Some years back, I gave a talk at O’Reilly’s ETech conference that urged the audience to spend less time thinking up clever ways dissidents could blog secretly from inside repressive regimes and more time thinking about the importance of ordinary participatory media tools, like blogs, Facebook and YouTube, for activism. I argued that the tools we use for sharing cute pictures of cats are often more effective for activism than those custom-designed to be used by activists.
Others have been kind enough to share the talk, referring to “the Cute Cat theory”. An Xiao Mina, in particular, has extended the idea to explain the importance of viral, humorous political content on the Chinese internet.
I’ve meant to write up a proper academic article on the ideas I expressed at ETech for years now, and finally got the chance as part of a project organized by Danielle Allen and Jennifer Light at the Institute for Advanced Studies. They invited a terrific crew of scholars to collaborate on a book titled “Youth, New Media and Political Participation”, now in review for publication by MIT Press. The volume is excellent – several of my students at MIT have used Tommie Shelby’s “Impure Dissent: Hip Hop & the Political Ethics of Marginalized Black Urban Youth“, which will appear in the volume, as a key source in their work on online dissent and protest.
I’m posting a pre-press version of my chapter both so there’s an open access version available online and because a few friends have asked me to expand on comments I made on social media and the “Arab Spring” at the University of British Columbia and in Foreign Policy. (I also thought it would be a nice tie-in to the Gawkerization of Foreign Policy, with their posting today of 14 Hairless Cats that look like Vladimir Putin.)
Abstract: Participatory media technologies like weblogs and Facebook provide a new space for political discourse, which leads some governments to seek controls over online speech. Activists who use the Internet for dissenting speech may reach larger audiences by publishing on widely-used consumer platforms than on their own standalone webservers, because they may provoke government countermeasures that call attention to their cause. While commercial participatory media platforms are often resilient in the face of government censorship, the constraints of participatory media are shaping online political discourse, suggesting that limits to activist speech may come from corporate terms of service as much as from government censorship.
Look for the Allen and Light book on MIT Press next Spring – it’s an awesome volume and one I’m proud to be part of.
Last year, Sweden took on an experiment in social media as a form of nation branding by turning over its national Twitter account, @sweden, to a different citizen each week. Citizens are nominated and evaluated by a panel, but their tweets aren’t reviewed or edited, which led some observers to predict the experiment would be a social media disaster.
Those predictions came true, more or less, with the week Sonja Abrahamsson took over the account. She spent the week offending as many people as possible, with offhand observations about Jews, people with AIDS, and the suggestion that her life would be easier if she had Down’s syndrome. In other words, she used @sweden to troll anyone who was paying attention. (Trolls, of course, hail from Scandinavian folklore and may be native to Sweden, so perhaps this behavior is simply part of the national character.)
Nasser has continued on this theme, reacting to some comments from readers and provoking responses from others, like the exchange below.
At the first Global Voices summit, eight years ago, Hossein Derakhshan offered a model for understanding the role social media could play in helping people understand life in another part of the world. Blogs could act as windows, bridges and as cafés, offering us a glimpse into life in another corner of the world, a connection to some place different than where we already are, and, maybe, a space to gather and have a conversation.
Sweden’s experiment proposes to use Twitter as a window. Inviting “ordinary” Swedes to tweet about everyday life promises a picture of life in Sweden that’s likely to be different from impressions we get of the nation through news, through entertainment media or through our interaction with Swedes in our social networks. Ideally, it gives the sort of multifaceted picture we might have of the nation if we had lots of Swedish friends in our social network, including “inbetweeners” like Naseer and trolls like Sonja.
But the Swedish experiment is an attempt at building bridges as well. For one thing, the experiment asks participants to tweet in English rather than Swedish so the conversation is accessible to a wider audience. Nasser’s decision to start his stint representing @sweden by telling his story is a form of bridging as well – by understanding his personal story, we’ve got a better chance of paying attention to the trivia of his everyday existence. And it’s possible that the comments on some of his posts will open a café of sorts, a conversation about what it means to be Swedish, bicultural, racist or nationalist.
I’m interested to see that my neighbors to the north, in Vermont, are trying a similar model, hoping that showing tweets from Vermont will help portray the state as younger and more tech savvy than we might otherwise assume. I’ll be interested to see whether more Swedes or Vermonters use Twitter to tell their personal story and build a relationship while they’re opening a window into their lives.
Scholars of social media spend a lot of time studying Twitter. Twitter’s not the largest social network in the world – Facebook has at least twice as many users – but it’s massive and influential, particularly in the world of journalism, where smart practitioners have learned to report on stories using accounts from Twitter. And Twitter is something of a model organism for social media researchers. Most relationships and content on Twitter are public, while relationships and content on Facebook are often private. There’s an ecosystem of tools that use Twitter’s API to understand popular topics and networks of influence on Twitter, and countless research projects that use Twitter’s API to understand behavioral dynamics on social networks.
By contrast, there’s little scholarly research in English on Sina Weibo, China’s most popular microblogging network. (The top article on Google Scholar that comes up for a search on “twitter” has 637 cites. Top article for “sina weibo” has 9 cites.) The service is structurally similar to Twitter, with @usernames, hashtags, reposting, and URL shortening (using the t.cn site instead of t.co used by Twitter.) In one sense, the service is richer than Twitter, as posts can contain both 140 characters (which may contain significantly more information than 140 alphanumeric characters, as the 140 characters in Chinese are ideograms), and an embedded image or video. And Sina Weibo offers an API and supports an ecosystem of tools and applications that interact with Weibo data. Oh, and Sina Weibo has almost as many users as Twitter – 250 million in October 2011, as compared to roughly 300 million for Twitter at the end of 2011.
The obvious reason for the lack of English language research is that most English-speaking social media scholars don’t read Chinese very well. But this a lame excuse for ignoring a powerful media tool. John Kelly of Morningside Analytics doesn’t speak Persian, but he’s done groundbreaking research mapping links in the Iranian blogosphere. Colleagues at the Berkman Center are using Media Cloud (built by researchers who speak no Russian) to understand conversations taking place in Russian blogs versus those in state-influenced media. Language is a powerful, but not insurmountable, barrier to researching a media space. In both the cases I mention above, English-speaking researchers worked with translators to understand novel social media phenomena.
I sometimes wonder whether English-speaking scholars pay insufficient attention to Chinese social media due to an assumption that Chinese media has been censored to the point of sterility. I often speak about internet censorship, and American audiences in particular are quick to share their knowledge of the “great firewall”, the “fifty cent party” and other aspects of Chinese internet censorship. Because Chinese censorship has been widely reported in American media, I suspect many Americans know more about what’s not on the Chinese internet than what’s present. (David Talbot of Technology Review wrote an excellent article about “China’s Internet Paradox” which makes the case that the Chinese internet is freer and more complicated than most audiences think.)
One of the best ways to get a sense for the complexity of Sina Weibo is through WeiboScope, a tool created by Cedric Sam and colleagues at the University of Hong Kong. WeiboScope uses Sina Weibo’s API to collect posts from 200,000 Sina Weibo users. His sample is a subset of Sina Weibo’s most popular users, and contains only users who have at least 1000 followers. (His blog, the Rice Cooker, offers lots of details on building and deploying the system.) Taking advantage of the fact that many Sina Weibo posts include images, WeiboScope offers a visual version of Weibo “trending topics”, showing the images associated with the most retweeted posts.
A first glance at WeiboScope offers a sense for what’s hot in the Chinese internet. There’s lots of images of pop stars, and lots of pretty women showing off cleavage. Dig a bit further and there’s some hope for the xenophiles amongst us: internet memes that need to translation. Sam the Seagull – a bird who steals Doritos from an Aberdeen convenience store – has been kicking around the internet since at least 2007, and an animated GIF of the thieving bird is the second most popular post today. Other memes appear to be shared in realtime – this comparison of pollution in a Chinese city versus the skies above Australia featured on WeiboScope today, and also appeared on Reddit this morning.
Dig a bit deeper and there’s quite a bit of political content. Take this deeply disconcerting image:
The face of the mammarilly-enhanced cow is that of Niu Gensheng, CEO of Mengniu Dairy, one of the companies implicated in the 2008 Melamine scandal, where companies apparently added a toxic chemical to milk powder to increase protein content in their products. Mengniu recently revealed that some of their milk is testing positive for another toxin, apparently because cows were fed moldy feed. The company’s share price dropped 24% on this news today, knocking more than $1 billion of the company’s value. The text accompanying the Gensheng cartoon warns the executive of the dangers of angering 1.3 billion people. Another post, the most popular today, links to an article on Songshuhui.net that argues that Chinese people should stop drinking milk. While the article doesn’t explicitly mention Mengniu, it references scandals about milk, and it’s likely that the conversation about eschewing milk is directly related to the Mengniu news. Another popular post suggests a boycott of Mengniu, reminding readers that Saatchi & Saatchi, which had worked to rebrand the company, left after the tainted milk scandal of 2008.
I suspect some readers will note that the story I’m featuring about popular dissent is about consumer issues, not about direct opposition to the government. It’s worth remembering that popular protest often focuses more on economic and social issues than on overtly political issues – the Occupy movement in the US has been triggered by frustration with banks at least as much as it is with frustration with US politics. And there’s more directly political content on Weibo as well – this post talks about a family’s house that’s demolished by the government and a man’s protests in Beijing. This isn’t to say that Sina Weibo isn’t censored – it is. But the speed of Weibo means that stories can be widely discussed before censors declare a topic off limits, as we saw with extensive online coverage of the July high speed train collision. And the popularity of Weibo gives Chinese authorities a classic Cute Cats problem – censoring the service too heavily would alienate the 250 million people who use it, including the majority who are largely interested in scantily dressed celebrities.
I should note: I don’t speak or read Chinese. That means that my interpretation of the Mengniu cow could be deeply mistaken. But it also means that it’s possible to puzzle out a breaking story in Chinese media using WeiboScope, Google Translate and a few web searches.
Here’s hoping tools like WeiboScope will help make the Chinese internet seem like less of a foreign land and more like a near neighbor.
Oiwan Lam at Global Voices has posted about online activism around Mengniu, with some wonderful (and generally less disturbing!) images. And An Xiao offers a great reaction post to the ideas I’m putting forward here, including a clever inversion of the Cute Cat Theory: “with Chinese political memes, the cute cats are the activist message.” Very interesting, something I’m still digesting.
John Kelly, chief scientist of Morningside Analytics, makes pretty diagrams that feature multicolored dots. The pretty dots frequently tell complicated and subtle stories about the spread of ideas in online media spheres, particularly the blogosphere. (Tragically, I don’t have Kelly’s slides for the talk, which means I’ll be trying to channel a very visual talk here…) He maps social media citations and studies the resulting topologies to understand the spread of ideas.
To understand what conversations are taking place about fact checking, he takes a “semantic slice” of the network. He looks for markers – keywords, URLs and metadata – and offers a “relevance metric” for bloggers to identify the bloggers he believes are most relevant in the space. Then he plots them with a size that shows how well-linked a blog is, and uses a physics model to cluster based on linkage.
Kelly then uses “attentive clustering” to color the graph – people who link to the same sources are colored the same way. There’s a clear cluster around conservative politics, and a visible cluster that’s conservative, pro-Israel. There’s a fringe group he calls “Islam critics”. On the other side, he sees clusters of progressive insiders, progressive outsiders, and progressive media critics. Other clusters are apolitical – economics, law, education, health and healthcare. Web cultures – Gizmodo, Make magazine – are also represented in the map. And there’s a cluster of journalism criticism, which Kelly notes is uncomfortably close to people who watch celebrities.
He characterizes the progressive critics as reasonably well connected to other conversations, and the conservative conversation as largely separate. Unsupriringly, a site like Newsbusters.org gets lots of attention from the conservative cluster… but does get some links from the big dogs on the progressive side. Factcheck.org is the mirror image – the big conservatives, and most people in the progressive space. Politifact is similar. Media Matters is further out towards the progressive fringe, though gets attention from big conservatives. Politicalcorrection.org is even further left.
MRC.org is mainly linked from the right, but gets good response from the journalism commentary cluster. Washington Post’s Factchecker blog gets equal attention from the left and the right, but lots of love from the journalists. CJR is loved by the left and the journalists, and invisible to the right. Sunlight Foundation has lots of traction in the tech community and is stronger thre than in political circles. For a comparison, Kelly offers snopes.org, which seems to be equally noted across the board.
Healthnewsreview.org, a site that focuses on corrections in the health and healthcare space, has excellent traction in one space, but almost no influence in other parts of the mediasphere. This offers some interesting implications for niche communication strategies, but offers some worries about information crossing from subject domains into the main conversation.
Kelly graphs 1000 top sites in terms of who links to them. The graph has two dimensions: left/right and political versus mainstream. The political fact sites range from the left to the right, but are strongly linked to by political sites. Some odd exceptions – CJR is left and fairly mainstream, while NPR is quite central and fairly mainstream.
Morningside has also looked, though in less depth, at a set of Twitter accounts that follow fact checking organizations. They picked a set of key fact checking twitter feeds and grabbed all of their followers. They looked for linkage and clustering and used k-core analysis to choose a densely connected set. What results is a space where conservatives appear to follow political fact checking more closely than progressives. (I’m not entirely clear on how Kelly is determining left-right within this set – I assume he’s hand-checking the clusters that emerge in his analysis, which is his standard operating method.)
Even a highly partisan site like politicancorrection.org has substantial followership from the right. Kelly drills down and sees clusters of followers in the Occupy movement, in the union and labor space, and in the eco/green space, as well as beltway insiders and people who study media. But he also sees a cluster of followers of conservative politicians, and a cluster around conservative media personalities.
How might we explain this? It could be that Twitter is where conservatives are making their stand in social media. Conservatives may be watching Twitter very closely and responding to each of these fact check interventions. It’s hard to know, though, as Kelly notes that Twitter is a space of “non-authentic actors”, both automated bots and coordinated groups of humans.
This summer, Sasha, Lorrie and I started brainstorming the sorts of events we wanted to host at the Center for Civic Media this fall. The first I put on the calendar was a session on “mapping civic media”, a chance to catch up with some of my favorite people who are working to study, understand and visualize how ideas move through the complicated ecosystem of professional and participatory media.
To represent the research being done in the space, we invited Hal Roberts, my collaborator on Media Cloud (and on a wide range of other research), Erhardt Graeff from the Web Ecology project, and Gilad Lotan, VP of R&D for internet analytics firm BetaWorks. On Wednesday night, I asked them to share some of the recent work they’ve been doing, understanding the structure of the US and Russian blogosphere, analyzing the influence networks in Twitter during the early Arab Spring events and understanding the social and political dynamics of hashtags. They didn’t disappoint, and I suspect our video of the session (which we’ll post soon) will be one of the more popular pieces of media we put together this fall. In the meantime, here are my notes, constrained by the fact that I was moderating the panel and so couldn’t lean back and enjoy the presentations the way I otherwise might have.
Hal Roberts is a fellow at the Berkman Center for Internet and Society, where he’s produced great swaths of research on internet filtering, surveillance, threats to freedom of speech, and the basic architecture of the internet. (That he’s written some of these papers with me reflects more on his generosity than on my wisdom.) He’s the lead architect of Media Cloud, the system we’re building at the Berkman Center and at Center for Civic Media to “ask and answer quantitative questions about the mediasphere in more systematic ways.” As Hal explains, media researchers “have been writing one-off scripts and systems to mine data in haphazard ways.” Media Cloud is an attempt to streamline that process, creating a collection of 30,000 blogs and mainstream media sources in English and Russian. “Our goal is to get as much media as possible, so we can ask our own questions and also let others ask questions of our duct tape and bubblegum system.”
Hal’s map of clusters in popular US blogs. An interactive version of this map is available here.
Much of Hal’s work has focused on using the content of media – rather than the structure of its hyperlinks – to map and cluster the mediasphere. He shows us a map of US blogs that cluster into three main areas – news and political blogs, technology blogs and what he calls “the love cluster”. This last cluster is so named because it’s filled with people talking about what they love. Subclusters include knitters, quilters, fans of recipes and photography. The technology cluser breaks down into a Google camp, an iPhone camp and a camp discussing Android Apps. Hal’s visualization shows the words most used in the sources within a cluster, which helps us understand what these clusters are talking about. The Google cluster features words like “SEO, webmaster, facebook, chrome” and others, suggesting the cluster is substantively about Google and its technology projects.
While we might expect the politics and news cluster to divide evenly into left and rightwing camps, it doesn’t. Study the link structure of the left and the right, as Glance and Adamic and later Eszter Hargittai have, and it’s clear that like links to like. But Hal’s research shows that the left and right use very similar language and talk about many of the same topics. This is a novel finding: It’s not that the left and right are talking about entirely different topics – instead they’re arguing over a common agenda, an agenda that’s well represented in mainstream media as well, which suggests the existence of subjects neither the right or left are talking about online.
Building on this finding, Hal and colleagues at Berkman looked at the Russian media sphere, to see if there was a similar overlap in coverage focus between mainstream media and blogs. “Newspapers and the television are subject to strong state control in Russia – we wanted to see if our analysis confirmed that, and whether the blogosphere was providing an alternative public sphere.
The technique he and Bruce Etling used is “the polar map” – put the source you believe is most important at the center, and other sources are mapped at a distance from that source where the distance reflects degree of similarity. The central dot is a summary of verbiage from Russian government ministry websites. Right next to it is the official government newspaper. TV stations cluster close to the center, while blogs cover a wide array of the space, including the edges of the map.
It’s possible that blogs are showing dissimilarities to the Kremlin agenda because they’re talking about knitting, not about politics. So a further analysis (the one mapped above) explicitly identified democratic opposition and ethno-nationalist blogs and looked at their placement on the map. There’s strong evidence of political conversations far from the government talking points in both the democratic opposition and in the far right nationalist blogosphere.
What’s particularly interesting about this finding is that we don’t see the same pattern in the US blogosphere. Make a polar map with the White House, or a similar proxy for a US government news agenda, at the center, and you’ll see a very different pattern. Some right wing American blogs flock quite closely to the White House talking points – mostly to critique them – while the left blogs and mainstream media generally don’t. However, when Hal and crew did an analysis of stories about Egypt, they saw a very different pattern than in looking at all stories published in these sources. They saw a tight cluster of US mainstream media and blogs – left and right – around the White House. The government, the media and bloggers left and right talked about Egypt using very similar language. In the Russian mediasphere, the pattern was utterly different – the democratic opposition was far from the Kremlin agenda, using the Egyptian protests to talk about potential revolution in Russia.
The ultimate goal of Media Cloud, Hal explains, is to both produce analysis like this, and to make it possible for other researchers to conduct this sort of analysis, without a first step of collecting months or years of data.
Erhardt Graeff is a good example of the sort of researcher Media Cloud would like to serve. He’s cofounder of the Web Ecology Project, which he describes as “as a ragtag group of casual researchers that has now turned in a peer-reviewed publication“. That publication is the result of mapping part of the Twitter ecosystem during the Tunisian and Egyptian revolutions, and attempting to tackle some of the hard problems of mapping media ecosystems in the process.
The Web Ecology Project began life researching the Iranian elections and resulting protests, focusing on the #iranelection hashtag. With a simple manifesto around “reimagining internet studies”, the project tries to understand the “nature and behavior of actors” in media systems. That means considering not just the top users, or even just the registered users of a system like Twitter, but the audience for the media they create. “Each individual user on Twitter has their personal media ecosystem” of people they follow, influence, are followed by and influenced by.
This sort of research rapidly bumps into three hard problems, Erhardt explains:
- Did someone read a piece of information that was published? Or as he puts it, “Did the State Department actually read our report about #IranElection?” It’s very hard to tell. “We end up using proxies – you followed a link, but that doesn’t mean you read it.”
- Which piece of media influenced someone to access other media? “Which tweet convinced me to follow the new Maru video, Erhardt’s or MC Hammer’s?”
- How does the media ecosystem change day to day? Or, referencing a Web Ecology paper, “How many genitalia were on ChatRoulette today?” The answer can vary sharply day to day, raising tough problems around generating a usable sample.
The paper Erhardt published with Gilad and other Web Ecology Project members looks at the Twitter ecosystem around the protest movements in Tunisia and Egypt. By quantitatively searhing for information flows, and qualitatively classifying different types of actors in that ecosystem, the research tries to untangle the puzzle of how (some) individuals used (one type of) social media in the context of a major protest.
To study the space, the team downloaded hundreds of thousands of tweets, representing roughly 40,000 users talking about Tunisia and 62,000 talking about Egypt. They used a “shingling” method of comparison to determine who was retweeting whom ad sought out the longest retweet chains. They looked at the top 10% of these chains in terms of length to find the “really massive, complex flows” and grabbed a random 1/6th of that sample. That yielded 774 users talking about Tunisia, 888 talking about Egypt… and only 963 unique users, suggesting a large overlap between those two sets.
Then Erhardt, Gilad and others started manually coding the participants in the chains. Categories included Mainstream Media (@AJEnglish, @nytimes), web news organizations (@HuffingtonPost), non-media organizations (@Wikileaks, @Vodaphone), bloggers, activists, digerati, political actors, celebrities, researchers, bots… and a too-broad unclassified category of “others”. This wasn’t an easy process – Erhardt describes a system in which researchers compared their codings to ensure a level of intercoder reliability, then had broader discussions on harder and harder edge cases. They used a leaderboard to track how many cases they’d each coded, and goaded those slow to participate into action.
The actors they classified are a very influential set of Twitter users. The average organization in their set has 4004 followers, the average individual 2340 (which is WAY more than the average user of the system). To examine influence with more subtlety than simply counting followers, Erhardt and his colleagues use retweets per tweet as an influence metric. What they conclude, in part, is that “mainstream media is a hit machine, as are digerati – what they have to say tends to be highly amplified.”
The bulk of the paper traces information flows started by specific people. In the case of Egypt, lots of information flows start from journalists, bloggers and activists, with bots as a lesser, but important, influence. In Tunisia, there were fewer flows started by journalists, more by bots and bloggers, and way fewer from activists. This may reflect the fact that the Tunisian story caught many journalists and activists by surprise – they were late to the story, and less significant as information sources than the bloggers who cover that space over time. By the time Egypt becomes a story, journalists realized the significance and were on the ground, providing original content on Twitter, as well as to their papers.
One of the most interesting aspects of the paper is an analysis of who retweets whom. It’s not surprising to hear that like retweets like – journalists retweet journalists, while bloggers retweet bloggers. Bloggers were much more likely to retweet journalists on the topic of Egypt than on Tunisia, possibly because MSM coverage of Egypt was so much more thorough than the superficial coverage of Tunisia.
While Gilad Lotan worked with Erhardt on the Tunisia and Egypt paper, his comments at Civic Media focused on the larger space of data analysis. “I work primarily on data – heaps and mounds of data,” he explains, for two different masters. Roughly half his work is for clients, media outlets who want to understand how to interact and engage with their audiences. The other half focuses on developing the math and algorithms to understand the social media space.
This work is increasingly important because “attention is the bottleneck in a world where threshhold to publishing is near zero.” If you want to be a successful brand or a viable social movement, understanding how people manage their attention is key: “It’s impossible to simply demand attention – you have to understand the dynamics of attention in the face of this bottleneck.”
Gilad references Alex Dragulescu’s work on digital portraits, pictures of people composed of the words they most tweet or share on social media. He’s interested not just in the individuals, but in the networks of people, showing us a visualization of tweets around Occupy Wall Street. Different networks take form in the space of minutes or hours as new news breaks – the network around a threatened shutdown of Zuccotti Park for a cleanup is utterly different than the network in July, when Adbusters was the leading actor in the space.
Lotan’s visualizations of Twitter conversations about Occupy in July and October 2011
Images like this, Lotan suggests, “are like images of earth from the moon. We knew what earth looked like, but we never saw it
We knew we lived in networks, but this is the first time we can envision it and see how it plays out.”
When we analyze huge data sets, we can start approaching answers to very difficult questions, like:
- What’s the audience of the New York Times versus Fox News?
- What type of content gains wider audiences through social media?
- What topics do certain outlets cover? What are their strengths, weaknesses and biases?
- How do audiences differ between different publications? How are they similar?
- How fast does news spread, and how does it break?
Much of media and communications research addresses these questions, though rarely directly – as Erhardt noted, we generally address these questions via proxies. But Lotan tells us, we can now ask and answer questions like, “How many Twitter users follow Justin Bieber and The Economist?” The answer, to a high degree of precision, is 46,000. It’s just shy of the number who follow The Economist and the New York Times, 54,000.
Lotan is able to research answers like this because his lab has access to the Twitter “firehose” (the stream of all public data posted to Twitter, moment to moment) and to the bit.ly firehose. This second information source allows Lotan to study what people are clicking on, not just what media they’re exposed to. He offers a LOLcat, where the feline in question is dressed in a chicken costume. “We can see the kitty in you, and the chicken you’re hiding behind.” What people share and what they click is very different, and Lotan is able to analyze both.
This data allowed Lotan to compare what audiences for four major news outlets were interested in, my measuring their clickstreams. Al Jazeera and The Economist, he tells us, are pretty much what you’d think. But Fox News watchers are fascinated by crime, murders, kidnappings and other dark news. This sort of insight may help networks understand and optimise for their audiences. Al Jazeera’s audience, he tells us, is very engaged, tweeting and sharing stories, while Fox’s audience reads a lot and shares very little.
Some of Lotan’s recent research is about algorithmic curation, specifically Twitter’s trending topics. Many observers of the Occupy movement have posited that Twitter is censoring tweets featuring the #occupywallstreet hashtag. Lotan acknowledges that the tag has been active, but suggests reasons why it’s never trended globally. Interest in the tag has grown steadily, and has a regular heartbeat, connected to who’s active on the east coast of the US. The tag has spiked at times, but remains invisible in part due to bad timing – a spike on October 1st was tiny in comparison to “#WhatYouShouldKnowAboutMe”, trending at the same time.
At this point, Lotan believes he’s partially reverse engineered the Trending Topics algorithm. The algorithm is very sensitive to the new, not to the slowly building. This raises the question: what does it mean to “get the math right”. Lotan observes, “Twitter doesn’t want to be a media outlet, but they made an algorithmic choice that makes them an editor.” He’s quick to point out that algorithmic curation is often very helpful – the Twitter algorithm is quite good at preventing spam attacks, which have a different signature than organic trends. So we see organic, fast-moving trends, even when they’re quite offensive. He points to #blamethemuslims, which started when a Muslim women in the UK snarkily observed that Muslims would be blamed for the Norway terror attacks. That tweet died out quickly, but was revived by Americans who used the tag unironically, suggesting that we blame Muslims for lots of different things – that small bump, then massive spike is a fairly common organic pattern… and very different from the spam patterns he’s seen on Twitter.
When we analyze networks, Lotan suggests, we encounter a paradox that James Gleick addresses in his recent book on information: just because I’m one hop away from you in a social network doesn’t mean I can send you information and expect you to pay attention. In the real world, people who can bridge between conversations are rare, important and powerful. He closes his talk with the map of a Twitter conversation about an event in Israel where settlers were killed. There’s a large conversation in the Israeli twittersphere, a small conversation in the Palestinian community, and two or three bridge figures attempting to connect the conversations. (One is my wife, @velveteenrabbi.) Studying events like this one may help us, ultimately, determine who’s able to build bridges between these conversations.
I can’t wait for the video for this event to be put online – we’ll get it up as soon as possible and I’ll link to it once we do.