Evidence of complex contagion of information in social media: An experiment using Twitter bots

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using ‘social bots’ deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

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Decentralized Social Networks Sound Great: Too Bad They’ll Never Work

by Chelsea Barabas, Neha Narula, and Ethan Zuckerman

Over the last 13 years, Facebook has evolved from a lifestyle site for college kids into a cornerstone of civic life. It is one of a handful of very large platforms that dominate our online world. As such platforms have gained traction, the web has transformed from an open space for free expression into a corporate-owned gated community of private platforms.

The power of giant platforms like Facebook, Google, and Twitter leads to problems ranging from the threat of government-ordered censorship to more subtle, algorithmic biases in the curation of content users consume. Moreover, as these platforms expand their reach, the ripple effects of exclusion can have serious consequences for people’s personal and professional lives, and users have no clear path to recourse. The platforms that host and inform our networked public sphere are unelected, unaccountable, and often impossible to audit or oversee.

In response, there is a growing movement among free speech advocates to create new technology to address these concerns. Early web pioneers like Brewster Kahle have called for ways we might “lock the web open” with code, enabling peer-to-peer interactions in place of mediated private platforms. The idea is to return to the good old days of the early ’90’s web, when users published content directly in a user-friendly decentralized fashion, without the need for corporate intermediaries and their aspirational approach.

Read the full article here.

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Why Everyone Should See Themselves as a Leader

Sue Ashford, a professor at the University of Michigan’s Ross School of Business, breaks down her decades of research on leadership—who achieves it, and how a group grants it. She explains that the world isn’t divided into leaders and followers. Instead, it’s a state that everyone can reach, whether they’re officially in charge or not. She also explains why shared leadership benefits a team and organization. Ashford offers tips on how to effectively grow leadership in yourself and your employees.

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How does network structure influence the wisdom of crowds?

Researchers at Annenberg School for Communication, University of Pennsylvania recently published a paper about “Network dynamics of social influence in the wisdom of crowds” in PNAS. They conducted an online network experiment where participants were asked to estimate numeric quantity (e.g., the caloric content) and tested how the accuracy of group estimates changes in different communication networks. They found that in decentralized networks, the group estimates were improved and in centralized networks, the accuracy of group estimates was undermined.

Read the full article here.

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Social networks may one day diagnose disease–but at a cost

by Sam Volchenboum

The world is becoming one big clinical trial. Humanity is generating streams of data from different sources every second. And this information, continuously flowing from social media, mobile GPS and wifi locations, search history, drugstore rewards cards, wearable devices, and much more, can provide insights into a person’s health and well-being.

It’s now entirely conceivable that Facebook or Google—two of the biggest data platforms and predictive engines of our behavior—could tell someone they might have cancer before they even suspect it. Someone complaining about night sweats and weight loss on social media might not know these can be signs of lymphoma, or that their morning joint stiffness and propensity to sunburn could herald lupus. But it’s entirely feasible that bots trolling social network posts could pick up on these clues.

Sharing these insights and predictions could save lives and improve health, but there are good reasons why data platforms aren’t doing this today. The question is, then, do the risks outweigh the benefits?

Read the full article here.

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Emotion shapes the diffusion of moralized content in social networks

by William J. Brady, Julian A. Willis, John T. Tost, Joshua A. Tucker, and Jay J. Van Bavel

Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call “moral contagion.”

Using a large sample of social media communications about three polarizing moral/political issues, we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them.

Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks.

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Cuba’s illegal gaming network serves the community

Cuba’s desire for digital connectivity has led to the organization and development of the “Street Network”. It acts as a social community as well as an alternative to government-controlled and regulated Internet service. Gaming online was a key motivator in the network’s development, but it now contains social media, wikis, marketplaces, and more. Being connected is important in 2017, but increasing access globally remains a challenge.

The last rule of the Street Network is that you don’t talk about the Street Network. But that wasn’t always the case.

For several years the clandestine Havana network of illegal Wi-Fi repeaters, lengths of high-speed network cable and squirreled away servers packed with pirated games, movies and music was sort of an open secret.

The government didn’t just turn a blind eye to it; in some cases it protected the valuable equipment located on windowsills and rooftops, keeping an eye out for potential thieves.

All of that changed in some people’s eyes in 2015 after several people in the Street Network (often just called the Snet) talked to the Associated Press and brought too much attention to their efforts. Since then, the Snet has continued to grow, quickly stretching outside the bounds of Havana and becoming something more than the gaming and entertainment network it started out as. But now that growth happens despite the government’s continued efforts to take the network down, several people who help maintain the network tell Polygon…

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The ‘time machine’ reconstructing ancient Venice’s social networks

Machine-learning project will analyse 1,000 years of maps and manuscripts from the floating city’s golden age.

History hangs heavy at the Frari, and computer scientist Frédéric Kaplan likes it that way. He has an ambition to capture well over 1,000 years of records in dynamic digital form, encompassing the glorious era of the Most Serene Republic of Venice. The project, which he calls the Venice Time Machine, will scan documents including maps, monographs, manuscripts and sheet music. It promises not only to open up reams of hidden history to scholars, but also to enable the researchers to search and cross-reference the information, thanks to advances in machine-learning technologies.

Read the full article here.

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How to judge a book by its network

Taking advantage of the ‘customers who bought this item also bought’ feature of online commerce, this study constructed a co-purchase network of political books and science books. Researches found a clear division, which they label “partisan differences in the consumption of science”.

Both groups bought science books — more than 400,000 between them. But it was relatively unusual to find books that appealed to both liberals and conservatives. Members of each group — and their good friends — had different ideas about what made a good book. Buyers of “blue books” (the liberals) tended to pick from basic science topics, including physics, astronomy and zoology. “Red” customers preferred books that discussed applied and commercial science, such as medicine, criminology and geophysics. And whereas liberal choices tended to reflect mainstream thinking, “red books” tended to be co-purchased with a narrower subset of science books on the fringes of each subject.

Read the full article here.

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Bad bots do good: Random artificial intelligence helps people coordinate

“To figure out whether random AI can help people coordinate, Hirokazu Shirado, a sociologist and systems engineer, and Nicholas Christakis, a sociologist and physician, both at Yale University, asked volunteers to play a simple online game. Each person controlled one node among 20 in a network. The nodes were colored green, orange, or purple, and people could change their node color at any time. The goal was for no two adjacent nodes to share the same color, but players could see only their color and the colors of the nodes to which they were connected, so sometimes settling conflicts with neighbors raised unseen conflicts between those neighbors and their neighbors. If the network achieved the goal before the 5-minute time limit was up, all players in the network received extra payment. The researchers recruited 4000 players and placed them in 230 randomly generated networks.
Some of the networks had 20 people controlling the nodes, but others had three of the most central or well-connected nodes already colored in such a way that they fit one of the solutions. (Each network had multiple solutions.) And some of the networks had 17 people and three bots, or simple AI programs, in charge of the nodes. In some networks, the bot-controlled nodes were placed centrally, in some they were placed peripherally, and in some they were placed randomly. The bots also varied in how much noise, or randomness, influenced their choice of node color. In some networks, every 1.5 seconds the bots picked whatever color differed from the greatest number of neighbors—generally a good strategy among people playing the game. In some networks, they followed this strategy, but 10% of the time they would pick randomly. And in some networks, they would pick randomly 30% of the time.
All of the networks with bots performed the same as the networks with 20 people, except for one type. The networks in which the bots were placed centrally and randomized their decisions 10% of the time outperformed the all-human networks. They solved the coordination game within the time limit more frequently (85% versus 67% of the time). And the median time spent on the task was 103 seconds versus 232 seconds, a significant difference, the researchers report today in Nature. The fact that bots with 0% noise or 30% noise did not outperform humans means that there’s a Goldilocks zone of randomness.
What’s more, the bot-aided networks performed just as well as the networks that already had a head start—those with three nodes preset to fit a solution. But whereas the set-color networks required top-down control, the noisy bots achieved equal results with just a bit of local randomness. “We get the same bang,” Christakis says. “To me that was a beautiful result.””
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