17
Dec 16

Hashtags, algorithms, ethical rules and regulation

by K. Tatakis

Competition sometimes is over hashtags in social media. Which hashtag will attract more users for a determined event?

Thus the more important the event is the most gets fragmented between hashtags in the social media world. An example is the recent (November 2016) earthquake in New Zealand when at least 4 hashtags like #nzearthquake , #eqnz , #nzquake , #earthquakenz appeared on Twitter next to more traditional ones’ as #earthquake.

Discussion other how algorithms influence what you’ ll see on your screen (and eventually your favorite search engine’s first pages (Meikle, 2016, p.78) is a hot and trendy topic with a variety of opinions about it.

Hashtags and algorithms, Design by K. Tatakis, December 2016

Continuing my review on Meikle’s recent book (Meikle 2016), I will stay now on users’ behaviours and users’ tiredness of exploring for long the same topic.

Meikle suggests that the continuous news flow over a certain subject, sooner or later makes the user unable or unwilling to follow developments (Meikle, 2016, p.75). There are of course are e-mail notifications, rss feeds and other tools to help people dealing with augmented flows but to my opinion users are feeling a psychological fatigue and a sense of burn-out, when constantly following the same subject. Thus it is more up to researchers to follow at long term a story than to everyday users which tend to change preferences when tired or when there is no immediate resolution over a problem.

Ethical rules: Some obey, others not

Meikle successfully questions over the significance and the importance of ethical rules on the web. Some obey on ethical principles, but often the same rules are heavily violated by individual users or even by organizations (Meikle, 2016, p.79). The advance of Syrian refugees in the heart of Europe was an example of this. Kids’ privacy was disregarded in some cases on visual documents about it…In a traditional news environment there would have been probably a debate over the need of publishing such pictures and the possible intervention of press syndicates in such a debate. Once now this is done individually by citizens – journalists and others, we are entering a new era regarding ethical rules and this landscape is is often full of new controversies.

Such a conclusion is close to the question pointed out Read, Taithe and Mac Ginty which in a recent article pointed out that “it becomes potentially problematic if technologies become naturalised and mainstreamed to the extent that they are not subject to fundamental questioning, or they exclude methodologies” (Read, Taithe, Mac Ginty, 2016, p.1325).

As traditional media get weaker and numbers of professional journalists working on night shifts reduced, the demand for 24/7 news is not always responded from such media as rapidly as a user would like. That is something that articles about last August earthquake in Italy reveal (Neri, 2016), (Minzi 2016).

The other side of the coin in this era is what Meikle calls “Being first counts more than being right” (Meikle, 2016, p.83).

Meikle’s book gives us some fresh views over an ongoing phenomenon which is social media influence on societies and every day’s life, an influence which is more significant and omnipresent than it ever was. “Communication, sharing and visibility” (Meikle, 2016, p.iii) are now more global than ever with the pros and the cons of such an expansion. “Raising public awareness” as Fuchs (2014, p.261)suggests about data handling and privacy problems could trigger a fruitful discussion over it.

A development of social media and new media for the social good will ask for new regulating schemes sooner or later, as technology only now start to mature. Will such regulation agent be social approval, self regulation, a public & experts commission, or eventually a new technology like

blockchain time will tell cause it’s only now the majority of people realizes how much the world changed over the last 20 years.

References

Fuchs, Christian (2014).Social Media: A critical introduction, London-Thousand Oaks: SAGE

Meikle, Graham (2016). Social Media: Communication, Sharing and Visibility, New York – Oxon, Routledge.

Minzi, Simone (2016, August 24). Terremoto 24 Agosto 2016: la notte in cui Twitter superò la stampa. Retrieved in November 1, 2016 from http://www.araundu.it/blog/2016/08/24/terremoto-24-agosto-2016-la-notte-in-cui-twitter-supero-la-stampa/

Neri, Gianluca (2016, August 24). Il giorno in cui in Italia morì  la stampa. Retrieved in November 1, 2016 from http://www.macchianera.net/2016/08/24/il-giorno-in-cui-in-italia-mori-la-stampa/

Read, Róisin, Taithe, Bertrand and Mac Ginty, Roger (2016). Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly Third World Quarterly, 37(8), 1314-1331. DOI: 10.1080/01436597.2015.1136208

NOTE: Product or corporate names may be registered trademarks or trademarks and are used only for identification and explanation without any intention to infringe


15
Dec 16

Emotions, big data archives and new taxonomies

by: K. Tatakis

Emotions not easily traced on Big data indexes, questions over items already missing from our digital archives and new taxonomies voluntarily or involuntarily happening in our digital world is what I will try to explore on this post. Reviewing parts of Graham Meikle’s book on “Social Media: Communication, Sharing and Visibility” (Meikle 2016), I take as an example and eventual case-study every day incidents from social environments which become more and more digital nowadays.

New alternatives, both positive and negative are being opened because of the new apps we now possess in order to solve everyday problems. Smartphones and tablets contributed a lot to the growth of time and scale we stay digitally connected and to the accession of more generations to what is digital communication.

Meikle questions how much previous journalistic patterns like the “inverted pyramid’ are used in this new environment. (Meikle, 2016,p.70). News evolution is in some cases so rapid that describing “when” is not necessarily primordial at least in social media chats, as everything happens in what I would call a prolonged now.

Meikle calls “timeliness” such a trend(Meikle, 2016, p.74).The news are often consumed at almost the same time they are produced. Missing the live development of an event is like missing the opportunity to participate in such a communication debate. It is not any more that meaningful to post something days after, a posteriori. The attention is already transferred somewhere else.

Meikle stays also on another element of the inverted pyramid, the space that “why” can occupy in such a thunderer news coverage.(Meikle, 2016, p.75).

Emotions, Big Data and Taxonomies, Design by: K.Tatakis, December 2016

Why” is not so much covered in a medium like Twitter because of space limitations, but people instead there and elsewhere prefer to describe people or events with adverbs which most commonly have a sense of exaggeration. Chatting through images, the advance of visual communication also sometimes puts “why” aside. Only critical citizens can elaborate meanings, but others just follow what’s buzzy is.

Furthermore it is very hard to be sure about “why” when trying to publish as early as possible. “Details, stories and entire controversies appear as though from nowhere and are replaced without resolution”(Bourdieu 1998 in Meikle, 2016, p.75).

Summaries, influences and buzz without resolution

No item is necessarily more important than others” Meikle notes (Meikle, 2016, p. 70). But the number of clicks, re-tweets or likes creates at the end of the day a new taxonomy of importance of this item compared to other similar items which can influence most people. “The user determines the connections as they make them, through these processes of navigation and search” as the same author suggests (Meikle, 2016, p. 70). The platform managers can also guide the navigation through suggestions, directions (interesting accounts etc. which appear when entering a platform’s “home” page). Even though you can describe that process more help or advice than manipulation, this taxonomy influences choices in many cases.

The user needs a kind of briefing, because the time she/he has to consume news or to explore what’s hapenning is limited. That’s why in today’s media landscape ads about articles or videos and apps like news managers are emerging. News managers offer what is important and thus what’s not. Many mobile phone companies turned on developing news managers, snipping the best articles or videos to watch.

Today we are used to look more infographics which is also can be a review or analysis of fragmented data in a new synthesis. Meikle notices the augmented role of such data in this new era (Meikle,2016, p.71).

The content gets connected with the success of the platform and the ability to grow its users’ base , the eventual stock price (if in a stock exchange) or the ability to attract new investors. If the platform (or the app) fails and goes down, the content disappears, sometimes regardless the value of it.

Of course fragments of these items remain in places like Internet Archive and it’s initiative The WayBack Machine which made a tremendous work of rescue. But in a search about South European digital newspapers in the nineties’ you can see that often just samples and portions (not the whole content of these media) where rescued. A part of our digital past has already vanished in some cases…

New initiatives on Big Data

Will in the future big data collectors maintain every bit of data (or information) produced? The more the base of news producers’ expands, the most difficult will be to safeguard such bases for any reason. Thus questions of choice will rise again. Prices of data storage devices are going down, but on the meanwhile digital data produced are multiplied at a blistering speed.

In Europe new initiatives are taking place in such a context, like Big Data Europe in order to connect new technology opportunities with communities and private companies. Bonding experts and citizens (and individuals’ needs) will be critical to the success of such an initiative. My opinion is that EU should listen more to individuals’ demands (not necessarily only through their representatives, but at a personal level) in order to remain popular.

Initiatives like The Linux Foundation’s summit Apache Big Data Europe” in Spain, #ApacheBigData also took place recently. But the big challenge remains. How to categorize things not expressed either visually or verbally?..

As I was typing this thought I found out that new projects come in life in this domain. Like the EU funded Mixed Emotions, a project managed by Dr. Paul Buitelaar and coordinated by the NUIGalway university. Scientists have already acknowledged the need to combine emotions and big data in order to create better understanding and I think that gives signs of optimism about the future.

References

Bourdieu, Paul (1998) On Television, New York: The New Press.

Meikle, Graham (2016). Social Media: Communication, Sharing and Visibility, New York – Oxon, Routledge.

NUIG – Dr. Paul Buitelaar (project manager)(2016). Mixed Emotions http://mixedemotions-project.eu/

NOTE: Product or corporate names may be registered trademarks or trademarks and are used only for identification and explanation without any intention to infringe


14
Dec 16

Big Data in healthcare: Dr Jekyll or Mr Hyde? Part II

Part II: the bad Mr Hyde?

By Athanasia K.

In a previous post, I have tried to show that Big Data applications could offer innovative and effective solutions towards better healthcare services.

Despite the very many promises however, Big Data applications in healthcare are not a panacea against all evils, but could also result in negative impacts with challenging aspects. And these challenges are still out there, unresolved for years now, despite the exponential technological development of the field.

In developed countries, one of the biggest concern seems to be the protection of personal data, which is even more sensitive when this is medical data. Kaplan very rightly notes that “data can be sold and replicated anywhere and, once sold, may be used for good or ill”. Furthermore, as Lunshof et al have showed, with the current IT technology we have, privacy and confidentiality can no longer be guaranteed. On the contrary, when we are dealing with the analysis of genetic samples, the re-identification of data samples back to their donor is more than possible, as Malin et al showed some years ago. But even if indeed there is a way to security and totally anonymise the samples in a genetic database, this can limit the usefulness of the data, as showed by Budimir et al.

One more point of concern on the impact of Big data in healthcare is that not all data are reliable. In fact, “people change their behavior and withhold information in order to protect their health information privacy” and  “according to a 1999 survey, nearly one in six patients withheld information, provided inaccurate information, doctor-hopped, paid out of pocket instead of using insurance, or even avoided care” as Kaplan notes. This has lead experts to fear a GIGO effect (e.g., garbage in–garbage out), and to a questioning of the reliability of this methodology to vulnerable groups and poorer regions, as also analysed previously by Shahin. However other scholars such as Alemayehu argue that “although much of real world data is sparse and a lot of the data is ‘‘dirty’’, with proper analytical, computational and data management tools, it is still useful and can support health policy decision-making”.

Adding to this conundrum of confidentiality vs usefulness, the lack of transparency in the acquisition and ownership of the data also adds more question marks in the field. It is common practice that Big data vendor companies do not disclose their contracts on the acquisition these data. As Kaplan notes, the legal framework in the United States and abroad ”does not address health data ownership clearly; it is not clear who the owner should be … Furthermore, it is also not clear where those who sell data analytics services obtain the data, or how they might use them.” Furthermore, as Kaplan continues, “vendors often consider their contracts intellectual property and do not reveal these and other contract provisions”.

But who benefits from this?

One could very logically assume that the companies involved in Big data do gain some sort of profit from this business. But what about the rest? As Kaplan notes, “the cost [of data gathering] is passed on to patients and payers, whether private of confidential. These individuals gain little benefit from the aggregation and sale of data about them, and they may even be harmed by it”. Indeed, Kaplan continues, “patients can be harmed when data about them are violated: to deny employment, credit, insurance”.

This unbalance of the distribution of benefits is more evident when we look in developing countries. As Rudan et al note, nearly all biobanks (at least back in 2011) “have been developed to address the health problems relevant to the minority of people living in wealthy countries”.  This has caused reluctance in developing countries to share their national data or permit foreign researchers to access them, in fear of exploitation. An example to illustrate this better is the one cited by Staunton and Moodley, where in “2007, Indonesia refused to share its H5N1 samples without a legally binding agreement which addressed among others, benefit arrangements and intellectual property rights”.

Apart from the benefits’ unbalance, one more real concern regarding data collection in healthcare is about the possible stigmatization of the patients in case the confidentiality of data is breached. This has been reflected even in court cases, where, as cited by Staunton and Moodley, in April 2010 the Arizona State University paid 700,000$ to the Havasupai Indian tribe as a settlement against claims of an improper use of blood samples which stigmatised the tribe. This fear of stigmatisation is also reported on African studies, where research participants fear about discrimination and possible stigmatisation of themselves and their family (see again the Staunton and Moodley paper). This aspect is more difficult to tackle since cultural differences make the analysis more difficult. As Kaplan notes, what is considered as very private, embarrassing, stigmatising, or posing grounds for discrimination varies among individuals and groups, and also differs between cultural backgrounds, places or time periods”.

But is it all that black and white, Dr Jekyll vs Mr Hyde situation when we speak about Big data for healthcare? In a forthcoming post, I’ll try to maybe find a third way of looking at this.


14
Dec 16

Big Data in healthcare: Dr Jekyll or Mr Hyde? Part I

By Athanasia K.

Big Data applications in healthcare is probably the field with the most heated discussions about the controversial impacts of this new technology. In fact, I could bet that there are not so many other discourses in this field where there is such clear contrast of benefits vs harm, individual vs common good, public vs private, data identification vs identity or last but not least, a contrast between the virtual vs the real, as Kaplan also observes. It’s like an old spaghetti western, where after a closer look in the plot we realise that what is a “good” and justified against a “bad” and condemned behaviour, really depends on the observer. I’ll start with the positive part:

Part I: the good Dr. Jekyll

According to Ya-Ri Lee et al “the field that shows the most promise among the application areas of Big Data is the medical sector”.

As Alemayehu lists in a recent paper, in the context of healthcare Big Data includes “not only electronic health records, claims data but also data captured through every conceivable medium, including Social Media, Internet search, wearable devices, video streams, and personal genomic services; it may also include data collected from randomized controlled clinical trials (particularly when dealing with high dimensional data, including genomic, laboratory, or imaging data)”. And all this vast information could be exploited in different applications.

In epidemiology, Big Data analysis’ applications can indeed offer innovative approaches in communicable diseases’ outbreak investigations, adding useful tools for more effective and cost-efficient ways to prevent and manage outbreaks. One such example is a study by the Karolinska Institute and Columbia University in response to the cholera outbreak in Haiti, where researchers have used data from mobile phone providers in order to have a better overview of population movements, and thus plan a better and more efficient action plan for managing the outbreak.

The positive impact of what Big Data has to offer is probably even more visible in the field of human genetics which traditionally had a rather slow progress due to the nature of the experiments needed to prove the field’s theoretical models (most of the experiments could not be performed due to ethical concerns). However, following the sequencing of the human genome at the beginning of our century, a brave new world has opened for human geneticists since a vast volume of raw data waiting to be analysed. Terms like “computational biology and medicine” enter the medical students’ curricula, and at least basic knowledge of database and system analytics is now a must in the modern bioscience researcher’s armory.

The genome-wide data analysis could indeed identify the causes of rare or other serious hereditary diseases, which would otherwise be difficult to identify and investigate because of their rarity. For example, the analysis of the Icelandic genetic database led to the identification of genes linked to human diseases, as cited by Kaplan.

Moreover, as Alemayehu notes, Big Data and the use of biobanks are very useful in drug development and they open revolutionary possibilities in the development of more efficient and safer drugs, in the direction of a completely personalised medicine and patient care. Furthermore, the use of smart mobile phone applications (like e.g. apps which measure the blood pressure via a smartphone screen) provide new field of direct-time monitoring of patients, as well as healthy persons, which provide again a unprecedented level of statistical information to researchers.

Apart from the science-related opportunities however, Big Data applications in healthcare could also lead to the reduction of costs. As Kaplan notes: multiple healthcare professionals, payers, researchers, and commercial enterprises can access data and reduce costs by eliminating duplication of services and conducting research on effective care.  In other words, Big Data is good for the business too, since healthcare organisations may benefit financially by selling medical records of their patients, at least in the US context described by Kaplan.

By browsing on tech-related articles, blogs and webpages one could find even more current, or futuristic applications of Big Data which will make our lives easier, safer and healthier.

But what’s the price for this? I’ll try to analyse some of the negative aspects of Big data applications in healthcare in my next post.


12
Dec 16

Big Data: from an omnipresent servant to an omnipotent master?

By Athanasia K.

Not so far ago, just in 1995, less than 1% of the Earth’s population were using internet, whereas today more than 40% has access to internet according to internet live stats. This number is constantly increasing and the broader and broader use of mobile phones, social networks, RFID tags, geolocation systems or interconnected personal or public monitoring systems (see also the internet of things for a glimpse into the future) all add to the omnipresence of information and communications’ technology (ICT) in our lives.

This exponentially fast rate of new ICT developments brings the average citizen also exponentially faster in front of new realities. New “tech” services, products words and jargon enter our lives on a daily basis and quickly become part of our reality, as if we were always twittering our news, using satellites to find our way to the supermarket, or instantly send ing pictures of our new-born cat to all known and unknown online “friends” of ours.

And if we consider that all these IT activities leave digital traces, the volume of the IT data and traces which are produced globally on a daily basis are almost impossible to conceive for our human brain. These pieces of data as single points of information might be insignificant. However, as compiled sets of data are more and more valuable for exploitation. As Boyd and Crawford note, the value comes from the patterns that can be derived from making connections between pieces of data, about an individual in relation to others, about groups of people, or simply about the structure of information itself.

This is where another jargon phrase is entering our lives: “Big Data”. This is a term which is not clearly defined, but is used to broadly describe a result of this exponential/massive increase of data gathering, their storage and their analysis capabilities as Hilbert notes in his paper.

ICT gurus and bloggers see Big Data as the new promise land which will bring innovative solutions to almost every problem in our human societies. From our personal daily routines, to innovative solutions for decision making in national and international development policies, there is something for all in the Big Data universe which promises to make things easier, cheaper, more efficient and reliable. Furthermore, milestone IT companies are aggressively promoting the use of Big Data as a tool for the benefit of our society, and which would in parallel also open new markets for the company, as the perfect win-win situation.

But is this a true wonderland?

One important concern is that Big Data is not necessarily open data. On the contrary, more and more companies are doing business in buying and selling databases of personal information, including consumer preferences and behaviour in social networks. A practice which poses considerable concerns on transparency of the transactions, respect of privacy of the data subjects, as well as about the ownership of personal data and their exploitation. This is more valid in particular for the health sector, where in black markets  medical record information is reported to be considered as more valuable information for identity theft than stolen credit card numbers are.

Furthermore, Big Data is not necessarily better data and there is a high risk of what is called “GIGO” in data analysis, that is: “garbage in-garbage out”. As Stefaan Verhulst of Markle Foundation reportedly said: “perhapsless is more” in many instances, because more data collection doesn’t mean more knowledge. It actually means much more confusion, false positives and so on. The challenge is for data holders to become more constrained in what they collect.

Big Data also is not necessarily more cost-efficient either, not at least for the less developed countries who cannot afford the necessary infrastructure, neither have adequately trained data specialists and analysts to support Big Data activities, as Hilbert notes. And here it comes the question of who really benefits from this new exciting technological opportunity?

Scholars such as Hilbert have raised concerns about a risk of widening the gap in the digital division between developed and developing countries. In addition, Rudan et al have argued for the need to develop infrastructure and personnel capacity in poorer countries and several promising activities are moving into that direction. However, the reality so far is that the challenges for a fair application of the Big Data revolution are far from resolved, as also outlined earlier in this blog by Shahin. This is in particular true for the health sector, on which I will focus in my forthcoming post.


12
Dec 16

ICT in social change: starting from a personal point of view

By Athanasia K.

In our days ICT technology advances with rhythms that are difficult to follow even for the high-tech passionates and professionals.

As I was reading the materials for the course’s assignments, this sentence caught my attention: no technology, including Big Data, is inherently good or bad for development, which was said by Kranzberg in 1986, as cited by Hilbert.

This reminded me one of my old high-school teachers and one of his favourite sayings, which was kind of a “trademark” for his science classes. He used to say to us that “technology is like a knife, you can cut bread with it and feed your family, or you can stub and kill someone with it. Whether it will be used for good or for evil, it’s all in the hand who holds it”.

This was at a time when mobile phones were just starting to enter in our everyday lives. When Google was an new, unknown spin-off company with 3-4 employees working in a garage somewhere on the other side of the Atlantic. When “social media” did not exist, when “twitter” only meant the sound of birds and “facebook” was still a nice memory of our high-school graduation year (even for Mark Zuckenberg who was only an elementary school boy at that time). In other words, when the “virtual” was far less than the “actual” in our lives.

Fast forwarding to the present, one cannot but observe the key role of ICT ended up playing in our lives, both as individuals and as society in ways that would have seem unimaginable less than two decades ago. How many of us do we really remember how science-fiction sounded when one was saying that “one day we will use our phones to send pictures” or to even “see each other when speaking”? Or how our life was without Google or Wikipedia always ready to answer any of our questions? Or how we could live our lives without internet or mobile phones at all? At least I had to force myself to remember how life was back then in my high school years when I now read old articles from that time, like this one from BBC.

But what sounded completely as a science fiction in my ears at the beginning of our century, is in less than a decade a reality. A reality which many of us in the developed world take almost for granted, as it has always been like this, or is for most people in this world. Which could be the case, since nowadays the use of high-tech ICT tools and devices is not only omnipresent in small or big aspects of our life, but also almost a necessity. And either consciously, or completely ignorant, we are all creating this necessity in our daily lives via our behaviour and choices we make.

From a high-school girl twitting about a music concert and adding “friends” on her facebook page, to the break-out of the “Arab spring” and the “occupy” -like movements, the use of social networks is increasingly influencing the way we think, behave and relate to others. From a stressed father who tries to find his way to their family’s vacation hotel in an unknown city, to a refugee who tries to avoid conflict zones on his journey to safer place, the use of geolocation systems are more and more a necessity for organising and living our lives.

These technological advances and the full use of what ICT has to offer come as natural for some. However, for others these are still a science-fiction dream to catch since the gap between technologically developed and less developed societies is still far from closed. In either case, the applications of high-tech ICT developments come with many opportunities as well as challenges for ourselves as individuals and for the societies we live in.

These impacts, opportunities and challenges of new ICT developments in our lives is what I will try to discuss in my forthcoming series of posts in this blog, focusing on what is called “Big Data“.


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