Diffusion of ISIS propaganda on Twitter

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My latest work titled "Contagion dynamics of extremist propaganda in social networks" has been published on Information Sciences. The study aims at modeling and understanding the diffusion of extremist propaganda, in particular content in support of ISIS, on social media like Twitter. Starting from a list of twenty-five thousand annotated accounts that have been associated with ISIS and suspended by Twitter, we obtained a large Twitter dataset of over one million posts these users generated. We studied network and temporal activity patterns, and investigated the dynamics of social influence within ISIS supporters.  To quantify the effectiveness of ISIS propaganda and determine the adoption of extremist content in the general population, we drew a parallel between radical propaganda and epidemics spreading. We identified information broadcasters and influential ISIS supporters and showed that…
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#MacronLeaks, bots, and the 2017 French election

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My latest work investigates the #MacronLeaks disinformation campaign that occurred in the run up to the 2017 French presidential election. Using a large dataset containing nearly 17 million tweets posted by users in the period between the end of April, and May 7, 2017 (Election Day), I first isolated the campaign that was carried out to allegedly reveal frauds and other illicit activities related to moderate candidate Emmanuel Macron, and in support of far-right candidate Marine Le Pen. New yet simple machine learning techniques devised specifically to analyze the millions of users appearing in this dataset revealed a large social bot operation and pointed to nearly 18 thousand bots deployed to push #MacronLeaks and related topics. The campaign attracted significant attention on the eve of Election Day, engaging overall nearly 100…
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Millions of social bots invaded Twitter!

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Our work titled Online Human-Bot Interactions: Detection, Estimation, and Characterization has been accepted for publication at the prestigious International AAAI Conference on Web and Social Media (ICWSM 2017) to be held in Montreal, Canada in May 2017! The goal of this study was twofold: first, we aimed at understanding how difficult is to detect social bots on Twitter respectively for machine learning models and for humans. Second, we wanted to perform a census of the Twitter population to estimate how many accounts are not controlled by humans, but rather by computer software (bots). To address the first question, we developed a family of machine learning models that leverages over one thousand features characterising the online behaviour of Twitter accounts. We then trained these models with manually-annotated collections of examples of…
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Complex System Society 2016 Junior Scientific Award!

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I was selected as recipient of the 2016 Junior Scientific Award by the Complex System Society! The award reads: Emilio Ferrara is one of the most active and successful young researchers in the field of computational social sciences. His works include the design and application of novel network-science models, algorithms, and tools to study phenomena occurring in large, dynamical techno-social systems. They improved our understanding of the structure of large online social networks and the dynamics of information diffusion. He has explored online social phenomena (protests, rumours, etc.), with applications to model and forecast individual behaviour, and characterise information diffusion and cyber-crime. 
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Twitter, Social Bots, and the US Presidential Elections!

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Our paper titled Social bots distort the 2016 U.S. Presidential election online discussion was published on the November 2016 issue of First Monday and selected as Editor's featured article! We investigated how social bots, automatic accounts that populate the Twitter-sphere, are distorting the online discussion about the 2016 U.S. Presidential elections. In a nutshell, we discovered that: About one-in-five tweets regarding the elections has been posted by a bot, totalling about 4 Million tweets posted during the month prior to the elections by over 400,000 bots. Regular (human) users cannot determine whether the source of some specific information is another legitimate user or a bot: therefore, bots are being retweeted at the same rate as humans. Bots are biased (by construction): Trump-supporting bots, for example, are producing systematically only positive contents in support of…
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The Rise of Social Bots!

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Emilio Ferrara discusses "The Rise of Social Bots" on the July 2016 Communications of the ACM. Our review paper on the rise of social bots has appeared on the cover of the July 2016 issue of Communications of the ACM and is the subject of my interview above! Social bots populate techno-social systems: they are often benign, or even useful, but some are created to harm, by tampering with, manipulating, and deceiving social media users. Social bots have been used to infiltrate political discourse, manipulate the stock market, steal personal information, and spread misinformation. The detection of social bots is therefore an important research endeavor. A taxonomy of the different social bot detection systems proposed in the literature accounts for network-based techniques, crowdsourcing strategies, feature-based supervised learning, and hybrid systems.…
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The structure of Mafia syndacates

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S Agreste, S Catanese, P De Meo, E Ferrara, G Fiumara. Network structure and resilience of Mafia syndicates. Information Sciences, 2016 Useful links: Journal page | Arxiv In this paper in collaboration with colleagues from University of Messina (Italy) we present the results of our study of Sicilian Mafia organizations using social network analysis. The study investigates the network structure of a Mafia syndicate, describing its evolution and highlighting its plasticity to membership-targeting interventions and its resilience to disruption caused by police operations. We analyze two different datasets dealing with Mafia gangs that were built by examining different digital trails and judicial documents that span a period of ten years. The first dataset includes the phone contacts among suspected individuals, and the second captures the relationships among individuals actively involved in various…
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Emotional contagion in Twitter!

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E Ferrara, Z Yang. Measuring Emotional Contagion in Social Media. PLoS ONE, 2015 Useful links: Journal page Spotlight: How Emotions Spread On Twitter from USC Viterbi on Vimeo. Our recent work on measuring the presence of emotional contagion in Twitter is finally published on Plos One! The paper, in collaboration with Zeyao (Patrick) Yang who recently graduated from Indiana University, is attracting a lot of media attention! The theory of emotional contagion hypothesizes that emotions and emotional states are transferred from one person to another by social interactions. Traditional social science studies that date more than half a century ago' (Fromm, The Art of Loving, 1956) aimed at proving that in-person exchanges cause the unconscious emotional alignment of the interacting parties. One hypothesis was that non-verbal cues (body language, facial expressions, tone of the voice, etc.)…
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Style in the age of Instagram!

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J Park, GL Ciampaglia, and E Ferrara. The 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016) Useful links: Arxiv | ACM Our work on Science of Success applied to the Fashion world is attracting a lot of media attention! The paper, in collaboration with IU PhD student Jaehyuk Park and my colleague and friend IU Research Scientist Giovanni Luca Ciampaglia, will be presented at CSCW 2016! We introduce a new machine learning framework, rooted in a through statistical analysis of a combination of physical attributes, professional information, and social media (Instagram) data, that is able to predict the rise to popularity of new fashion models with over 80% accuracy! To test the forecasting ability of our system we actually predicted the success of 6 out 7 new…
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Defining and identifying Sleeping Beauties in science

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Q Ke, E Ferrara, F Radicchi, and A Flammini. Proceedings of the National Academy of Sciences, 2015 Useful links: Arxiv | PNAS Scientific papers typically have a finite lifetime: their rate to attract citations achieves its maximum a few years after publication, and then steadily declines. Previous studies pointed out the existence of a few blatant exceptions: papers whose relevance has not been recognized for decades, but then suddenly become highly influential and cited. The Einstein, Podolsky, and Rosen “paradox” paper is an exemplar Sleeping Beauty. We study how common Sleeping Beauties are in science. We introduce a quantity that captures both the recognition intensity and the duration of the “sleeping” period, and show that Sleeping Beauties are far from exceptional. The distribution of such quantity is continuous and has…
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