Twitter bots for good, and information contagion!

Our latest work, titled “Evidence of complex contagion of information in social media: An experiment using Twitter bots” was published in Plos One on September 22, 2017!

In this study, in collaboration with Bjarke Mønsted, Piotr Sapieżyński, and Sune Lehmann from the Denmark Technical University (DTU), we studied the effects of deploying positive interventions on Twitter using social bots.

The DTU team developed and deployed 39 Twitter bots, which connected within the community of users of San Francisco, during the second half of 2014. Starting in early October 2014 and throughout the rest of the year, the bots, some of which accrued thousands of followers, started to introduce positive memes, (listed in the table), to foster public health, fitness behaviors, and doing social good.

By using mathematical modelling in combination with statistical techniques, we used the data we collected to study how information spreads on Twitter. In particular, we seek to understand whether information passes from person to person like an epidemic spreading (or simple contagion), where each exposure to a virus (or likewise a meme) yields an independent probability of contracting the given disease, or otherwise whether being exposed to the meme multiple times from multiple sources greatly enhances the probability of that meme being adopted/retweeted by a user (complex contagion). 

Our analysis shows that the complex contagion hypothesis is the most likely to fully capture information diffusion dynamics on Twitter. By means of our experiment, in which Twitter users naturally partitioned themselves in groups following one bot, two bots, three bots, etc., we were capable of recording the number and sources of exposures of memes for each user in our pool, and therefore estimate, for the first time in a setting similar to a semi-controlled experiment, what factors play a role in information diffusion online: it appears that, for the type of positive memes we introduced, seeing them from multiple sources greatly enhanced the probability of retweeting the meme.

We hope to use what we learned from this study to improve our ability to deliver online interventions in the future!

You can read the rest of the study on Plos One!

Cite as:

Mønsted B, Sapieżyński P, Ferrara E, Lehmann S (2017) Evidence of complex contagion of information in social media: An experiment using Twitter bots. PLOS ONE 12(9): e0184148.

 Press coverage:

  1. Researchers find that Twitter bots can be used for good – Tech Crunch
  2. Twitter Bots Can Encourage Decent Conduct, Not Just Fake News – News18
  3. Twitter bots for good: USC ISI study reveals how information spreads on social media – EurekAlert!


Source: Emilio

Diffusion of ISIS propaganda on Twitter

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 they generate highly-infectious cascades of information contagion.

To read further, please refer to the published journal version. The paper is also available on arxiv.

Cite as:

Emilio Ferrara. Contagion dynamics of extremist propaganda in social networks. Information Sciences (2017) doi:10.1016/j.ins.2017.07.030

Source: Emilio

#MacronLeaks, bots, and the 2017 French election

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 thousand users in the time span of a few days.

The analysis revealed important new insights about social bot operations and disinformation campaigns on online social media:

  1. Many bot accounts that supported alt-right narrative in the context of #MacronLeaks were originally created shortly prior to the 2016 U.S. presidential election and used to support the same views in the context of American politics. The accounts went dark after November 8, 2016, only to re-emerge at the beginning of May 2017 to push #MacronLeaks, attack Macron, and support the far-right candidate Marine Le Pen. This corroborates a recent hypothesis about the existence of black markets for reusable political botnets. 
  2. The audience engaged with #MacronLeaks was mainly English-speaking American userbase, rather than French users. Their prior interests prominently feature support for Trump and Republican views, as well as more extreme, alt-right narratives. This suggests a possible explanation for the scarce success of the disinformation campaign: French users, those more likely to vote in support of Macron, were not mobilized nor significantly engaged in discussing these document leaks.

The paper, titled “Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election”, is set for publication on August 7, 2017 in the peer-reviewed journal First Monday. To learn more about this work, read the preprint paper available on SSRN.

Cite as:

Emilio Ferrara. Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election. First Monday, 22(8), 2017


  1. Fake news bots are so economical, you can use them over and over – Harvard NiemanLab
  2. Pro-Trump Twitter bots were also used to target Macron, research shows – The Verge
  3. There’s a Bit of Overlap Between Bots Trying to Manipulate American and French Elections – New York Magazine
  4. Research links pro-Trump, anti-Macron Twitter bots – The Hill
  5. The Same Twitter Bots That Helped Trump Tried to Sink Macron, Researcher Says – VICE

Press in non-English media

  1. Macron Leaks : Les bots pro-Trump utilisés dans la campagne de désinformation – Le Monde (in French)

Source: Emilio

Millions of social bots invaded Twitter!

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 human and bot-controlled accounts across the spectrum of complexity, ranging from simple bots to very sophisticated ones fueled by advanced AI. We discovered that, while human accounts and simple bots are very easy to identify, both by other humans and by our models, there exist a family of sophisticated social AIs that systematically escape identification by our models and by human snap-judgment.

Our second finding reveals that a significant fraction of Twitter accounts, between 9% and 15%,  are likely social bots. This translates in nearly 50 million accounts, according to recent estimates that put the Twitter userbase at above 320 million. Although not all bots are dangerous, many are used for malicious purposes: in the past, for example, Twitter bots have been used to manipulate public opinion during election times, to manipulate the stock market, and by extremist groups for radical propaganda.

To learn more, read our paper: Online Human-Bot Interactions: Detection, Estimation, and Characterization.

Cite as:

Onur Varol, Emilio Ferrara, Clayton Davis, Filippo Menczer, Alessandro Flammini. Online Human-Bot Interactions: Detection, Estimation, and Characterization. ICWSM 2017


Press Coverage

  1. CMO Today: Marketers and Political Wonks Gather for SXSW – The Wall Street Journal
  2. Huge number of Twitter accounts are not operated by humans – ABC News
  3. Up to 48 million Twitter accounts are bots, study says – CNET
  4. R u bot or not? – VICE
  5. New Machine Learning Framework Uncovers Twitter’s Vast Bot Population – VICE/Motherboard
  6. A Whopping 48 Million Twitter Accounts Are Actually Just Bots, Study Says – Tech Times
  7. Study reveals whopping 48M Twitter accounts are actually bots – CBS News
  8. Twitter is home to nearly 48 million bots, according to report – The Daily Dot
  9. As many as 48 million Twitter accounts aren’t people, says study – CNBC
  10. New Study Says 48 Million Accounts On Twitter Are Bots – We are social media
  11. Almost 48 million Twitter accounts are bots – Axios
  12. Twitter user accounts: around 15% or 48 million are bots [study] – The Vanguard
  13. Rise of the TWITTERBOTS – Daily Mail
  14. 15 per cent of Twitter is bots, but not the Kardashian kind – The Inquirer
  15. 48 mn Twitter accounts are bots, says study – The Economic Times
  16. 9-15 per cent of Twitter accounts are bots, reveals study – Financial Express
  17. Nearly 48 million Twitter accounts are bots: study – Deccan herald
  18. Study: Nearly 48 Million Twitter Accounts Are Fake; Many Push Political Agendas – The Libertarian Republic
  19. As many as 48 million accounts on Twitter are actually bots, study finds – Sacramento Bee
  20. Study Reveals Roughly 48M Twitter Accounts Are Actually Bots – CBS DFW
  21. Up to 48 million Twitter accounts may be Bots – Financial Buzz
  22. Up to 15% of Twitter accounts are not real people – Blasting News
  23. Tech Bytes: Twitter is Being Invaded by Bots – WDIO Eyewitness News
  24. About 9-15% of Twitter accounts are bots: Study – The Indian Express
  25. Twitter Has Nearly 48 Million Bot Accounts, So Don’t Get Hurt By All Those Online Trolls – India Times
  26. Twitter May Have 45 Million Bots on Its Hands – Investopedia
  27. Bots run amok on Twitter – My Broadband
  28. 9-15% of Twitter accounts are bots: Study – MENA FN
  29. Up To 15 Percent Of Twitter Users Are Bots, Study Says – Vocativ
  30. 48 million active Twitter accounts could be bots – Gearbrain
  31. Study: 15% of Twitter accounts could be bots – Marketing Dive
  32. 15% of Twitter users are actually bots, study claims – MemeBurn
  33. Almost 48 million Twitter accounts are bots – Click Lancashire

Press in non-English media

  1. Bad Bot oder Mensch – das ist hier die Frage – Medien Milch (in German)
  2. Studie: Bis zu 48 Millionen Twitter-Nutzer sind in Wirklichkeit Bots – T3N (in German)
  3. Der Aufstieg der Twitter-Bots: 48 Millionen Nutzer sind nicht menschlich – Studie – Sputnik News (in German)
  4. Studie: Bis zu 48 Millionen Nutzer auf Twitter sind Bots – der Standard (in German)
  5. “Blade Runner”-Test für Twitter-Accounts: Bot oder Mensch? – der Standard (in German)
  6. Bot-Paradies Twitter – Sachsische Zeitung (in German)
  7. 15 Prozent Social Bots? – DLF24 (in German)
  8. TWITTER: IST JEDER SIEBTE USER EIN BOT? – UberGizmo (in German)
  9. Twitter: Bis zu 48 Millionen Bot-Profile – Heise (in German)
  10. Studie: Bis zu 15 Prozent aller aktiven, englischsprachigen Twitter-Konten sind Bots – Netzpolitik (in German)
  11. Automatische Erregung – Wiener Zeitung (in German)
  12. 15 por ciento de las cuentas de Twitter son ‘bots’: estudio – CNET (in Spanish)
  13. 48 de los 319 millones de usuarios activos de Twitter son bots – TIC Beat (in Spanish)
  14. 15% de las cuentas de Twitter son ‘bots’ – Merca 2.0 (in Spanish)
  15. 48 de los 319 de usuarios activos en Twitter son bots – MDZ (in Spanish)
  16. Twitter, paradis des «bots»? – Slate (in French)
  17. Twitter compterait 48 millions de comptes gérés par des robots – MeltyStyle (in French)
  18. Twitter : 48 millions de comptes sont des bots – blog du moderateur (in French)
  19. ’30 tot 50 miljoen actieve Twitter-accounts zijn bots’ – NOS (in Dutch)
  20. 48 εκατομμύρια χρήστες στο Twitter δεν είναι άνθρωποι, σύμφωνα με έρευνα Πηγή – LiFo (in Greek)
  21. 48 triệu người dùng Twitter là bot và mối nguy hại – Khoa Hoc Phattrien (in Vietnamese)

Source: Emilio

Complex System Society 2016 Junior Scientific Award!

I was selected as recipient of the 2016 Junior Scientific Award by the Complex System Society!

The award readsEmilio 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. 


Source: Emilio

Twitter, Social Bots, and the US Presidential Elections!

First Monday: Social bots distort the 2016 U.S. Presidential election online discussion

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 their candidate, altering the public perception by giving the impression that there is a grassroot positive and sustained support for that candidate.
  • It remains impossible, to date, to determine who’s behind these bots (the master puppeteers): single individuals, third-party organizations, and even foreign governments may be orchestrating these operations.

To know more, read our paper: Social bots distort the 2016 U.S. Presidential election online discussion

Cite as:

Alessandro Bessi, Emilio Ferrara. Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21(11), 2016

Press Coverage

  1. How the Bot-y Politic Influenced This Election – MIT Technology Review
  2. Facebook, Twitter & Trump – The New York Review of Books
  3. How Twitter bots played a role in electing Donald Trump – WIRED
  4. How Twitter bots helped Donald Trump win the US presidential election – Arstechnica
  5. On Twitter, No One Knows You Are a Trump Bot – Fast Company
  6. Election 2016 Belongs to the Twitter Bots – VICE
  7. Almost a fifth of election chatter on Twitter comes from bots – Fusion
  8. Study reports that nearly 20% of election-related tweets were ‘algorithmically driven’ – Talking New Media
  9. How Twitter bots affected the US presidential campaign – The Conversation
  10. Advertising is driving social media-fuelled fake news and it is here to stay – The Conversation
  11. 20% of All Election Related Tweets Came From Non-Humans – Futurism
  12. Twitter Bots Dominate 2016 Presidential Election: New Study – Heavy
  13. Tracking The Election With Social Media In Real-Time: How Accurate Is It? – Heavy
  14. BOTS ‘SWAY’ ELECTION Fake tweets by social media robots could swing US Presidential election – The Sun (UK)
  15. A fifth of all US election tweets have come from bots – ABC News
  16. There are 400,000 Bots That Just Tweet Political Views All Day – Investopedia
  17. Real, or not? USC study finds many political tweets come from fake accounts – Science Blog
  18. Software bots distort Donald Trump support on Twitter: Study – ETCIO
  19. How hackers, social bots, data analysts shaped the U.S. election – The Nation
  20. That swarm of political tweets in your feed? Many could be from bots – The Business Journals
  21. Software ‘bots’ distort Trump support on Twitter – New Vision
  22. Bots Invade Twitter, Spreads Misinformation On US Election – EconoTimes
  23. Software ‘bots’ seen skewing support for Trump on Twitter – The Japan Times
  24. US Presidential Elections 2016: Bot-generated fake tweets influencing US election outcome, says new study – Indian Express
  25. US elections 2016: Researchers show how Twitter bots are trying to influence the poll in favour of Trump – International Business Times
  26. Hillary vs Trump: Most of the election chatter online by Twitter bots, says study – Tech 2 First Post
  27. Twitter bots distort Trump support – iAfrica
  28. Social Media ‘Bots’ Working To Influence U.S. Election – CBS San Francisco
  29. Elezioni Usa: il 19% dei tweet elettorali è prodotto da software – (in Italian)
  30. Almost a fifth of election chatter on Twitter comes from bots – Full Act
  31. Software ‘bots’ distort Trump support on Twitter: study – Yahoo! News
  32. Bots Will Break 2016 US Elections Results – iTechPost
  33. Scientist Worries Robot-Generated Tweets Could Compromise The Presidential Election – Newsroom America
  34. Software ‘bots’ distort Trump support on Twitter: study –
  35. Spotlight: Fake tweets endanger integrity of U.S. presidential election – XinhuanNet
  36. New Study: Twitter Bots Amount for One-Fifth of US Election Conversation – Dispatch Weekly
  37. Are Robot generated Tweets compromising US Polls? – TechRadar India
  38. Fake tweets endanger integrity of US presidential election – Global Times
  39. Software ‘bots’ distort Trump support on Twitter: study – The Daily Star
  40. Software ‘bots’ distort Trump support on Twitter: study – News Dog
  41. Malicious Twitter bots could have profound consequences for the election – RawStory
  42. ‘Robot-generated fake tweets influencing US election outcome’ – DNA – Daily News & Analysis
  43. Sophisticated Bot-Generated Tweets Could Influence Outcome of US Presidential Election – Telegiz
  44. UIC Journal Shows ‘Bots’ Sway Political Discourse, Could Impact Election – NewsWise
  45. Bot-generated tweets could threaten integrity of 2016 US presidential election: Study –
  46. Robots behind the millions of tweets: “The integrity at danger” – Svenska Dagbladet (in Swedish)
  47. Bot generated tweets influence US Presidential election polls – I4U News
  48. High percentage of robot-generated fake tweets likely to influence public opinion – NewsGram
  49. ‘Robot-generated fake tweets influencing US election outcome’ – Press Trust of India
  50. Robot-generated fake tweets influencing US election outcome: Study – IndianExpress
  51. Fake Tweets, real consequences for the election –
  52. Real, or not? USC study finds many political tweets come from fake accounts – USC News
  53. We’re in a digital world filled with lots of social bots – USC News

Source: Emilio

The Rise of Social Bots!

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!

The Rise of Social Bots

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.

Cite as:

Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, Alessandro Flammini. The Rise of Social Bots. Communications of the ACM, Vol. 59 No. 7, Pages 96-104

Source: Emilio

The structure of Mafia syndacates

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 criminal offenses.


Our report illustrates the limits of traditional investigative methods like wiretapping. Criminals high up in the organization hierarchy do not occupy the most central positions in the criminal network, and oftentimes do not appear in the reconstructed criminal network at all. However, we also suggest possible strategies of intervention. We show that, although criminal networks (i.e., the network encoding mobsters and crime relationships) are extremely resilient to different kinds of attacks, contact networks (i.e., the network reporting suspects and reciprocated phone calls) are much more vulnerable, and their analysis can yield extremely valuable insights.


Cite as:

Santa Agreste, Salvatore Catanese, Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara, Network structure and resilience of Mafia syndicates, Information Sciences, Volume 351, Pages 30-47, 2016.

Source: Emilio

Emotional contagion in Twitter!

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.) are crucial ingredients for emotional contagion.

Fast forward 60+ years, nowadays we are trying to validate the theory of emotional contagion via social media interactions! This setting is much weaker than the traditional one: first, our goal is to assess whether only textual cues are sufficient to trigger emotional contagion, through interactions where non-verbal stimuli are absent; then, we try to isolate this phenomenon in a social platform (Twitter) where links among users carry a weak meaning (users follow others mostly based on shared interests, rather than on pre-existing links in the offline world, like on Facebook). Indeed, it’s quite intuitive to expect that, if a Facebook friend informs us of a good (or bad) news, we will be happy (or sad) for her/him. However, what about complete strangers? Is language alone powerful enough to change our emotional states when exposed to emotional contents?

Our study describes phenomena compatible with emotional contagion, and proposes clever statistical techniques aimed at discounting biases common in observational social network studies.

Positive emotions are stronger than negative ones, in that they successfully affect up to four times more frequently users exposed to them than negative emotions. Furthermore, not all users are equally susceptible to emotional contagion: similarly to what happens with people’ susceptibility to catching the flu, or that of computers to catch a virus, some individuals are more inclined to change emotional states as a reaction to the contents (and the emotions) they are exposed to, while others hold stronger emotional states that rarely change, irrespectively of the type of emotional stimuli they receive.

Read the paper to learn moreMeasuring Emotional Contagion in Social Media. Plos One, 2015



Media coverage

  1. Why you need to purge your Twitter feed of angry people – The Telegraph
  2. Twitter Emotions Are Contagious, Says New Study, But At Least The Positive Ones Are More So Than The Negative Ones – Bustle
  3. Twitter users more likely to share happiness than sadness – The Rakyat post
  4. Lo que encontramos en las redes sociales afecta cada vez más a nuestro estado de ánimo – Puro Marketing (in Spanish)
  5. La joie, plus partagée que la tristesse sur Twitter – Luxemburg Wort (in French)
  6. La joie, un sentiment virtuellement plus partagé que la tristesse sur Twitter – Le Soir (in French)
  7. Tuitea la alegría, que eso se pega – Primera Hora (in Spanish)
  8. Study: Twitter “infects” people with positive emotions – (in Russian)
  9. Cómo Facebook y Twitter pueden influir en tu estado de ánimo – La Nacion (in Spanish)
  10. Twitter reacts positively to upbeat emotions, study finds – USC News
  11. Positive emotions more contagious than negative ones on Twitter –
  12. New data suggest social media brings out the best in us, after all – Quartz
  13. Bad news travels fast but positive posts spread wide – The Straits Times
  14. Positive content has greater reach – Business first magazine
  15. Data Shows that Positive Content Does Better on Social Media – Good

Source: Emilio