Research Highlights

The following is a selection of scientific results coming from the Truthy project. A complete list of scientific publications can be found at the publications, and additional press links can be found at the press page of this site.

Using Instagram to predict model success during Fashion Week

Predicting popularity and success in cultural markets is hard due to strong inequalities and inherent unpredictability. A good example comes from the world of fashion, where industry professionals face every season the difficult challenge of guessing who will be the next seasons’ top models. A recent study (DOI: 10.1145/2818048.2820065) by graduate student Jaehyuk Park, research scientist Giovanni Luca Ciampaglia (also at the IU Network Science Institute), and research scientist Emilio Ferrara (now at the University of Southern California) is now showing that early success in modeling can be predicted from the digital traces left by the buzz on social media such as Instagram. The study has been accepted for presentation at the 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW’16). The work has been covered in the media by the MIT Technology Review, Die Welt, Fusion, Vogue UK, Harper's Bazaar, and CBS News.

ACM Web Science 2014 Best Paper Award

Congratulations to Onur Varol, Emilio Ferrara, Chris Ogan, Fil Menczer, and Sandro Flammini for winning the ACM Web Science 2014 Best Paper Award with their paper Evolution of online user behavior during a social upheaval (preprint). In the paper, the authors study the pivotal role played by Twitter during the political mobilization of the Gezi Park movement in Turkey. By analyzing over 2.3 million tweets produced during 25 days of protest in 2013, the authors show that similarity in trends of discussion mirrors geographic cues. The analysis also reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. Finally, the study highlights how real-world events, such as political speeches and police actions, affect social media conversations and trigger changes in individual behavior.

Social Bot Detection: Best Poster Award and The Good Wife

A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior. Social bots have been circulating on social media platforms for a few years, and if you frequent online social media, you've probably come across them whether you know it or not! In order to learn more about social bots, we built BotOrNot, a tool to analyze a Twitter user's behavior and compare it to the behavior of known bots. So far, this effort has yielded The Rise of Social Bots (preprint) by Emilio Ferrara, Onur Varol, Clayton A Davis, Fil Menczer, and Sandro Flammini, to appear in Communications of the ACM; and a Best Poster Award at the 2015 Conference on Complex Systems.

Previously on August 11, 2013, the New York Times published an article by Ian Urbina with the headline: I Flirt and Tweet. Follow Me at #Socialbot. The article reports on how socialbots are being designed to sway elections, to influence the stock market, even to flirt with people and one another. Fil Menczer is quoted: “Bots are getting smarter and easier to create, and people are more susceptible to being fooled by them because we’re more inundated with information.” The article also mentions the Truthy project and some of our 2010 findings on political astroturf.

Inspired by this, the writers of The Good Wife consulted with us on an episode in which the main character finds that a social news site is using a socialbot to bring traffic to the site, defaming her client. The episode aired on November 24, 2013, on CBS (Season 5 Episode 9, “Whack-a-Mole”) . Good show!

More Tweets, More Votes

Truthy team members Karissa McKelvey and Johan Bollen collaborated with IU Department of Sociology members Joseph DiGrazia and Fabio Rojas on the paper More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior published in PLoS ONE. The paper aimed to see if the share of social media attention garnered by political candidates is significantly and reliably correlated with electoral performance. Their research suggests that indeed, holding other factors constant, candidates who received a larger share of attention on Twitter were more likely to win than their opponents.

Popular news outlets including Wall Street Journal, NPR and Washington Post picked up the story, many focusing in particular on potential ramifications the research will have on the current methods of political polling. More press links for this paper can be found at Karissa's website.

Geography of Twitter Trends

One might think that online social media, operating on a global scale via the Internet, wouldn't be affected much by geography. In fact, authors Emilio Ferrara, Onur Varol, Fil Menczer, and Sandro Flammini, show in their paper Traveling trends: social butterflies or frequent fliers? that online social media trends follow similar patterns as epidemics and disease patterns, exploiting the same pathways as human travelers to diffuse across the country.

The research identified three distinct geographical clusters in the US information flow (east coast, midwest, and southwest) as well as global patterns in the flow corresponding to main air traffic hubs. They conclude that travel hubs act as trendsetters, generating topics that eventually trend at the country level, then driving the conversation across the country. This work has received press attention from sources including Washington Post and Seattle Times.

Winner of WICI Data Challenge

Congratulations to Przemyslaw Grabowicz, Luca Aiello, and Fil Menczer for winning the WICI Data Challenge . A prize of $10,000 CAD accompanies this award from the Waterloo Institute for Complexity and Innovation at the University of Waterloo. The Challenge called for tools and methods that improve the exploration, analysis, and visualization of complex-systems data.

The winning entry, titled Fast visualization of relevant portions of large dynamic networks , is an algorithm that selects subsets of nodes and edges that best represent an evolving graph and visualizes it either by creating a movie, or by streaming it to an interactive network visualization tool. The algorithm will be available as an interactive demo on this website, and will allow users to create, in near-real time, YouTube videos that illustrate the spread and co-occurrence of memes on Twitter. Przemek and Luca worked on this project while visiting CNetS in 2011 and collaborating with the Truthy team. Bravo!

Meme Competition & Virality

In our paper on Competition among memes in a world with limited attention in Nature Scientific Reports, Lilian Weng and coauthors Sandro Flammini, Alex Vespignani, and Fil Menczer report that we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas. The findings have been mentioned in the popular press, including Information Week, The Atlantic, and the Dutch daily NRC.

A follow-up effort by Lilian Weng, Fil Menczer, and YY Ahn and published in Scientific Reports explores how virality of a meme can be predicted by analyzing the structural diversity of the early retweet network. This work was reported on by Scientific American among others. More information about this work can be found at Lilian's Website.