This emoji could mean your suicide risk is high, according to AI

Curated on Posted on by Stefaan Verhulst

Rebecca Ruiz at Mashable: “Since its founding in 2013, the free mental health support service Crisis Text Line has focused on using data and technology to better aid those who reach out for help. 

Unlike helplines that offer assistance based on the order in which users dialed, texted, or messaged, Crisis Text Line has an algorithm that determines who is in most urgent need of counseling. The nonprofit is particularly interested in learning which emoji and words texters use when their suicide risk is high, so as to quickly connect them with a counselor. Crisis Text Line just released new insights about those patterns. 

Based on its analysis of 129 million messages processed between 2013 and the end of 2019, the nonprofit found that the pill emoji, or 💊, was 4.4 times more likely to end in a life-threatening situation than the word suicide. 

Other words that indicate imminent danger include 800mg, acetaminophen, excedrin, and antifreeze; those are two to three times more likely than the word suicide to involve an active rescue of the texter. The loudly crying emoji face, or 😭, is similarly high-risk. In general, the words that trigger the greatest alarm suggest the texter has a method or plan to attempt suicide or may be in the process of taking their own life. …(More)”.

Our personal health history is too valuable to be harvested by the tech giants

Curated on Posted on by Stefaan Verhulst

Eerke Boiten at The Guardian: “…It is clear that the black box society does not only feed on internet surveillance information. Databases collected by public bodies are becoming more and more part of the dark data economy. Last month, it emerged that a data broker in receipt of the UK’s national pupil database had shared its access with gambling companies. This is likely to be the tip of the iceberg; even where initial recipients of shared data might be checked and vetted, it is much harder to oversee who the data is passed on to from there.

Health data, the rich population-wide information held within the NHS, is another such example. Pharmaceutical companies and internet giants have been eyeing the NHS’s extensive databases for commercial exploitation for many years. Google infamously claimed it could save 100,000 lives if only it had free rein with all our health data. If there really is such value hidden in NHS data, do we really want Google to extract it to sell it to us? Google still holds health data that its subsidiary DeepMind Health obtained illegally from the NHS in 2016.

Although many health data-sharing schemes, such as in the NHS’s register of approved data releases], are said to be “anonymised”, this offers a limited guarantee against abuse.

There is just too much information included in health data that points to other aspects of patients’ lives and existence. If recipients of anonymised health data want to use it to re-identify individuals, they will often be able to do so by combining it, for example, with publicly available information. That this would be illegal under UK data protection law is a small consolation as it would be extremely hard to detect.

It is clear that providing access to public organisations’ data for research purposes can serve the greater good and it is unrealistic to expect bodies such as the NHS to keep this all in-house.

However, there are other methods by which to do this, beyond the sharing of anonymised databases. CeLSIUS, for example, a physical facility where researchers can interrogate data under tightly controlled conditions for specific registered purposes, holds UK census information over many years.

These arrangements prevent abuse, such as through deanonymisation, do not have the problem of shared data being passed on to third parties and ensure complete transparency of the use of the data. Online analogues of such set-ups do not yet exist, but that is where the future of safe and transparent access to sensitive data lies….(More)”.

Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach

Curated on Posted on by Stefaan Verhulst

Paper by Rabia I.Kodapanakka, lMark J.Brandt, Christoph Kogler, and Iljavan Beest: “Big data technologies have both benefits and costs which can influence their adoption and moral acceptability. Prior studies look at people’s evaluations in isolation without pitting costs and benefits against each other. We address this limitation with a conjoint experiment (N = 979), using six domains (criminal investigations, crime prevention, citizen scores, healthcare, banking, and employment), where we simultaneously test the relative influence of four factors: the status quo, outcome favorability, data sharing, and data protection on decisions to adopt and perceptions of moral acceptability of the technologies.

We present two key findings. (1) People adopt technologies more often when data is protected and when outcomes are favorable. They place equal or more importance on data protection in all domains except healthcare where outcome favorability has the strongest influence. (2) Data protection is the strongest driver of moral acceptability in all domains except healthcare, where the strongest driver is outcome favorability. Additionally, sharing data lowers preference for all technologies, but has a relatively smaller influence. People do not show a status quo bias in the adoption of technologies. When evaluating moral acceptability, people show a status quo bias but this is driven by the citizen scores domain. Differences across domains arise from differences in magnitude of the effects but the effects are in the same direction. Taken together, these results highlight that people are not always primarily driven by self-interest and do place importance on potential privacy violations. They also challenge the assumption that people generally prefer the status quo….(More)”.

The Story of Goldilocks and Three Twitter’s APIs: A Pilot Study on Twitter Data Sources and Disclosure

Curated on Posted on by Stefaan Verhulst

Paper by Yoonsang Kim, Rachel Nordgren and Sherry Emery: “Public health and social science increasingly use Twitter for behavioral and marketing surveillance. However, few studies provide sufficient detail about Twitter data collection to allow either direct comparisons between studies or to support replication.

The three primary application programming interfaces (API) of Twitter data sources are Streaming, Search, and Firehose. To date, no clear guidance exists about the advantages and limitations of each API, or about the comparability of the amount, content, and user accounts of retrieved tweets from each API. Such information is crucial to the validity, interpretation, and replicability of research findings.

This study examines whether tweets collected using the same search filters over the same time period, but calling different APIs, would retrieve comparable datasets. We collected tweets about anti-smoking, e-cigarettes, and tobacco using the aforementioned APIs. The retrieved tweets largely overlapped between three APIs, but each also retrieved unique tweets, and the extent of overlap varied over time and by topic, resulting in different trends and potentially supporting diverging inferences. Researchers need to understand how different data sources can influence both the amount, content, and user accounts of data they retrieve from social media, in order to assess the implications of their choice of data source…(More)”.

Twitter might have a better read on floods than NOAA

Curated on Posted on by Stefaan Verhulst

Interview by By Justine Calma: “Frustrated tweets led scientists to believe that tidal floods along the East Coast and Gulf Coast of the US are more annoying than official tide gauges suggest. Half a million geotagged tweets showed researchers that people were talking about disruptively high waters even when government gauges hadn’t recorded tide levels high enough to be considered a flood.

Capturing these reactions on social media can help authorities better understand and address the more subtle, insidious ways that climate change is playing out in peoples’ daily lives. Coastal flooding is becoming a bigger problem as sea levels rise, but a study published recently in the journal Nature Communications suggests that officials aren’t doing a great job of recording that.

The Verge spoke with Frances Moore, lead author of the new study and a professor at the University of California, Davis. This isn’t the first time that she’s turned to Twitter for her climate research. Her previous research also found that people tend to stop reacting to unusual weather after dealing with it for a while — sometimes in as little as two years. Similar data from Twitter has been used to study how people coped with earthquakes and hurricanes…(More)”.

The many perks of using critical consumer user data for social benefit

Curated on Posted on by Stefaan Verhulst

Sushant Kumar at LiveMint: “Business models that thrive on user data have created profitable global technology companies. For comparison, market capitalization of just three tech companies, Google (Alphabet), Facebook and Amazon, combined is higher than the total market capitalization of all listed firms in India. Almost 98% of Facebook’s revenue and 84% of Alphabet’s come from serving targeted advertising powered by data collected from the users. No doubt, these tech companies provide valuable services to consumers. It is also true that profits are concentrated with private corporations and societal value for contributors of data, that is, the user, can be much more significant….

In the existing economic construct, private firms are able to deploy top scientists and sophisticated analytical tools to collect data, derive value and monetize the insights.

Imagine if personalization at this scale was available for more meaningful outcomes, such as for administering personalized treatment for diabetes, recommending crop patterns, optimizing water management and providing access to credit to the unbanked. These socially beneficial applications of data can generate undisputedly massive value.

However, handling critical data with accountability to prevent misuse is a complex and expensive task. What’s more, private sector players do not have any incentives to share the data they collect. These challenges can be resolved by setting up specialized entities that can manage data—collect, analyse, provide insights, manage consent and access rights. These entities would function as a trusted intermediary with public purpose, and may be named “data stewards”….(More)”.

See also: http://datastewards.net/ and https://datacollaboratives.org/

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

Curated on Posted on by Stefaan Verhulst

Paper by Geoff Boeing et al: “Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities.

Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases….(More)”.

International Humanitarian and Development Aid and Big Data Governance

Curated on Posted on by Stefaan Verhulst

Chapter by Andrej Zwitter: “Modern technology and innovations constantly transform the world. This also applies to humanitarian action and development aid, for example: humanitarian drones, crowd sourcing of information, or the utility of Big Data in crisis analytics and humanitarian intelligence. The acceleration of modernization in these adjacent fields can in part be attributed to new partnerships between aid agencies and new private stakeholders that increasingly become active, such as individual crisis mappers, mobile telecommunication companies, or technological SMEs.

These partnerships, however, must be described as simultaneously beneficial as well as problematic. Many private actors do not subscribe to the humanitarian principles (humanity, impartiality, independence, and neutrality), which govern UN and NGO operations, or are not even aware of them. Their interests are not solely humanitarian, but may include entrepreneurial agendas. The unregulated use of data in humanitarian intelligence has already caused negative consequences such as the exposure of sensitive data about aid agencies and of victims of disasters.

This chapter investigates the emergent governance trends around data innovation in the humanitarian and development field. It takes a look at the ways in which the field tries to regulate itself and the utility of the humanitarian principles for Big Data analytics and data-driven innovation. It will argue that it is crucially necessary to formulate principles for data governance in the humanitarian context in order to ensure the safeguarding of beneficiaries that are particularly vulnerable. In order to do that, the chapter proposes to reinterpret the humanitarian principles to accommodate the new reality of datafication of different aspects of society…(More)”.

The New City Regulators: Platform and Public Values in Smart and Sharing Cities

Curated on Posted on by Stefaan Verhulst

Paper by Sofia Ranchordás and Catalina Goanta: “Cities are increasingly influenced by novel and cosmopolitan values advanced by transnational technology providers and digital platforms. These values which are often visible in the advancement of the sharing economy and smart cities, may differ from the traditional public values protected by national and local laws and policies. This article contrasts the public values created by digital platforms in cities with the democratic and social national values that the platform society is leaving behind.

It innovates by showing how co-regulation can balance public values with platform values. In this article, we argue that despite the value-creation benefits produced by the digital platforms under analysis, public authorities should be aware of the risks of technocratic discourses and potential conflicts between platform and local values. In this context, we suggest a normative framework which enhances the need for a new kind of knowledge-service creation in the form of local public-interest technology. Moreover, our framework proposes a negotiated contractual system that seeks to balance platform values with public values in an attempt to address the digital enforcement problem driven by the functional sovereignty role of platforms….(More)”.

What if you ask and they say yes? Consumers' willingness to disclose personal data is stronger than you think

Curated on Posted on by Stefaan Verhulst

Grzegorz Mazurek and Karolina Małagocka at Business Horizons: “Technological progress—including the development of online channels and universal access to the internet via mobile devices—has advanced both the quantity and the quality of data that companies can acquire. Private information such as this may be considered a type of fuel to be processed through the use of technologies, and represents a competitive market advantage.

This article describes situations in which consumers tend to disclose personal information to companies and explores factors that encourage them to do so. The empirical studies and examples of market activities described herein illustrate to managers just how rewards work and how important contextual integrity is to customer digital privacy expectations. Companies’ success in obtaining client data depends largely on three Ts: transparency, type of data, and trust. These three Ts—which, combined, constitute a main T (i.e., the transfer of personal data)—deserve attention when seeking customer information that can be converted to competitive advantage and market success….(More)”.