Category Archives: Humanitarian Crises

Data show an increase in hate crimes

When, now President, Donald J. Trump was elected in the United States’ elections in November 8, 2016, it ‘whipped’ up a storm of emotions, reactions, and actions. Some were positive, some were negative, and some outright worrisome. In my previous posts I have written about social media engagement through #resist and how social media was used to organize activists and supporters for the Women’s March. Something that I have yet to cover is that many hate crimes and hate speeches have emerged as well.

After the US elections in 2016, Ushahidi, a crowdsourcing tool, converted their USA Election Monitoring platform to start monitoring post election hate speech, harassment, violence, threats, and protests. By November 18, 2016, the crowdsourcing data collection had received over 800 reports (though some were duplicates). Ushahidi “was developed to map reports of violence in Kenya after the post-election violence in 2008.” The tech organization, headquartered in Nairobi, has since then been used by thousands to raise their voice.

Another, US based organization, Southern Poverty Law Center’s (SPLC) project Hatewatch in collaboration with ProPublica had by February 7, 2017, registered 1,372 post-election bias incidents. The data was collected through their #ReportHate intake page and from news reports. SPLC has partnered with ProPublica in order to better document, verify, and investigate these incidents. The New York police reported that between 2015 to 2016 hate crimes had increased by 31.5%, up from 250 to 328. Hate crimes targeting Muslims are up from 12  to 25, and hate crimes targeting Jews are up from 102 to 111. Overall, according to SPLC’s data, anti-immigrant incidents remain the most reported. Below is a chart of the highest motivational factors for the hate incidents:

Snapshot of data of Southern Poverty Law Center and ProPublica data collection of Hate Incident Motivation post-election in the United States, 2016: https://www.splcenter.org/hatewatch/2017/02/10/post-election-bias-incidents-1372-new-collaboration-propublica   

The authors Read, Taithe, and Mac Ginty examines technological innovation, primarily digital technology, and the promise it shows for improving humanitarian responses (Read, Taithe, & Mac 2016: 7f). The data technologies used by the three forementioned organizations create awareness, and it could be argued that they are empowering. There have been multiple cases of where crowdsourcing of data on violence which have had somewhat different outcomes. For example, the Libya Crisis Map, has involved the coordination and vertical transmission of knowledge of urgent situations to national and international actors and audiences. In another example, in Kenya, the knowledge took a horizontal form, used by locals to alert each other of unfolding situations (Read, Taithe, & Mac 2016: 11).

So how does it work? Ushahidi collects data by using custom surveys as well as crowdsourcing tools. The organization’s tools are not only used for monitoring elections, but also for crisis response, as well as advocacy and human rights. The sources stem from surveys, and third party emails,Twitter, Twillio, Nexmo, and SMS connected with an SMS gateway or SMSsync, amongst others. If subscribed, organizations and users can receive important alerts and notifications while deployed. What is interesting with Ushahidi is that it is not an organization from the global North and thus challenging not only the norm of what a humanitarian tech organization looks like and where it comes from, but that they also become a stakeholder in the power of knowledge (Read, Taithe, & Mac 2016: 12f). Below, is an example from Ushahidi’s site: a Muslim NYC Transit worker who was pushed down flights of stairs and called terrorist.

Snapshot of Ushahidi’s platform to monitor USA post election hate crimes. https://usaelectionmonitor.ushahidi.io/views/map

However, Read, Taithe, and Mac suggests that while collecting data by and for domestic and international development may have several advantages, it also comes with responsibilities and risks. The authors write that the evidence thus far suggests that the information gathering capabilities of some humanitarian actors outstrip their capacity to deal with the information.

Ultimately we conclude that the new aspiration towards hubristic big data processing is just another step in the same modernist process of the production of statistical truth. Where it holds a particularly seductive power is in the promise that it may, somehow, become autonomous of human intervention, magnified in legitimacy and relevance by the new processing technologies (Read, Taithe, & Mac 2016: 13).

The unfolding situation in the United States hits home as my father is Muslim, and my father’s side of the family are too, yet the minority Muslim community is just one of many targeted minority groups in the country; the communities of African-Americans, LGBTQs, refugees, immigrants, and women all over the United States are targeted too. This hits home because I have Muslim friends, immigrant friends, African-American friends, LGBTQ friends, and jewish friends, many of whom are women, myself included. This hits home because the misconceptions, stereotypes, and convictions, of these minority groups are so strong that it will lead to violence, threats, and harassment.

References – imbedded in the text and:

Read, R., Taithe, B., and Mac Ginty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming.

Big Data in Crises: Predicting the Future

Little ‘Data’, aNto on Flickr CC by A

Gathering, processing and interpreting data sets is what runs the modern global economy. Everything from your weekly online supermarket grocery shop, to how the shelves get stacked with goods delivered by cargo ships from all across the world. Teams of statisticians make their daily bread from finding more and more sophisticated ways of predicting human behaviour.

Complex mathematical modelling is also being used in humanitarian emergency situations to prevent further loss of life. I’m not just referring to how aid is delivered through more efficient supply chains, but to how the mapping of crises and outbreaks of diseases is used to instigate the correct response and predict how the spread is evolving.

Dr Sebastian Funk at the London School of Hygiene and Tropical medicine is a leader in the field and was at the centre of important work to map the Ebola outbreak in West Africa. By processing data collected from the affected area Dr Funk and peers across the world were able to look almost 6 weeks into the future, with 95% confidence of the first week.

Finding a ‘magic bullet’ or key to suppressing an outbreak is time sensitive – one must collect enough quality data to make sure that the models can be accurate, but when people’s lives are at stake conclusions need be drawn quickly.

It was found that ‘most people infected with the deadly virus became ill through contact with a small number of so-called ‘superspreaders’ and ‘if superspreading had been under control, about two-thirds of Ebola cases could have been avoided’.

Here he is speaking to RFI’s English service

Dr Funk’s work was reliant on effective feedback on the ground. He knew that whilst cases might be dropping off, there are unreported areas and if the superspreaders had been identified earlier, lives could have been saved.

The unprecedented rise of smartphone and social media has changed the data landscape. Agencies can have access to crowd-sourced information, which taken together can be highly accurate.

This was already happening during the Ebola outbreak – ‘When epidemiological data are scarce, social media and Internet reports can be reliable tools for forecasting infectious disease outbreaks’, from findings published in the Journal of Infectious Diseases.

Patrick Meier advocates the use of geographically mapping social media posts –  in Digital Humanitarians: how BIG DATA is changing the face of humanitarian response he describes crisis mapping of the 2010 Haitian earthquake and Libyan political turmoil in 2011 known as the Arab Spring. This approach allows aid workers to have a more complete live overview of the situation that’s constantly updated.

Libya Crisis Map Deployment 2011 Report

The crowd-sourced reports are collated by a volunteer team that work 24hrs a day, corroborating information from social media sources. It led to a significant change in the response, something which was praised by UNOCHA, if not without some reservations.

In this case, the role of the human as an arbiter of trustworthiness remains a significant undertaking. Even with pleas like: ‘we just need to make sure you’re not Gaddafi!…we are not Facebook!’ (Meier, p.125) for people to declare their background information, the digital gathering of sources, even on a big scale, still has to have an element of journalistic rigour.

The future will be to use artificial intelligence to perform checks that people simply don’t have the capacity to do, given the volume of information coming in. Systems are being developed that analyse qualitative rather than quantitative qualities of posts, allowing computers to detect false rumours and unrelated background noise.

With accurate models, the prediction capabilities in future outbreaks of disease or disaster will certainly be enhanced, leading to an untold impact on lives saved.


Leave your comments or tweet @data_bigly if you want to join the conversation.

References. Links in text plus:

Mier, Patrick. Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response CRC Press, London. 2015