers could be re-identified in the 2018 Myki data release, with detailed discussion of specific people. The implications of re-identification are potentially serious: ex-partners, one-time acquaintances, or other parties can determine places of home, work, times of travel, co-travelling patterns—presenting risk to vulnerable groups in particular…
In 2018 the Victorian Government released a large passenger centric transport dataset to a data science competition—the 2018 Melbourne Datathon. Access to the data was unrestricted, with a URL provided on the datathon’s website to download the complete dataset from an Amazon S3 Bucket. Over 190 teams continued to analyse the data through the 2 month competition period. The data consisted of touch on and touch off events for the Myki smart card ticketing system used throughout the state of Victoria, Australia. With such data, contestants would be able to apply retrospective analyses on an entire public transport system, explore suitability of predictive models, etc.
The Myki ticketing system is used across Victorian public transport: on trains, buses and trams. The dataset was a longitudinal dataset, consisting of touch on and touch off events from Week 27 in 2015 through to Week 26 in 2018. Each event contained a card identifier (cardId; not the actual card number), the card type, the time of the touch on or off, and various location information, for example a stop ID or route ID, along with other fields which we omit here for brevity. Events could be indexed by the cardId and as such, all the events associated with a single card could be retrieved. There are a total of 15,184,336 cards in the dataset—more than twice the 2018 population of Victoria. It appears that all touch on and off events for metropolitan trains and trams have been included, though other forms of transport such as intercity trains and some buses are absent. In total there are nearly 2 billion touch on and off events in the dataset.
No information was provided as to the de-identification that was performed on the dataset. Our analysis indicates that little to no de-identification took place on the bulk of the data, as will become evident in Section 3. The exception is the cardId, which appears to have been mapped in some way from the Myki Card Number. The exact mapping has not been discovered, although concerns remain as to its security effectiveness….(More)”.
Book edited by Masamichi Sasaki: “… deals with conceptual, theoretical and social interaction analyses, historical data on societies, national surveys or cross-national comparative studies, and methodological issues related to trust. The authors are from a variety of disciplines: psychology, sociology, political science, organizational studies, history, and philosophy, and from Britain, the United States, the Czech Republic, the Netherlands, Australia, Germany, and Japan. They bring their vast knowledge from different historical and cultural backgrounds to illuminate contemporary issues of trust and distrust. The socio-cultural perspective of trust is important and increasingly acknowledged as central to trust research. Accordingly, future directions for comparative trust research are also discussed….(More)”.
Paper by Toby Walsh, Neil Levy, Genevieve Bell, Anthony Elliott, James Maclaurin, Iven Mareels, Fiona Woods: “As Artificial Intelligence (AI) becomes more advanced its applications will become increasingly complex and will find their place in homes, work places and cities.
AI offers broad-reaching opportunities, but uptake also carries serious implications for human capital, social inclusion, privacy and cultural values to name a few. These must be considered to pre-empt responsible deployment.
This project examined the potential that Artificial Intelligence (AI) technologies have in enhancing Australia’s wellbeing, lifting the economy, improving environmental sustainability and creating a more equitable, inclusive and fair society. Placing society at the core of AI development, the report analyses the opportunities, challenges and prospects that AI technologies present, and explores considerations such as workforce, education, human rights and our regulatory environment.
- AI offers major opportunities to improve our economic, societal and environmental wellbeing, while also presenting potentially significant global risks, including technological unemployment and the use of lethal autonomous weapons. Further development of AI must be directed to allow well-considered implementation that supports our society in becoming what we would like it to be – one centred on improving prosperity, reducing inequity and achieving continued betterment.
- Proactive engagement, consultation and ongoing communication with the public about the changes and effects of AI will be essential for building community awareness. Earning public trust will be critical to enable acceptance and uptake of the technology.
- The application of AI is growing rapidly. Ensuring its continued safe and appropriate development will be dependent on strong governance and a responsive regulatory system that encourages innovation. It will also be important to engender public confidence that the goods and services driven by AI are at, or above, benchmark standards and preserve the values that society seeks.
- AI is enabled by access to data. To support successful implementation of AI, there is a need for effective digital infrastructure, including data centres and structures for data sharing, that makes AI secure, trusted and accessible, particularly for rural and remote populations. If such essential infrastructure is not carefully and appropriately developed, the advancement of AI and the immense benefits it offers will be diminished.
- Successful development and implementation of AI will require a broad range of new skills and enhanced capabilities that span the humanities, arts and social sciences (HASS) and science, technology, engineering and mathematics (STEM) disciplines. Building a talent base and establishing an adaptable and skilled workforce for the future will need education programs that start in early childhood and continue throughout working life and a supportive immigration policy.
- An independently led AI body that brings stakeholders together from government, academia and the public and private sectors would provide a critical mass of skills and institutional leadership to develop AI technologies, as well as promote engagement with international initiatives and to develop appropriate ethical frameworks….(More)”.
RMIT: “Researchers used data from mobile phone accelerometers – the tiny sensors tracking phone movement for step-counting and other apps – to predict people’s personalities.
RMIT University computer scientist Associate Professor Flora Salim said previous studies had predicted personality types using phone call and messaging activity logs, but this study showed adding accelerometer data improved accuracy
“Activity like how quickly or how far we walk, or when we pick up our phones up during the night, often follows patterns and these patterns say a lot about our personality type,” said Salim, a leading expert in human mobility data.
Physical activity is proven to have a strong correlation with human personality. Therefore, researchers analysed physical activity features from different dimensions like dispersion, diversity, and regularity.
Key findings from the study:
- People with consistent movements on weekday evenings were generally more introverted, while extroverts displayed more random patterns, perhaps meeting up with different people and taking up unplanned options.
- Agreeable people had more random activity patterns and were busier on weekends and weekday evenings than others.
- Friendly and compassionate females made more outgoing calls than anyone else.
- Conscientious, organized people didn’t tend to contact the same person often in a short space of time.
- Sensitive or neurotic females often checked their phones or moved with their phones regularly well into the night, past midnight. Sensitive or neurotic males did the opposite.
- More inventive and curious people tended to make and receive fewer phone calls compared to others….(More)”
Laurence Scott at the New Atlantis: “But while there are few things more clearly of-the-moment than our biggest video-sharing site, YouTube is also the closest thing we have invented to a time machine: Its channels open new routes back to the past. Over these years I’ve come to understand that my YouTube, what I make of it, is one of the most melancholy places I’ve ever visited. I find that I turn to it to experience an exquisite kind of sadness, born from its way of restoring lost time only to take it away once more. The scenes and atmospheres of the past that come and go — as copyright infringements are enforced or channels simply subside — are like digital visitations, having the capriciousness and the fragility of all revenants.
history of our era may one day be told through the hungry, wide-angle lens of YouTube. Adding hundreds of hours of footage to its archive every minute, YouTube captures the appetites and deliriums of our times. Historians of the future will be able to trace contemporary ethics in the site’s “community guidelines.” This evolving document records our prohibitions. It defines the territory of acceptable behavior and the scope of our vision, setting limits on what we can permit one another to see. How will the short-lived “Bird Box Challenge” — in which people recorded themselves performing daily tasks blindfolded, endangering themselves and others in imitation of the eponymous film — come to mark our relationship to reality in our increasingly mediated, movie-like world?
The digital era has given more people than ever before the ability to turn into instant videographers, recording life as it occurs simply by holding up a smartphone. Consider the relative rarity of citizen footage of 9/11, compared to how comprehensively that event would have been documented today.With the improving robustness of live-streaming software, it’s not surprising that video-hosting sites such as YouTube and Facebook have become broadcasters of the ever-unfolding moment. Both sites were widely criticized after the mass shooting at a mosque in Christchurch, New Zealand in March, which the perpetrator live-streamed on Facebook. The initial stream was viewed live by about two hundred people, but before Facebook removed it, users recorded it and re-uploaded it to Facebook over a million times. They also uploaded it to YouTube: A spokesperson told the Guardian that the site had received an “unprecedented” volume of content showing the horrific event, with the rate reaching a new video uploaded every second. The sites struggled to subdue these gruesome scenes, which nightmarishly returnedmore quickly than their content moderators, both human and automated, could remove them…
But while there are few things more clearly of-the-moment than our biggest video-sharing site, YouTube is also the closest thing we have invented to a time machine: Its channels open new routes back to the past. Over these years I’ve come to understand that my YouTube, what I make of it, is one of the most melancholy places I’ve ever visited. I find that I turn to it to experience an exquisite kind of sadness, born from its way of restoring lost time only to take it away once more. The scenes and atmospheres of the past that come and go — as copyright infringements are enforced or channels simply subside — are like digital visitations, having the capriciousness and the fragility of all revenants….(More)”.