Filippo Menczer is an American and Italian academic. He is a University Distinguished Professor and the Luddy Professor of Informatics and Computer Science at the Luddy School of Informatics, Computing, and Engineering, Indiana University. Menczer is the Director of the Observatory on Social Media,[1] a research center where data scientists and journalists study the role of media and technology in society and build tools to analyze and counter disinformation and manipulation on social media. Menczer holds courtesy appointments in Cognitive Science and Physics, is a founding member and advisory council member of the IU Network Science Institute,[2] a former director the Center for Complex Networks and Systems Research,[3] a senior research fellow of the Kinsey Institute, a fellow of the Center for Computer-Mediated Communication,[4] and a former fellow of the Institute for Scientific Interchange in Turin, Italy. In 2020 he was named a Fellow of the ACM.
Menczer's research focuses on Web science, social networks, social media, social computation, Web mining, data science, distributed and intelligent Web applications, and modeling of complex information networks. He introduced the idea of topical and adaptive Web crawlers, a specialized and intelligent type of Web crawler.[10][11]
Analysis by Menczer's team demonstrated the echo-chamber structure of information-diffusion networks on Twitter during the 2010 United States elections.[34] The team found that conservatives almost exclusively retweeted other conservatives while liberals retweeted other liberals. Ten years later, this work received the Test of Time Award at the 15th International AAAI Conference on Web and Social Media (ICWSM).[35] As these patterns of polarization and segregation persist,[36] Menczer's team has developed a model that shows how social influence and unfollowing accelerate the emergence of online echo chambers.[37]
Menczer and colleagues have advanced the understanding of information virality, and in particular the prediction of what memes will go viral based on the structure of early diffusion networks[38][39] and how competition for finite attention helps explain virality patterns.[40][41] In a 2018 paper in Nature Human Behaviour, Menczer and coauthors used a model to show that when agents in a social networks share information under conditions of high information load and/or low attention, the correlation between quality and popularity of information in the system decreases.[42] An erroneous analysis in the paper suggested that this effect alone would be sufficient to explain why fake news are as likely to go viral as legitimate news on Facebook. When the authors discovered the error, they retracted the paper.[43]
Following influential publications on the detection of astroturfing[44][45][46][47][48] and social bots,[49][50] Menczer and his team have studied the complex interplay between cognitive, social, and algorithmic factors that contribute to the vulnerability of social media platforms and people to manipulation,[51][52][53][54] and focused on developing tools to counter such abuse.[55][56] Their bot detection tool, Botometer, was used to assess the prevalence of social bots[57][58] and their sharing activity.[59] Their tool to visualize the spread of low-credibility content, Hoaxy,[60][61][62][63] was used in conjunction with Botometer to reveal the key role played by social bots in spreading low-credibility content during the 2016 United States presidential election.[64][65][66][67][68] Menczer's team also studied perceptions of partisan political bots, finding that Republican users are more likely to confuse conservative bots with humans, whereas Democratic users are more likely to confuse conservative human users with bots.[69] Using bot probes on Twitter, Menczer and coauthors demonstrated a conservative political bias on the platform.[70]
As social media have increased their countermeasures against malicious automated accounts, Menczer and coauthors have shown that coordinated campaigns by inauthentic accounts continue to threaten information integrity on social media, and developed a framework to detect these coordinated networks.[71] They also demonstrated new forms of social media manipulation by which bad actors can grow influence networks[72] and hide high-volume of content with which they flood the network.[73]
Menczer and colleagues have shown that political audience diversity can be used as an indicator of news source reliability in algorithmic ranking.[74]
Textbookedit
The textbook A First Course in Network Science by Menczer, Fortunato, and Davis was published by Cambridge University Press in 2020.[75] The textbook has been translated into Japanese, Chinese, and Korean.
Projectsedit
Observatory on Social Media (OSoMe, pronounced awesome):[76] A research center aimed to study and visualize how information spreads online.[77] Includes data and tools to visualize Twitter trends, diffusion networks, detect social bots, etc.[78][79]
Botometer:[80] A machine learning tool to detect social bots on Twitter. Previously known as BotOrNot. Includes a public API, a social bot dataset repository, and the BotAmp tool[81] to assess the role of automated accounts in boosting a given topic.
Hoaxy:[82] An open-source search and network visualization tool to study the spread of narratives on Twitter. Includes a public API.
Fakey:[83] A mobile game for news literacy. Fakey mimics a social media news feed where you have to tell real news from fake ones.
Kinsey Reporter:[89] A global mobile survey platform to share, explore, and visualize anonymous data about sex and sexual behaviors. Developed in collaboration with the Kinsey Institute. Reports are submitted via Web or smartphone, then available for visualization or offline analysis via a public API.[90][91]
Referencesedit
^"Observatory on Social Media (OSoMe)". Retrieved February 5, 2023.
^Menczer, F.; G. Pant; P. Srinivasan (2004). "Topical Web Crawlers: Evaluating Adaptive Algorithms". ACM Transactions on Internet Technology. 4 (4): 378–419. doi:10.1145/1031114.1031117. S2CID 5931711.
^Srinivasan, P.; F. Menczer; G. Pant (2005). "A General Evaluation Framework for Topical Crawlers". Information Retrieval. 8 (3): 417–447. CiteSeerX10.1.1.6.1074. doi:10.1007/s10791-005-6993-5. S2CID 5351345.
^Jagatic, Tom; Nathaniel Johnson; Markus Jakobsson; Filippo Menczer (October 2007). "Social Phishing". Communications of the ACM. 50 (10): 94–100. doi:10.1145/1290958.1290968. S2CID 15077519.
^LENZ, RYAN (July 22, 2007). "School Conducts Anti-Phishing Research". The Washington Post.
^Maguitman, Ana; Filippo Menczer; Heather Roinestad; Alessandro Vespignani (2005). "Algorithmic detection of semantic similarity". Proceedings of the 14th international conference on World Wide Web - WWW '05. pp. 107–116. doi:10.1145/1060745.1060765. ISBN 978-1595930460. S2CID 2011198.
^Markines, Benjamin; Ciro Cattuto; Filippo Menczer; Dominik Benz; Andreas Hotho; Gerd Stumme (2009). "Evaluating similarity measures for emergent semantics of social tagging". Proceedings of the 18th international conference on World wide web. pp. 641–650. CiteSeerX10.1.1.183.2930. doi:10.1145/1526709.1526796. ISBN 9781605584874. S2CID 2708853.
^Menczer, F (2004). "Lexical and semantic clustering by web links". Journal of the American Society for Information Science and Technology. 55 (14): 1261–1269. CiteSeerX10.1.1.72.1136. doi:10.1002/asi.20081.
^Schifanella, Rossano; Alain Barrat; Ciro Cattuto; Benjamin Markines; Filippo Menczer (2010). "Folks in Folksonomies". Proceedings of the third ACM international conference on Web search and data mining. pp. 271–280. arXiv:1003.2281. Bibcode:2010arXiv1003.2281S. doi:10.1145/1718487.1718521. ISBN 9781605588896. S2CID 10097662.
^Menczer, F (2004). "Evolution of document networks". Proc. Natl. Acad. Sci. U.S.A. 101 (suppl. 1): 5261–5265. Bibcode:2004PNAS..101.5261M. doi:10.1073/pnas.0307554100. PMC387305. PMID 14747653.
^Menczer, F (2002). "Growing and navigating the small world web by local content". Proc. Natl. Acad. Sci. U.S.A. 99 (22): 14014–14019. Bibcode:2002PNAS...9914014M. doi:10.1073/pnas.212348399. PMC137828. PMID 12381792.
^"Researchers: Impact of censorship significant on Google, other search engine results". Network World. March 15, 2006. Archived from the original on May 4, 2014. Retrieved May 4, 2014.
^Meiss, Mark; Filippo Menczer (2008). "Visual comparison of search results: A censorship case study". First Monday. 13 (7). doi:10.5210/fm.v13i7.2019.
^Fortunato, Santo; Alessandro Flammini; Filippo Menczer; Alessandro Vespignani (2006). "Topical interests and the mitigation of search engine bias". Proc. Natl. Acad. Sci. U.S.A. 103 (34): 12684–12689. arXiv:cs/0511005. Bibcode:2006PNAS..10312684F. doi:10.1073/pnas.0605525103. PMC1568910. PMID 16901979.
^"Egalitarian engines". The Economist. November 17, 2005.
^Conover, Michael; Clayton Davis; Emilio Ferrara; Karissa McKelvey; Filippo Menczer; Alessandro Flammini (2013). "The Geospatial Characteristics of a Social Movement Communication Network". PLOS ONE. 8 (3): e55957. arXiv:1306.5473. Bibcode:2013PLoSO...855957C. doi:10.1371/journal.pone.0055957. PMC3590214. PMID 23483885.
^Conover, Michael; Emilio Ferrara; Filippo Menczer; Alessandro Flammini (2013). "The Digital Evolution of Occupy Wall Street". PLOS ONE. 8 (5): e64679. arXiv:1306.5474. Bibcode:2013PLoSO...864679C. doi:10.1371/journal.pone.0064679. PMC3667169. PMID 23734215.
^Varol, Onur; Emilio Ferrara; Christine L. Ogan; Filippo Menczer; Alessandro Flammini (2014). "Evolution of online user behavior during a social upheaval". Proceedings of the 2014 ACM conference on Web science. pp. 81–90. arXiv:1406.7197. Bibcode:2014arXiv1406.7197V. doi:10.1145/2615569.2615699. ISBN 9781450326223. S2CID 6986974.
^Conover, Michael; Bruno Gonçalves; Alessandro Flammini; Filippo Menczer (2012). "Partisan asymmetries in online political activity". EPJ Data Science. 1: 6. arXiv:1205.1010. Bibcode:2012arXiv1205.1010C. doi:10.1140/epjds6. S2CID 2347930.
^Robb, Amanda (November 16, 2017). "Anatomy of a Fake News Scandal". Rolling Stone. Retrieved 18 March 2019.
^Solon, Olivia (18 December 2017). "How Syria's White Helmets became victims of an online propaganda machine". The Guardian. Retrieved 18 March 2019.
^Bing, Christopher (November 2, 2018). "Exclusive: Twitter deletes over 10,000 accounts that sought to discourage U.S. voting". Reuters. Retrieved 18 March 2019.
^Pierri, Francesco; Perry, Brea; DeVerna, Matthew; Yang, Kai-Cheng; Flammini, Alessandro; Menczer, Filippo; Bryden, John (2022). "Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal". Scientific Reports. 12 (1): 5966. arXiv:2104.10635. Bibcode:2022NatSR..12.5966P. doi:10.1038/s41598-022-10070-w. PMC9043199. PMID 35474313. S2CID 247939732.
^Conover, Michael; Jacob Ratkiewicz; Matthew Francisco; Bruno Gonçalves; Filippo Menczer; Alessandro Flammini (2011). "Political Polarization on Twitter". Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.
^"ICWSM-2021 Award Winners". Retrieved February 5, 2023.
^Nikolov, Dimitar; Alessandro Flammini; Filippo Menczer (2021). "Right and left, partisanship predicts (asymmetric) vulnerability to misinformation". Harvard Kennedy School Misinformation Review. 1 (7). arXiv:2010.01462. doi:10.37016/mr-2020-55. S2CID 234356375.
^Sasahara, Kazutoshi; Wen Chen; Hao Peng; Giovanni Luca Ciampaglia; Alessandro Flammini; Filippo Menczer (2021). "Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers". Journal of Computational Social Science. 4: 381–402. arXiv:1905.03919. doi:10.1007/s42001-020-00084-7. S2CID 257090517.
^Weng, Lilian; Filippo Menczer; Yong-Yeol Ahn (2013). "Virality Prediction and Community Structure in Social Networks". Scientific Reports. 3: 2522. arXiv:1306.0158. Bibcode:2013NatSR...3E2522W. doi:10.1038/srep02522. PMC3755286. PMID 23982106.
^Matson, John (December 17, 2013). "Twitter Trends Help Researchers Forecast Viral Memes". Scientific American.
^Weng, L; A Flammini; A Vespignani; F Menczer (2012). "Competition among memes in a world with limited attention". Scientific Reports. 2: 335. Bibcode:2012NatSR...2E.335W. doi:10.1038/srep00335. PMC3315179. PMID 22461971.
^McKenna, Phil (April 13, 2012). "Going viral on Twitter is a random act". New Scientist.
^Qiu, X.; F. M. Oliveira, D.; Sahami Shirazi, A.; Flammini, A.; Menczer, F. (2017). "Limited individual attention and online virality of low-quality information". Nature Human Behaviour. 1 (7): 0132. arXiv:1701.02694. Bibcode:2017arXiv170102694Q. doi:10.1038/s41562-017-0132. S2CID 23363010.
^Dancyger, Lilly (10 January 2019). "Researchers Retract Widely Cited Fake-News Study". Rolling Stone. Retrieved 18 March 2019.
^Ratkiewicz, Jacob; Michael Conover; Mark Meiss; Bruno Gonçalves; Snehal Patil; Alessandro Flammini; Filippo Menczer (2011). "Truthy". Proceedings of the 20th international conference companion on World wide web. pp. 249–252. arXiv:1011.3768. doi:10.1145/1963192.1963301. ISBN 9781450306379. S2CID 1958549.
^Ratkiewicz, Jacob; Michael Conover; Mark Meiss; Bruno Gonçalves; Alessandro Flammini; Filippo Menczer (2011). "Detecting and Tracking Political Abuse in Social Media". Proc. Fifth International AAAI Conference on Weblogs and Social Media.
^Giles, Jim (27 October 2010). "Twitter tool roots out disguised mass postings". New Scientist.
^Keller, Jared (November 10, 2010). "When Campaigns Manipulate Social Media". The Atlantic.
^Silverman, Craig (November 4, 2011). "Misinformation Propagation". Columbia Journalism Review.
^Ferrara, Emilio; Varol, Onur; Davis, Clayton A.; Menczer, Filippo; Flammini, Alessandro (2016). "The rise of social bots". Comm. ACM. 59 (7): 96–104. arXiv:1407.5225. doi:10.1145/2818717. S2CID 1914124.
^Urbina, Ian (August 10, 2013). "I Flirt and Tweet. Follow Me at #Socialbot". The New York Times.
^Lazer, D.; Baum, M.; Benkler, Y.; Berinsky, A.; Greenhill, K.; Menczer, F.; et al. (2018). "The science of fake news". Science. 359 (6380): 1094–1096. arXiv:2307.07903. Bibcode:2018Sci...359.1094L. doi:10.1126/science.aao2998. PMID 29590025. S2CID 4410672.
^Menczer, Filippo (November 27, 2016). "Misinformation on social media: Can technology save us?". The Conversation. Retrieved 18 March 2019.
^Bergado, Gabe (December 14, 2016). "The Man Who Saw Fake News Coming". Inverse. Retrieved 18 March 2019.
^Mitchell Waldrop, M. (November 28, 2017). "The genuine problem of fake news". PNAS. 114 (48): 12631–12634. Bibcode:2017PNAS..11412631W. doi:10.1073/pnas.1719005114. PMC5715799. PMID 29146827.
^Ciampaglia, Giovanni Luca; Menczer, Filippo (June 20, 2018). "Misinformation and biases infect social media, both intentionally and accidentally". The Conversation. Retrieved 18 March 2019.
^Zamudio-Suaréz, Fernanda (December 22, 2016). "A Professor Once Targeted by Fake News Now Is Helping to Visualize It". The Chronicle of Higher Education. Retrieved 18 March 2019.
^Varol, Onur; Ferrara, Emilio; Davis, Clayton A.; Menczer, Filippo; Flammini, Alessandro (2017). "Online Human-Bot Interactions: Detection, Estimation, and Characterization". Proceedings of the International AAAI Conference on Web and Social Media. 11: 280–289. arXiv:1703.03107. Bibcode:2017arXiv170303107V. doi:10.1609/icwsm.v11i1.14871. S2CID 15103351.
^Chong, Zoey (March 14, 2017). "Up to 48 million Twitter accounts are bots, study says". CNET. Retrieved 18 March 2019.
^WOJCIK, STEFAN; MESSING, SOLOMON; SMITH, AARON; RAINIE, LEE; HITLIN, PAUL (2018-04-09). "Bots in the Twittersphere". Pew Research Center. Retrieved 18 March 2019.
^Gershgorn, Dave (December 21, 2016). "There's a new tool to visualize how fake news is spread on Twitter". Quartz. Retrieved 18 March 2019.
^Kauffman, Gretel (December 22, 2016). "Indiana University tech tool 'Hoaxy' shows how fake news spreads". The Christian Science Monitor. Retrieved 18 March 2019.
^Skallerup Bessette, Lee (January 9, 2017). "Hoaxy Visualizes the Spread of Online News". The Chronicle of Higher Education. Retrieved 18 March 2019.
^Reaney, Patricia (December 21, 2016). "U.S. university launches tool to show how fake news spreads". Reuters. Retrieved 18 March 2019.
^Shao, C.; Ciampaglia, G. L.; Varol, O.; Yang, K.; Flammini, A.; Menczer, F. (2018). "The spread of low-credibility content by social bots". Nature Communications. 9 (1): 4787. arXiv:1707.07592. Bibcode:2018NatCo...9.4787S. doi:10.1038/s41467-018-06930-7. PMC6246561. PMID 30459415.
^Shao, C.; Hui, P.; Wang, L.; Jiang, X.; Flammini, A.; Menczer, F.; Ciampaglia, G. L. (2018). "Anatomy of an online misinformation network". PLOS ONE. 13 (4): e0196087. arXiv:1801.06122. Bibcode:2018PLoSO..1396087S. doi:10.1371/journal.pone.0196087. PMC5922526. PMID 29702657.
^Ouellette, Jennifer (21 November 2018). "Study: It only takes a few seconds for bots to spread misinformation". Ars Technica. Retrieved 18 March 2019.
^Boyce, Jasmin (November 21, 2018). "'Relatively few' Twitter bots were needed to spread misinformation and overwhelm fact checkers, study finds". NBC News. Retrieved 18 March 2019.
^de Haldevang, Max (November 20, 2018). "Twitter could have partly blocked Russia's 2016 election attack with CAPTCHAs". Quartz. Retrieved 18 March 2019.
^Yan, Harry; Yang, Kai-Cheng; Menczer, Filippo; Shanahan, James (2021). "Asymmetrical Perceptions of Partisan Political Bots". New Media and Society. 23 (10): 3016–3037. doi:10.1177/1461444820942744. S2CID 225633835.
^Chen, Wen; Pacheco, Diogo; Yang, Kai-Cheng; Menczer, Filippo (2021). "Neutral Bots Probe Political Bias on Social Media". Nature Communications. 12 (1): 5580. arXiv:2005.08141. Bibcode:2021NatCo..12.5580C. doi:10.1038/s41467-021-25738-6. PMC8458339. PMID 34552073.
^Pacheco, Diogo; Hui, Pik-Mai; Torres-Lugo, Christopher; Truong, Bao Tran; Flammini, Alessandro; Menczer, Filippo (2021). "Uncovering Coordinated Networks on Social Media: Methods and Case Studies". Proc. International AAAI Conference on Web and Social Media (ICWSM). AAAI. pp. 455–466. arXiv:2001.05658. doi:10.1609/icwsm.v15i1.18075.
^Torres-Lugo, Christopher; Yang, Kai-Cheng; Menczer, Filippo (2022). "The Manufacture of Partisan Echo Chambers by Follow Train Abuse on Twitter". Proc. International AAAI Conference on Web and Social Media (ICWSM). AAAI. pp. 1017–1028. arXiv:2010.13691. doi:10.1609/icwsm.v16i1.19354.
^Torres-Lugo, Christopher; Pote, Manita; Nwala, Alexander; Menczer, Filippo (2022). "Manipulating Twitter through Deletions". Proc. International AAAI Conference on Web and Social Media (ICWSM). AAAI. pp. 1029–1039. arXiv:2203.13893. doi:10.1609/icwsm.v16i1.19355.
^"OSoMe Tools". Observatory on Social Media. Retrieved 18 March 2019.
^Davis, Clayton A.; et al. (2016). "OSoMe: The IUNI Observatory on Social Media". PeerJ Computer Science. 2: e87. doi:10.7717/peerj-cs.87. hdl:11858/00-001M-0000-002D-21B1-D.