My colleague Kate Starbird recently shared a very neat study entitled “Learning from the Crowd: Collaborative Filtering Techniques for Identifying On-the-Ground Twitterers during Mass Disruptions” (PDF). As she and her co-authors rightly argue, “most Twitter activity during mass disruption events is generated by the remote crowd.” So can we use advanced computing to rapidly identify Twitter users who are reporting from ground zero? The answer is yes.
I loathe Wikipedia. I fought for months through their arcane rules to have a mass of misinformation about myself and my work deleted. And many of my friends and colleagues tell similar stories. Nothing that has anything to do consciousness research can trusted to be accurate on Wikipedia. In fact there is a group called Guerrilla Skeptics that makes a point of distorting anything having to do with consciousness or parapsychological research. I can on! ly assume that there are many other areas where there may be controversy that are similarly rigged. NEVER, EVER use Wikipedia if accuracy matters.
The sixth most widely used website in the world is not run anything like the others in the top 10. It is not operated by a sophisticated corporation but by a leaderless collection of volunteers who generally work under pseudonyms and habitually bicker with each other. It rarely tries new things in the hope of luring visitors; in fact, it has changed little in a decade. And yet every month 10 billion pages are viewed on the English version of Wikipedia alone. When a major news event takes place, such as the Boston Marathon bombings, complex, widely sourced entries spring up within hours and evolve by the minute. Because there is no other free information source like it, many online services rely on Wikipedia. Look something up on Google or ask Siri a question on your iPhone, and you’ll often get back tidbits of information pulled from the encyclopedia and delivered as straight-up fact! s.
Yet Wikipedia and its stated ambition to ‘compile the sum of all human knowledge” are in trouble. The volunteer workforce that built the project’s flagship, the English-language Wikipedia-and must defend it against vandalism, hoaxes, and manipulation-has shrunk by more than a third since 2007 and is still shrinking. Those participants left seem incapable of fixing the flaws that keep Wikipedia from becoming a high-quality encyclopedia by any standard, including the project’s own. Among the significant problems that aren’t getting resolved is the site’s skewed coverage: its entries on Pokemon and female porn stars are comprehensive, but its pages on female novelists or places in sub-Saharan Africa are sketchy. Authoritative entries remain elusive. Of the 1,000 articles that the project’s own volunteers have tagged as forming the core of a good encyclopedia, most don’t earn even Wikipedia’s own middle-ranking quality scores.
The main source of those problems is not mysterious. The loose collective running the site today, estimated to be 90 percent male, operates a crushing bureaucracy with an often abrasive atmosphere that deters newcomers who might increase participation in Wikipedia and broaden its coverage.
Philosophy Professor, Karen Frost-Arnold, has just published a highly lucid analysis of the dangers that come with Internet accountability (PDF). While the anonymity provided by social media can facilitate the spread of lies, Karen rightly argues that preventing anonymity can undermine online communities by stifling communication and spreading ignorance, thus leading to a larger volume of untrustworthy information. Her insights are instructive for those interested in information forensics and digital humanitarian action.
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To make her case, Karen distinguishes between error-avoidance and truth-attainment. The former seeks to avoid false beliefs while the latter seeks to attain true belief. Take mainstream and social media, for example. Some argue that the “value of traditional media surpasses that of the blogosphere […] because the traditional media are superior at filtering out false claims” since professional journalists “reduce the number of errors that might otherwise be reported and believed.” Others counter this assertion: “People who confine themselves to a filtered medium may well avoid believing falsehoods (if the filters are working well), but inevitably they will also miss out on valuable knowledge,” including many true beliefs.
Analyzing Fake Content on Twitter During Boston Marathon Bombings
As iRevolution readers already know, the application of Information Forensics to social media is one of my primary areas of interest. So I’m always on the lookout for new and related studies, such as this one (PDF), which was just published by colleagues of mine in India. The study by Aditi Gupta et al. analyzes fake content shared on Twitter during the Boston Marathon Bombings earlier this year.
Gupta et al. collected close to 8 million unique tweets posted by 3.7 million unique users between April 15-19th, 2013. The table below provides more details. The authors found that rumors and fake content comprised 29% of the content that went viral on Twitter, while 51% of the content constituted generic opinions and comments. The remaining 20% relayed true information. Interestingly, approximately 75% of fake tweets were propagated via mobile phone devices compared to true tweets which comprised 64% of tweets posted via mobiles.
The authors also found that many users with high social reputation and verified accounts were responsible for spreading the bulk of the fake content posted to Twitter. Indeed, the study shows that fake content did not travel rapidly during the first hour after the bombing. Rumors and fake information only goes viral after Twitter users with large numbers of followers start propagating the fake content. To this end, “determining whether some information is true or fake, based on only factors based on high number of followers and verified accounts is not possible in the initial hours.”
I was invited to a potluck dinner on Wednesday to meet a bunch of strangers and discuss the importance of sharing. Well, kind of. Our hosts were looking to bolster the so-called sharing economy, which, depending on who you talk to, is either a lightweight form of socialism or an artisanal flavor of capitalism spawned by the Internet.
The event had clearly attracted people from both camps. It wasn’t long before one guest trumpeted a desire to “kick capitalism in the crotch,” while several others gently reminded people to sign a petition supporting Airbnb, the peer-to-peer apartment renting service. One man offered to sing a few songs. I spent much of the evening wondering why my red pepper hummus, which is always a hit at Super Bowl parties, wasn’t as popular as the vegan mac ‘n’ cheese. Plus whoever made the salad put in way too many red onions.
Standing in the cloudy center of all this was a group called Peers, which popped up over the summer with the goal of becoming a grassroots movement based on sharing. The potluck dinner—and about 130 other events taking place in 90 cities around the world on the same week—is its first big push to bring people to the cause.
There have been movements based on sharing before, as my aging hippie parents remind me with increasing regularity. But the brand-name version is relatively new. In the past few years, a series of startups have based their business models on creating online platforms where people can sell one another access to their homes (Airbnb), labor (Taskrabbit), or possessions (Lyft, Sidecar). These companies bathe in the spirit of cooperation—and it’s clearly to their benefit to frame themselves as facilitators of generosity—but they are also marketplaces looking for commissions.
This year’s World Disaster Report was just released this morning. I had the honor of authoring Chapter 3 on “Strengthening Humanitarian Information: The Role of Technology.” The chapter focuses on the rise of “Digital Humanitarians” and explains how “Next Generation Humanitarian Technology” is used to manage Big (Crisis) Data. The chapter complements the groundbreaking report “Humanitarianism in the Network Age” published by UN OCHA earlier this year.
The Sharing Economy is a topic that has been garnering a lot of attention lately — from startups embracing the collaborative model to consumers embracing the age old “sharing is caring” philosophy when it comes to vacation rentals, like AirBnB, or transportation solutions like NYC’s Citibike or Zipcar. We at sparks & honey have seen real value in the sharing economy trend as of late and recently partnered with global digital agency, Tribal Worldwide, to host the Collaborative Economy Summit, in which we set out to gather thought leaders in this space an answer one simple question — where is the Collaborative Economy heading and what value will it bring to business, brands and customers of the future?
