The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing


Paper by Christopher Loynes, Jamal Ouenniche & Johannes De Smedt: “This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster’s geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an on-the-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further…(More)”.

The AI Powered State: What can we learn from China’s approach to public sector innovation?


Essay collection edited by Nesta: “China is striding ahead of the rest of the world in terms of its investment in artificial intelligence (AI), rate of experimentation and adoption, and breadth of applications. In 2017, China announced its aim of becoming the world leader in AI technology by 2030. AI innovation is now a key national priority, with central and local government spending on AI estimated to be in the tens of billions of dollars.

While Europe and the US are also following AI strategies designed to transform the public sector, there has been surprisingly little analysis of what practical lessons can be learnt from China’s use of AI in public services. Given China’s rapid progress in this area, it is important for the rest of the world to pay attention to developments in China if it wants to keep pace.

This essay collection finds that examining China’s experience of public sector innovation offers valuable insights for policymakers. Not everything is applicable to a western context – there are social, political and ethical concerns that arise from China’s use of new technologies in public services and governance – but there is still much that can be learned from its experience while also acknowledging what should be criticized and avoided….(More)”.

Capturing Citizens’ Information Needs through Analysis of Public Library Circulation Data


Paper by Tomoya Igarashi, Masanori Koizumi and Michael Widdersheim: “The Japanese government has initiated lifelong learning policies to promote lifelong learning to a super-aging society. It is said that lifelong learning contributes to a richer and more fulfilling life. It is within this context that public libraries have been identified as ideal facilities for promoting lifelong learning. To support lifelong learning successfully, libraries must accurately grasp citizens’ needs, all while working within limited budgets. To understand citizens’ learning needs, this study uses public library circulation data. This study is significant because such data use is often unavailable in Japan. This data was used to clarify citizens’ learning interests. Circulation data was compared from two libraries in Japan: Koto District Library in Tokyo and Tahara City Library in Aichi Prefecture. The data was used to identify general learning needs while also accounting for regional differences. The methodology and results of this research are significant for the development of lifelong learning policy and programming….(More)”.

How to Make the Perfect Citizen? Lessons from China’s Model of Social Credit System


Paper by Liav Orgad and Wessel Reijers: “The COVID19 crisis has triggered a new wave of digitalization of the lives of citizens. To counter the devastating effects of the virus, states and corporations are experimenting with systems that trace citizens as an integral part of public life. In China, a comprehensive sociotechnical system of citizenship governance has already in force with the implementation of the Social Credit System—a technology-driven project that aims to assess, evaluate, and steer the behavior of Chinese citizens.

After presenting social credit systems in China’s public and private sectors (Part I), the article provides normative standards to distinguish the Chinese system from comparable Western systems (Part II). It then shows the manner in which civic virtue is instrumentalized in China, both in content (“what” it is) and in form (“how” to cultivate it) (Part III), and claims that social credit systems represent a new form of citizenship governance, “cybernetic citizenship,” which implements different conceptions of state power, civic virtue, and human rights (Part V). On the whole, the article demonstrates how the Chinese Social Credit System redefines the institution of citizenship and warns against similar patterns that are mushrooming in the West.

The article makes three contributions: empirically, it presents China’s Social Credit Systems and reveals their data sources, criteria used, rating methods, and attached sanctions and rewards. Comparatively, it shows that, paradoxically, China’s Social Credit System is not fundamentally different than credit systems in Western societies, yet indicates four points of divergence: scope, authority, regulation, and regime. Normatively, it claims that China’s Social Credit System creates a form of cybernetic citizenship governance, which redefines the essence of citizenship….(More)”

How Civic Technology Can Help Stop a Pandemic


Jaron Lanier and E. Glen Weyl at Foreign Affairs: “The spread of the novel coronavirus and the resulting COVID-19 pandemic have provided a powerful test of social and governance systems. Neither of the world’s two leading powers, China and the United States, has been particularly distinguished in responding. In China, an initial bout of political denial allowed the virus to spread for weeks, first domestically and then globally, before a set of forceful measures proved reasonably effective. (The Chinese government also should have been better prepared, given that viruses have jumped from animal hosts to humans within its territory on multiple occasions in the past.) The United States underwent its own bout of political denial before adopting social-distancing policies; even now, its lack of investment in public health leaves it ill-equipped for this sort of emergency.

The response of the bureaucratic and often technophobic European Union may prove even worse: Italy, although far from the epicenter of the outbreak, has four times the per capita rate of cases as China does, and even famously orderly Germany is already at half China’s rate. Nations in other parts of the world, such as information-manipulating Iran, provide worse examples yet.

Focusing on the countries that have done worst, however, may be less useful at this point than considering which country has so far done best: Taiwan. Despite being treated by the World Health Organization as part of China, and despite having done far broader testing than the United States (meaning the true rate of infection is far less hidden), Taiwan has only one-fifth the rate of known cases in the United States and less than one-tenth the rate in widely praised Singapore. Infections could yet spike again, especially with the global spread making visitors from around the world vectors of the virus. Yet the story of Taiwan’s initial success is worth sharing not just because of its lessons for containing the present pandemic but also because of its broader lessons about navigating pressing challenges around technology and democracy.

Taiwan’s success has rested on a fusion of technology, activism, and civic participation. A small but technologically cutting-edge democracy, living in the shadow of the superpower across the strait, Taiwan has in recent years developed one of the world’s most vibrant political cultures by making technology work to democracy’s advantage rather than detriment. This culture of civic technology has proved to be the country’s strongest immune response to the new coronavirus….(More)”.

Data Reveals the True Impact of the Coronavirus Outbreak


Gian Volpicelli at Wired: “Something was wrong with Malaysia’s internet. It was March 13, and the more Simon Angus looked at the data, the more he suspected that the country might be in the midst of a coronavirus crisis.

Angus is an academic at Monash University and the cofounder of Kaspr Datahaus, a Melbourne-based company that analyses the quality of global internet connection to glean economic and social insights. The company monitors millions of internet-connected devices to gauge internet speed across the world. For them, a sudden deterioration in a country’s internet speed means that something is putting the network under strain. In recent weeks Kaspr’s theory is that the “something” is linked to the Covid-19 epidemics – as people who are working from home, or quarantining, or staying home as a precaution start using the internet more intensely than usual.

“For people who are in lockdown, or in panic mode, or in self-isolation, the internet has become a fundamentally important part of their information source, and of their consumption of entertainment,” Angus says.

To put it bluntly, when millions more turn on Netflix, scroll through TikTok, start a Zoom call, play Fortnite, or simply scroll idly through Twitter, that has repercussions on the quality of the country’s internet. (That is why EU commissioner Thierry Breton asked Netflix to restrict high-definition streaming until the emergency is over.)

Now, Angus’ scanning had detected that Malaysia’s internet had become over five percent slower in the March 12 to 13 timespan—worse even than in locked-down Italy. Officially, though, Malaysia had only 129 confirmed coronavirus cases—a relatively low number, although it had been inching up for a week.

What was happening, though, was that the population was cottoning on to the government’s sloppy handling of the pandemic. In late February, in what would turn out to be a monumental blunder, authorities had allowed a religious mass gathering to go ahead in Kuala Lumpur. Once Covid-19 cases linked to the event started to emerge, the government scrambled to find all the attendees, but got the numbers wrong—first saying that only 5,000 people at the gathering were Malaysia residents, then updating the figure to 10,000 and then 14,500. With the mess laid bare, many Malaysians seemed to have decided to stay at home out of sheer self-preservation…(More)”.

How Taiwan Used Big Data, Transparency and a Central Command to Protect Its People from Coronavirus


Article by Beth Duff-Brown: “…So what steps did Taiwan take to protect its people? And could those steps be replicated here at home?

Stanford Health Policy’s Jason Wang, MD, PhD, an associate professor of pediatrics at Stanford Medicine who also has a PhD in policy analysis, credits his native Taiwan with using new technology and a robust pandemic prevention plan put into place at the 2003 SARS outbreak.

“The Taiwan government established the National Health Command Center (NHCC) after SARS and it’s become part of a disaster management center that focuses on large-outbreak responses and acts as the operational command point for direct communications,” said Wang, a pediatrician and the director of the Center for Policy, Outcomes, and Prevention at Stanford. The NHCC also established the Central Epidemic Command Center, which was activated in early January.

“And Taiwan rapidly produced and implemented a list of at least 124 action items in the past five weeks to protect public health,” Wang said. “The policies and actions go beyond border control because they recognized that that wasn’t enough.”

Wang outlines the measures Taiwan took in the last six weeks in an article published Tuesday in the Journal of the American Medical Association.

“Given the continual spread of COVID-19 around the world, understanding the action items that were implemented quickly in Taiwan, and the effectiveness of these actions in preventing a large-scale epidemic, may be instructive for other countries,” Wang and his co-authors wrote.

Within the last five weeks, Wang said, the Taiwan epidemic command center rapidly implemented those 124 action items, including border control from the air and sea, case identification using new data and technology, quarantine of suspicious cases, educating the public while fighting misinformation, negotiating with other countries — and formulating policies for schools and businesses to follow.

Big Data Analytics

The authors note that Taiwan integrated its national health insurance database with its immigration and customs database to begin the creation of big data for analytics. That allowed them case identification by generating real-time alerts during a clinical visit based on travel history and clinical symptoms.

Taipei also used Quick Response (QR) code scanning and online reporting of travel history and health symptoms to classify travelers’ infectious risks based on flight origin and travel history in the last 14 days. People who had not traveled to high-risk areas were sent a health declaration border pass via SMS for faster immigration clearance; those who had traveled to high-risk areas were quarantined at home and tracked through their mobile phones to ensure that they stayed home during the incubation period.

The country also instituted a toll-free hotline for citizens to report suspicious symptoms in themselves or others. As the disease progressed, the government called on major cities to establish their own hotlines so that the main hotline would not become jammed….(More)”.

How Singapore sends daily Whatsapp updates on coronavirus


Medha Basu at GovInsider: “How do you communicate with citizens as a pandemic stirs fear and spreads false news? Singapore has trialled WhatsApp to give daily updates on the Covid-19 virus.

The World Health Organisation’s chief praised Singapore’s reaction to the outbreak. “We are very impressed with the efforts they are making to find every case, follow up with contacts, and stop transmission,” Tedros Adhanom Ghebreyesus said.

Since late January, the government has been providing two to three daily updates on cases via the messaging app. “Fake news is typically propagated through Whatsapp, so messaging with the same interface can help stem this flow,” Sarah Espaldon, Operations Marketing Manager from Singapore’s Open Government Products unit told GovInsider….

The niche system became newly vital as Covid-19 arrived, with fake news and fear following quickly in a nation that still remembers the fatal SARS outbreak of 2003. The tech had to be upgraded to ensure it could cope with new demand, and get information out rapidly before misinformation could sow discord.

The Open Government Products team used three tools to adapt Whatsapp and create a rapid information sharing system.

1. AI Translation

Singapore has four official languages – Chinese, English, Malay and Tamil. Government used an AI tool to rapidly translate the material from English, so that every community receives the information as quickly as possible.

An algorithm produces the initial draft of the translation, which is then vetted by civil servants before being sent out on WhatsApp. The AI was trained using text from local government communications so is able to translate references and names of Singapore government schemes. This project was built by the Ministry of Communication and Information and Agency for Science, Technology and Research in collaboration with GovTech.

2. Make it easy to sign up

People specify their desired language through an easy sign up form. Singapore used Form.Sg, a tool that allows officials to launch a new mailing list in 30 minutes and connect to other government systems. A government-built form ensures that data is end-to-end encrypted and connected to the government cloud.

3. Fast updates

The updates were initially too slow in reaching people. It took four hours to add new subscribers to the recipient list and the system could send only 10 messages per second. “With 500,000 subscribers, it would take almost 14 hours for the last person to get the message,” Espaldon says….(More)”.

How big data is dividing the public in China’s coronavirus fight – green, yellow, red


Article by Viola Zhou: “On Valentine’s Day, a 36-year-old lawyer Matt Ma in the eastern Chinese province of Zhejiang discovered he had been coded “red”.The colour, displayed in a payment app on his smartphone, indicated that he needed to be quarantined at home even though he had no symptoms of the dangerous coronavirus.

Without a green light from the system, Ma could not travel from his ancestral hometown of Lishui to his new home city of Hangzhou, which is now surrounded by checkpoints set up to contain the epidemic.

Ma is one of the millions of people whose movements are being choreographed by the government through software that feeds on troves of data and issues orders that effectively dictate whether they must stay in or can go to work.Their experience represents a slice of China’s desperate attempt to stop the coronavirus by using a mixed bag of cutting-edge technologies and old-fashioned surveillance. It was also a rare real-world test of the use of technology on a large scale to halt the spread of communicable diseases.

“This kind of massive use of technology is unprecedented,” said Christos Lynteris, a medical anthropologist at the University of St Andrews who has studied epidemics in China.

But Hangzhou’s experiment has also revealed the pitfalls of applying opaque formulas to a large population.

In the city’s case, there are reports of people being marked incorrectly, falling victim to an algorithm that is, by the government’s own admission, not perfect….(More)”.

Crowdsourcing data to mitigate epidemics


Gabriel M Leung and Kathy Leung at The Lancet: “Coronavirus disease 2019 (COVID-19) has spread with unprecedented speed and scale since the first zoonotic event that introduced the causative virus—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—into humans, probably during November, 2019, according to phylogenetic analyses suggesting the most recent common ancestor of the sequenced genomes emerged between Oct 23, and Dec 16, 2019. The reported cumulative number of confirmed patients worldwide already exceeds 70 000 in almost 30 countries and territories as of Feb 19, 2020, although that the actual number of infections is likely to far outnumber this case count.

During any novel emerging epidemic, let alone one with such magnitude and speed of global spread, a first task is to put together a line list of suspected, probable, and confirmed individuals on the basis of working criteria of the respective case definitions. This line list would allow for quick preliminary assessment of epidemic growth and potential for spread, evidence-based determination of the period of quarantine and isolation, and monitoring of efficiency of detection of potential cases. Frequent refreshing of the line list would further enable real-time updates as more clinical, epidemiological, and virological (including genetic) knowledge become available as the outbreak progresses….

We surveyed different and varied sources of possible line lists for COVID-19 (appendix pp 1–4). A bottleneck remains in carefully collating as much relevant data as possible, sifting through and verifying these data, extracting intelligence to forecast and inform outbreak strategies, and thereafter repeating this process in iterative cycles to monitor and evaluate progress. A possible methodological breakthrough would be to develop and validate algorithms for automated bots to search through cyberspaces of all sorts, by text mining and natural language processing (in languages not limited to English) to expedite these processes.In this era of smartphone and their accompanying applications, the authorities are required to combat not only the epidemic per se, but perhaps an even more sinister outbreak of fake news and false rumours, a so-called infodemic…(More)”.