2nd call: Show Me Your Dashboard - Digital Methods Winter School 2015 - Univ. of Amsterdam
This is the second call for participation in the Digital Methods Winter School at the University of Amsterdam, 12-16 January 2015. The deadline for applications is 8 December 2014. Together with Nathaniel Tkacz on dashboard critique (Univ Warwick) and Carolin Gerlitz on social media metrics (Univ Amsterdam), new speakers have confirmed from SumOfUs, UNICEF, TckTckTck, Climate Action Network and the Dutch design agency Clever Franke. We are also joined by the Density Design Lab, Milan. SHOW ME YOUR DASHBOARD New Media Monitoring and Data Analytics as Critical PracticeDigital Methods Winter School, Data Sprint and Mini-Conference *12-16 January 2015 | Digital Methods Winter School * *Digital Methods Initiative | http://www.digitalmethods.net/ <http://www.digitalmethods.net/>**Media Studies | University of Amsterdam* *https://wiki.digitalmethods.net/Dmi/WinterSchool2015* <https://wiki.digitalmethods.net/Dmi/WinterSchool2015> The Digital Methods Initiative (DMI), Amsterdam, is pleased to announce its 7th annual Winter School, on New Media Monitoring and Data Analytics as Critical Practice. The format is that of a data sprint, with hands-on work on media monitoring with data analytics, and a Mini-conference, where PhD candidates, motivated scholars and advanced graduate students present short papers on digital methods and new media related topics, and receive feedback from the Amsterdam group of DMI researchers and international participants. Participants need not give a paper at the Mini-conference to attend the Winter School. The focus of this year's Winter School is on how online media monitoring is currently done by non-governmental (NGOs) such as treealerts.org, and it seeks to identify practices that could fill in the notion of critical data analytics. For the occasion we have invited academics to present on the state of the art of online media monitoring by focusing on three areas where there is both innovation as well as repurposing of techniques normally associated with marketing, business intelligence and the work of digital agencies: issue discovery and language placement (who's carrying the conversation), engagement and public fund-raising (when do images and other engagement formats ‘work’?) and crisis communication (who is making the calls when there is a breakdown?). At the Winter School social media analysts and communications specialists from NGOs will present on the state of the art of media monitoring, their current analytical needs and what the Internet can continue to add with respect to new data sources as well as monitoring techniques. We will also ask each of the organizations to show us their dashboards. The first day kicks off with Nathaniel Tkacz from the University of Warwick who will talk about Dashboards and Data Signals <http://blogs.cim.warwick.ac.uk/dashboard/about-2/>, and the desire to control the data deluge. The second keynote speaker is Carolin Gerlitz from the University of Amsterdam who will talk about new media metrics critique. Next a series of online media monitoring dashboards and methods will be presented. The Dutch design agency Clever Franke will show TrendViz <http://www.trendviz.com/#home>. Soenke Lorenzen of Greenpeace International, Eoin Dubsky of SumOfUs, Dounia Kchiere of UNICEF, and Christian Teriete of TckTckTck will be talking about media monitoring at their respective organisations. Next will be project pitches by Ria Voorhaar of the Climate Action Network, Danie Stockmann of Leiden University, Jonathan Gray of the Open Knowledge Foundation, and Alberto Abellan of Social Alto Analytics. After the the first day of talks as well as dashboard show and tell, the data sprint commences, whereupon the attendees, including analysts, designers and programmers, undertake empirical projects that address the state of the art in NGO online media data analysis. We work on projects that seek to meet the current analytical needs. The week closes with presentations of the outcomes as well as a festive celebration. During the week there is also an evening of talks and a debate with Jimmy Wales <http://en.wikipedia.org/wiki/Jimmy_Wales>, co-founder of Wikipedia, at the nearby Royal Netherlands Academy of Arts and Science <https://www.knaw.nl/en/>. The theme of the 2015 Winter School furthers the analytical collaboration between the Digital Methods Initiative and NGO media analysts, including Soenke Lorenzen of Greenpeace International <http://www.greenpeace.org>. Previously workshop facilitators and collaborators have included representatives from Human Rights Watch <http://www.hrw.org/>, Association for Progressive Communications <https://www.apc.org/>, Women on Waves <http://www.womenonwaves.org/>, Carbon Trade Watch <http://www.carbontradewatch.org/>, Corporate Observatory Europe <http://corporateeurope.org> and Fair Phone <http://www.fairphone.com/>. In preparation for the sprint we also have developed how-to worksheets on New Media Monitoring and Tooling that take as their case studies NGO issue mappings with digital methods. Upon conclusion we aim to compile the Sprint projects from the Winter School, and combine them with the how-to sheets to produce an open access publication on NGO media monitoring. All participants are invited to contribute. Digital Methods Winter School Data Sprint A data sprint is a workshop format for intensive, empirical project work, where analysts, programers, designers and subject matter experts collaborate to output research. This year's data sprint is devoted to new media monitoring with data analytics, and particularly its critical practice. Broadly speaking, media monitoring is understood as the process of reading, watching or listening to the editorial content of media sources on a continuing basis, and then identifying, analyzing and saving materials that contain specific themes, topics, keywords, names, forms or formats. Monitoring the editorial content of news sources including newspapers, magazines, trade journals, TV shows, radio programs and specific websites is by far the most common form of media monitoring, but most organizations increasingly monitor social media online, and its impact on the diffusion of news in all media or in online conversation (including the comment space) more generally. Most companies, government agencies, not-for-profit organizations utilize media monitoring as a tool to study the "meaning of mentions" of their organization, its campaigns and slogans, and gain some sense of the composition of their audiences, and what animates them (or keeps them quiet). During the first day of the data sprint academics studying online media monitoring will present the state of the art of the field, focusing on three areas: issue discovery and issue language placement (who is the carrying the conversation, and which voices are continually elided?), engagement and fundraising communication (how are audiences and funders reacting to so-called 'faces of need' and other formats and calls for engagement?) and crisis communication (when there is a breakdown, who makes the calls?). Representatives from leading NGOs will present to the attendees how they practice online media monitoring, the look of their dashboards and the analytical needs that drive them. What are these experts able to accomplish with the techniques available to them, and which questions remain unanswered? What are the critical media monitoring practices and questions that are specific to NGOs? How to conceptualize and operationalize issue discovery, engagement for fundraising and crisis monitoring? We will ask the NGO communications experts to address these questions. We also will ask them what they think digital methods and issue mapping may add to the outputs of media monitoring. The conversations with the experts will serve as starting points for winter school attendees - including analysts, designers and programmers - to develop into empirical projects that aim to answer research questions, and develop further techniques for media monitoring online.Digital Methods Mini-Conference at the Winter School The annual Digital Methods Mini-Conference at the Winter School, normally a one-day affair, provides the opportunity for digital methods and allied researchers to present short yet complete papers (5,000-7,500 words) and serve as respondents, providing feedback. Often the work presented follows from previous Digital Methods Summer Schools. The mini-conference accepts papers in the general digital methods and allied areas: the hyperlink and other natively digital objects, the website as archived object, web historiographies, search engine critique, Google as globalizing machine, cross-spherical analysis and other approaches to comparative media studies, device cultures, national web studies, Wikipedia as cultural reference, the technicity of (networked) content, post-demographics, platform studies, crawling and scraping, graphing and clouding, and similar. Key dates The deadline for application is 8 December 2014. To apply please send along a letter of motivation as well as your CV to winterschool [at] digitalmethods.net, with DMI Winter School in the subject header. Notifications will be sent on 9 December. If you are participating in the Mini-conference the deadline for submission of paper titles, abstracts and bios is also 8 December, with DMI Mini-conference & Winter School in the subject header. Please send your materials to winterschool [at] digitalmethods.net . To attend the Winter School, you need not participate in the Mini-conference. Deadline for submission of complete papers (5,000-7,500 words) is 6 January 2015. The program and schedule are available on 7 January. Fees & Logistics The fee for the Digital Methods Winter School 2015 is EUR 295. Bank transfer information will be sent along with the notification on 9 December 2014. The Winter School is self-catered. The venue is in the center of Amsterdam with abundant coffee houses and lunch places. Participants are expected to find their own housing (airbnb and other short-stay sites are helpful). During the week there is an evening at the Royal Academy with Jimmy Wales of Wikipedia. The Winter School closes on Friday with a festive event, after the final presentations. Here is a guide to the Amsterdam new media scene <https://www.digitalmethods.net/MoM/NewMediaAmsterdam>. For further questions, please contact the organizers, Liliana Bounegru, Natalia Sanchez and Saskia Kok, at winterschool@digitalmethods.net. About DMI The Digital Methods Winter School is part of the Digital Methods Initiative, Amsterdam, dedicated to reworking method for Internet-related research. The Digital Methods Initiative holds the annual Digital Methods Summer Schools (eight to date), which are intensive and full time 2-week undertakings in the Summertime. The 2015 Summer School will take place 29 June - 10 July 2015. The coordinators of the Digital Methods Initiative are Sabine Niederer and Esther Weltevrede (PhD candidates in New Media & Digital Culture, University of Amsterdam), and the director is Richard Rogers, Professor of New Media & Digital Culture, University of Amsterdam. Liliana Bounegru is the managing director. Digital methods are online at http://www.digitalmethods.net/. The DMI about page includes a substantive introduction, and also a list of Digital Methods people, with bios. DMI holds occasional Autumn and Spring workshops, such as recent ones on mapping climate change and vulnerability indexes <https://wiki.digitalmethods.net/Dmi/ClimateConflicts> as well as on studying right-wing extremism and populism <https://wiki.digitalmethods.net/Dmi/RightWingPopulismStudy> online. There is also a Digital Methods book <http://mitpress.mit.edu/books/digital-methods> (MIT Press, 2013), papers and articles <https://wiki.digitalmethods.net/Dmi/PapersPublications> by DMI researchers as well as Digital Methods tools <https://wiki.digitalmethods.net/Dmi/ToolDatabase>. See you in the winter time in Amsterdam! Image credit: Online resonance of the international climate change issue agenda <https://wiki.digitalmethods.net/Dmi/014Project3aOnlineResonanceOfTheInternationalClimateChangeIssueAgenda>, EMAPS data sprint, Amsterdam, April 2014.
FYI: http://www.sciencemag.org/content/346/6213/1063.summary From the press release: Using Social Media For Large Behavioral Studies Is Fast and Cheap, But Fraught With Biases and Distortion PITTSBURGH—The rise of social media has seemed like a bonanza for behavioral scientists, who have eagerly tapped the social nets to quickly and cheaply gather huge amounts of data about what people are thinking and doing. But computer scientists at Carnegie Mellon University and McGill University warn that those massive datasets may be misleading. In a perspective article published in the Nov. 28 issue of the journal Science, Carnegie Mellon’s Juergen Pfeffer and McGill’s Derek Ruths contend that scientists need to find ways of correcting for the biases inherent in the information gathered from Twitter and other social media, or to at least acknowledge the shortcomings of that data. And it’s not an insignificant problem; Pfeffer, an assistant research professor in CMU’s Institute for Software Research, and Ruths, an assistant professor of computer science at McGill, note that thousands of research papers each year are now based on data gleaned from social media, a source of data that barely existed even five years ago. “Not everything that can be labeled as ‘Big Data’ is automatically great,” Pfeffer said. He noted that many researchers think — or hope — that if they gather a large enough dataset they can overcome any biases or distortion that might lurk there. “But the old adage of behavioral research still applies: Know Your Data,” he maintained. Still, social media is a source of data that is hard to resist. “People want to say something about what’s happening in the world and social media is a quick way to tap into that,” Pfeffer said. Following the Boston Marathon bombing in 2013, for instance, Pfeffer collected 25 million related tweets in just two weeks. “You get the behavior of millions of people — for free.” The type of questions that researchers can now tackle can be compelling. Want to know how people perceive e-cigarettes? How people communicate their anxieties about diabetes? Whether the Arab Spring protests could have been predicted? Social media is a ready source for information about those questions and more. But despite researchers’ attempts to generalize their study results to a broad population, social media sites often have substantial population biases; generating the random samples that give surveys their power to accurately reflect attitudes and behavior is problematic. Instagram, for instance, has special appeal to adults between the ages of 18 and 29, African-Americans, Latinos, women and urban dwellers, while Pinterest is dominated by women between the ages of 25 and 34 with average household incomes of $100,000. Yet Ruths and Pfeffer said researchers seldom acknowledge, much less correct, these built-in sampling biases. Other questions about data sampling may never be resolved because social media sites use proprietary algorithms to create or filter their data streams and those algorithms are subject to change without warning. Most researchers are left in the dark, though others with special relationships to the sites may get a look at the site’s inner workings. The rise of these “embedded researchers,” Ruths and Pfeffer said, in turn is creating a divided social media research community. As anyone who has used social media can attest, not all “people” on these sites are even people. Some are professional writers or public relations representatives, who post on behalf of celebrities or corporations, others are simply phantom accounts. Some “followers” can be bought. The social media sites try to hunt down and eliminate such bogus accounts — half of all Twitter accounts created in 2013 have already been deleted — but a lone researcher may have difficulty detecting those accounts within a dataset. “Most people doing real social science are aware of these issues,” said Pfeffer who noted that some solutions may come from applying existing techniques already developed in such fields as epidemiology, statistics and machine learning. In other cases, scientists will need to develop new techniques for managing analytic bias.
It's been fairly easy to statistically critique many twitter and facebook data gathering "experiments". -----Original Message----- From: Air-L [mailto:air-l-bounces@listserv.aoir.org] On Behalf Of Katja Mayer Sent: Sunday, November 30, 2014 2:35 PM To: air-l@listserv.aoir.org Subject: [Air-L] Social Media data fraught with biases and distortion FYI: http://www.sciencemag.org/content/346/6213/1063.summary From the press release: Using Social Media For Large Behavioral Studies Is Fast and Cheap, But Fraught With Biases and Distortion PITTSBURGH—The rise of social media has seemed like a bonanza for behavioral scientists, who have eagerly tapped the social nets to quickly and cheaply gather huge amounts of data about what people are thinking and doing. But computer scientists at Carnegie Mellon University and McGill University warn that those massive datasets may be misleading. In a perspective article published in the Nov. 28 issue of the journal Science, Carnegie Mellon’s Juergen Pfeffer and McGill’s Derek Ruths contend that scientists need to find ways of correcting for the biases inherent in the information gathered from Twitter and other social media, or to at least acknowledge the shortcomings of that data. And it’s not an insignificant problem; Pfeffer, an assistant research professor in CMU’s Institute for Software Research, and Ruths, an assistant professor of computer science at McGill, note that thousands of research papers each year are now based on data gleaned from social media, a source of data that barely existed even five years ago. “Not everything that can be labeled as ‘Big Data’ is automatically great,” Pfeffer said. He noted that many researchers think — or hope — that if they gather a large enough dataset they can overcome any biases or distortion that might lurk there. “But the old adage of behavioral research still applies: Know Your Data,” he maintained. Still, social media is a source of data that is hard to resist. “People want to say something about what’s happening in the world and social media is a quick way to tap into that,” Pfeffer said. Following the Boston Marathon bombing in 2013, for instance, Pfeffer collected 25 million related tweets in just two weeks. “You get the behavior of millions of people — for free.” The type of questions that researchers can now tackle can be compelling. Want to know how people perceive e-cigarettes? How people communicate their anxieties about diabetes? Whether the Arab Spring protests could have been predicted? Social media is a ready source for information about those questions and more. But despite researchers’ attempts to generalize their study results to a broad population, social media sites often have substantial population biases; generating the random samples that give surveys their power to accurately reflect attitudes and behavior is problematic. Instagram, for instance, has special appeal to adults between the ages of 18 and 29, African-Americans, Latinos, women and urban dwellers, while Pinterest is dominated by women between the ages of 25 and 34 with average household incomes of $100,000. Yet Ruths and Pfeffer said researchers seldom acknowledge, much less correct, these built-in sampling biases. Other questions about data sampling may never be resolved because social media sites use proprietary algorithms to create or filter their data streams and those algorithms are subject to change without warning. Most researchers are left in the dark, though others with special relationships to the sites may get a look at the site’s inner workings. The rise of these “embedded researchers,” Ruths and Pfeffer said, in turn is creating a divided social media research community. As anyone who has used social media can attest, not all “people” on these sites are even people. Some are professional writers or public relations representatives, who post on behalf of celebrities or corporations, others are simply phantom accounts. Some “followers” can be bought. The social media sites try to hunt down and eliminate such bogus accounts — half of all Twitter accounts created in 2013 have already been deleted — but a lone researcher may have difficulty detecting those accounts within a dataset. “Most people doing real social science are aware of these issues,” said Pfeffer who noted that some solutions may come from applying existing techniques already developed in such fields as epidemiology, statistics and machine learning. In other cases, scientists will need to develop new techniques for managing analytic bias. _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org Join the Association of Internet Researchers: http://www.aoir.org/
My reaction to that title is scorn and the impulse to point out that all data has biases, and statistics is how you turn raw data into insights about *the data*. After a little more consideration, I think the press release is much more reasonable than the title makes it out to be. Yes, it's very common that people want to make it implicit that social media is a good representation of reality. It ain't necessarily so, folks! :) And a great big "Yes! That!" to inbuilt sampling bias and "black box" views of available data - these are very real problems that can come along with complexity and huge rivers of data. :) (To reply to Peter Timusk's other reply to this post -- yes, lots of people write bad papers. Not worth worrying about, really. It's a shame, but... what can you do? Best thing is always to do our own, superlative work, and just be model citizens of the ecosystem :) ) best, --e On Sun, Nov 30, 2014 at 1:35 PM, Katja Mayer <katja.mayer@univie.ac.at> wrote:
FYI: http://www.sciencemag.org/content/346/6213/1063.summary
From the press release: Using Social Media For Large Behavioral Studies Is Fast and Cheap, But Fraught With Biases and Distortion
PITTSBURGH—The rise of social media has seemed like a bonanza for behavioral scientists, who have eagerly tapped the social nets to quickly and cheaply gather huge amounts of data about what people are thinking and doing. But computer scientists at Carnegie Mellon University and McGill University warn that those massive datasets may be misleading. In a perspective article published in the Nov. 28 issue of the journal Science, Carnegie Mellon’s Juergen Pfeffer and McGill’s Derek Ruths contend that scientists need to find ways of correcting for the biases inherent in the information gathered from Twitter and other social media, or to at least acknowledge the shortcomings of that data.
And it’s not an insignificant problem; Pfeffer, an assistant research professor in CMU’s Institute for Software Research, and Ruths, an assistant professor of computer science at McGill, note that thousands of research papers each year are now based on data gleaned from social media, a source of data that barely existed even five years ago. “Not everything that can be labeled as ‘Big Data’ is automatically great,” Pfeffer said. He noted that many researchers think — or hope — that if they gather a large enough dataset they can overcome any biases or distortion that might lurk there. “But the old adage of behavioral research still applies: Know Your Data,” he maintained. Still, social media is a source of data that is hard to resist. “People want to say something about what’s happening in the world and social media is a quick way to tap into that,” Pfeffer said. Following the Boston Marathon bombing in 2013, for instance, Pfeffer collected 25 million related tweets in just two weeks. “You get the behavior of millions of people — for free.”
The type of questions that researchers can now tackle can be compelling. Want to know how people perceive e-cigarettes? How people communicate their anxieties about diabetes? Whether the Arab Spring protests could have been predicted? Social media is a ready source for information about those questions and more. But despite researchers’ attempts to generalize their study results to a broad population, social media sites often have substantial population biases; generating the random samples that give surveys their power to accurately reflect attitudes and behavior is problematic. Instagram, for instance, has special appeal to adults between the ages of 18 and 29, African-Americans, Latinos, women and urban dwellers, while Pinterest is dominated by women between the ages of 25 and 34 with average household incomes of $100,000. Yet Ruths and Pfeffer said researchers seldom acknowledge, much less correct, these built-in sampling biases.
Other questions about data sampling may never be resolved because social media sites use proprietary algorithms to create or filter their data streams and those algorithms are subject to change without warning. Most researchers are left in the dark, though others with special relationships to the sites may get a look at the site’s inner workings. The rise of these “embedded researchers,” Ruths and Pfeffer said, in turn is creating a divided social media research community. As anyone who has used social media can attest, not all “people” on these sites are even people. Some are professional writers or public relations representatives, who post on behalf of celebrities or corporations, others are simply phantom accounts. Some “followers” can be bought. The social media sites try to hunt down and eliminate such bogus accounts — half of all Twitter accounts created in 2013 have already been deleted — but a lone researcher may have difficulty detecting those accounts within a dataset.
“Most people doing real social science are aware of these issues,” said Pfeffer who noted that some solutions may come from applying existing techniques already developed in such fields as epidemiology, statistics and machine learning. In other cases, scientists will need to develop new techniques for managing analytic bias. _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
participants (4)
-
Elijah Wright -
Katja Mayer -
Liliana Bounegru -
Peter Timusk