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Filippo Menczer

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Filippo Menczer
Born16 May 1965 (1965-05-16) (age 59)
Rome, Italy
Alma materSapienza University of Rome
University of California, San Diego
Scientific career
FieldsCognitive science
Computer science
Physics
InstitutionsIndiana University Bloomington
Websitecnets.indiana.edu/fil/

Filippo Menczer (born 16 May 1965) 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.

Education, career, service

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Menczer holds a Laurea in physics from the Sapienza University of Rome and a PhD in computer science and cognitive science from the University of California, San Diego. He used to be an assistant professor of management sciences at the University of Iowa, and a fellow-at-large of the Santa Fe Institute. At Indiana University Bloomington since 2003, he served as division chair in the Luddy School in 2009–2011. Menczer has been the recipient of Fulbright, Rotary Foundation, and NATO fellowships, and a CAREER Award from the National Science Foundation. He holds editorial positions for the journals Network Science,[5] EPJ Data Science,[6] PeerJ Computer Science,[7] and HKS Misinformation Review.[8] He has served as program or track chair for various conferences including The Web Conference and the ACM Conference on Hypertext and Social Media. He was general chair of the ACM Web Science 2014 Conference[9] and general co-chair of the NetSci 2017 Conference.

Research

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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]

Menczer is also known for his work on social phishing,[12][13] a type of phishing attacks that leverage friendship information from social networks, yielding over 70% success rate in experiments (with Markus Jakobsson); semantic similarity measures for information and social networks;[14][15][16][17] models of complex information and social networks (with Alessandro Vespignani and others);[18][19][20][21] search engine censorship;[22][23] and search engine bias.[24][25]

The group led by Menczer has analyzed and modeled how memes, information, and misinformation spread through social media in domains such as the Occupy movement,[26][27] the Gezi Park protests,[28] and political elections.[29] Data and tools from Menczer's lab have aided in finding the roots of the Pizzagate conspiracy theory[30] and the disinformation campaign targeting the White Helmets,[31] and in taking down voter-suppression bots on Twitter.[32] Menczer and coauthors have also found a link between online COVID-19 misinformation and vaccination hesitancy.[33]

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]

Textbook

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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.

Projects

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  • 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]

References

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  1. ^ "Observatory on Social Media (OSoMe)". Retrieved February 5, 2023.
  2. ^ "IUNI". Retrieved March 18, 2019.
  3. ^ "Center for Complex Networks and Systems Research (CNetS)". Retrieved May 8, 2014.
  4. ^ "Center for Computer-Mediated Communication". Retrieved March 18, 2019.
  5. ^ "Editorial Board". Network Science. Retrieved March 18, 2019.
  6. ^ "Editorial Board". EPJ Data Science Editorial Board. Retrieved March 18, 2019.
  7. ^ "PeerJ Academic Editors". PeerJ. Retrieved March 18, 2019.
  8. ^ "HKS Misinformation Review Editorial Board". Retrieved February 5, 2023.
  9. ^ "Web Science 2014". Retrieved May 4, 2014.
  10. ^ 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.
  11. ^ Srinivasan, P.; F. Menczer; G. Pant (2005). "A General Evaluation Framework for Topical Crawlers". Information Retrieval. 8 (3): 417–447. CiteSeerX 10.1.1.6.1074. doi:10.1007/s10791-005-6993-5. S2CID 5351345.
  12. ^ 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.
  13. ^ LENZ, RYAN (July 22, 2007). "School Conducts Anti-Phishing Research". The Washington Post.
  14. ^ 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.
  15. ^ 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. CiteSeerX 10.1.1.183.2930. doi:10.1145/1526709.1526796. ISBN 9781605584874. S2CID 2708853.
  16. ^ Menczer, F (2004). "Lexical and semantic clustering by web links". Journal of the American Society for Information Science and Technology. 55 (14): 1261–1269. CiteSeerX 10.1.1.72.1136. doi:10.1002/asi.20081.
  17. ^ 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.
  18. ^ Fortunato, Santo; Alessandro Flammini; Filippo Menczer (2006). "Scale-free network growth by ranking". Physical Review Letters. 96 (21): 218701. arXiv:cond-mat/0602081. Bibcode:2006PhRvL..96u8701F. doi:10.1103/PhysRevLett.96.218701. PMID 16803279. S2CID 11357370.
  19. ^ Ratkiewicz, Jacob; Santo Fortunato; Alessandro Flammini; Filippo Menczer; Alessandro Vespignani (2010). "Characterizing and modeling the dynamics of online popularity". Physical Review Letters. 105 (15): 158701. arXiv:1005.2704. Bibcode:2010PhRvL.105o8701R. doi:10.1103/PhysRevLett.105.158701. PMID 21230945. S2CID 17597814.
  20. ^ 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. PMC 387305. PMID 14747653.
  21. ^ 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. PMC 137828. PMID 12381792.
  22. ^ "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.
  23. ^ Meiss, Mark; Filippo Menczer (2008). "Visual comparison of search results: A censorship case study". First Monday. 13 (7). doi:10.5210/fm.v13i7.2019.
  24. ^ 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. PMC 1568910. PMID 16901979.
  25. ^ "Egalitarian engines". The Economist. November 17, 2005.
  26. ^ 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. PMC 3590214. PMID 23483885.
  27. ^ 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. PMC 3667169. PMID 23734215.
  28. ^ 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.
  29. ^ 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.
  30. ^ Robb, Amanda (November 16, 2017). "Anatomy of a Fake News Scandal". Rolling Stone. Retrieved 18 March 2019.
  31. ^ Solon, Olivia (18 December 2017). "How Syria's White Helmets became victims of an online propaganda machine". The Guardian. Retrieved 18 March 2019.
  32. ^ Bing, Christopher (November 2, 2018). "Exclusive: Twitter deletes over 10,000 accounts that sought to discourage U.S. voting". Reuters. Retrieved 18 March 2019.
  33. ^ 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. PMC 9043199. PMID 35474313. S2CID 247939732.
  34. ^ 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.
  35. ^ "ICWSM-2021 Award Winners". Retrieved February 5, 2023.
  36. ^ 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.
  37. ^ 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.
  38. ^ 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. PMC 3755286. PMID 23982106.
  39. ^ Matson, John (December 17, 2013). "Twitter Trends Help Researchers Forecast Viral Memes". Scientific American.
  40. ^ 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. PMC 3315179. PMID 22461971.
  41. ^ McKenna, Phil (April 13, 2012). "Going viral on Twitter is a random act". New Scientist.
  42. ^ 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. (Retracted, see doi:10.1038/s41562-018-0507-0,  Retraction Watch)
  43. ^ Dancyger, Lilly (10 January 2019). "Researchers Retract Widely Cited Fake-News Study". Rolling Stone. Retrieved 18 March 2019.
  44. ^ 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.
  45. ^ 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.
  46. ^ Giles, Jim (27 October 2010). "Twitter tool roots out disguised mass postings". New Scientist.
  47. ^ Keller, Jared (November 10, 2010). "When Campaigns Manipulate Social Media". The Atlantic.
  48. ^ Silverman, Craig (November 4, 2011). "Misinformation Propagation". Columbia Journalism Review.
  49. ^ 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.
  50. ^ Urbina, Ian (August 10, 2013). "I Flirt and Tweet. Follow Me at #Socialbot". The New York Times.
  51. ^ 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.
  52. ^ Menczer, Filippo (November 27, 2016). "Misinformation on social media: Can technology save us?". The Conversation. Retrieved 18 March 2019.
  53. ^ Bergado, Gabe (December 14, 2016). "The Man Who Saw Fake News Coming". Inverse. Retrieved 18 March 2019.
  54. ^ 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. PMC 5715799. PMID 29146827.
  55. ^ Ciampaglia, Giovanni Luca; Menczer, Filippo (June 20, 2018). "Misinformation and biases infect social media, both intentionally and accidentally". The Conversation. Retrieved 18 March 2019.
  56. ^ 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.
  57. ^ 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.
  58. ^ Chong, Zoey (March 14, 2017). "Up to 48 million Twitter accounts are bots, study says". CNET. Retrieved 18 March 2019.
  59. ^ WOJCIK, STEFAN; MESSING, SOLOMON; SMITH, AARON; RAINIE, LEE; HITLIN, PAUL (2018-04-09). "Bots in the Twittersphere". Pew Research Center. Retrieved 18 March 2019.
  60. ^ Gershgorn, Dave (December 21, 2016). "There's a new tool to visualize how fake news is spread on Twitter". Quartz. Retrieved 18 March 2019.
  61. ^ Kauffman, Gretel (December 22, 2016). "Indiana University tech tool 'Hoaxy' shows how fake news spreads". The Christian Science Monitor. Retrieved 18 March 2019.
  62. ^ Skallerup Bessette, Lee (January 9, 2017). "Hoaxy Visualizes the Spread of Online News". The Chronicle of Higher Education. Retrieved 18 March 2019.
  63. ^ Reaney, Patricia (December 21, 2016). "U.S. university launches tool to show how fake news spreads". Reuters. Retrieved 18 March 2019.
  64. ^ 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. PMC 6246561. PMID 30459415.
  65. ^ 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. PMC 5922526. PMID 29702657.
  66. ^ Ouellette, Jennifer (21 November 2018). "Study: It only takes a few seconds for bots to spread misinformation". Ars Technica. Retrieved 18 March 2019.
  67. ^ 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.
  68. ^ de Haldevang, Max (November 20, 2018). "Twitter could have partly blocked Russia's 2016 election attack with CAPTCHAs". Quartz. Retrieved 18 March 2019.
  69. ^ 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.
  70. ^ 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. PMC 8458339. PMID 34552073.
  71. ^ 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.
  72. ^ 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.
  73. ^ 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.
  74. ^ Bhadani, Saumya; Yamaya, Shun; Flammini, Alessandro; Menczer, Filippo; Ciampaglia, Giovanni; Nyhan, Brendan (2022). "Political audience diversity and news reliability in algorithmic ranking". Nature Human Behaviour. 6 (4): 495–505. arXiv:2007.08078. doi:10.1038/s41562-021-01276-5. PMID 35115677. S2CID 220546483.
  75. ^ Menczer, Filippo; Fortunato, Santo; Davis, Clayton (2020). A First Course in Network Science. Cambridge University Press. ISBN 9781108471138.
  76. ^ "OSoMe: Home". Retrieved 18 March 2019.
  77. ^ Hotz, Robert Lee (October 1, 2011). "Decoding Our Chatter". The Wall Street Journal.
  78. ^ "OSoMe Tools". Observatory on Social Media. Retrieved 18 March 2019.
  79. ^ 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.
  80. ^ "Botometer". Retrieved 5 February 2023.
  81. ^ "BotAmp". Retrieved 5 February 2023.
  82. ^ "Hoaxy". Hoaxy. Retrieved 5 February 2023.
  83. ^ "Fakey". Fakey. Retrieved 5 February 2023.
  84. ^ "Scholarometer". Retrieved 18 March 2019.
  85. ^ Kolowich, Steve (December 15, 2009). "Tenure-o-meter". Inside Higher Ed.
  86. ^ Kaur, Jasleen; Diep Thi Hoang; Xiaoling Sun; Lino Possamai; Mohsen JafariAsbagh; Snehal Patil; Filippo Menczer (2012). "Scholarometer: A Social Framework for Analyzing Impact across Disciplines". PLOS ONE. 7 (9): e43235. Bibcode:2012PLoSO...743235K. doi:10.1371/journal.pone.0043235. PMC 3440403. PMID 22984414.
  87. ^ Kaur, Jasleen; Filippo Radicchi; Filippo Menczer (2013). "Universality of scholarly impact metrics". Journal of Informetrics. 7 (4): 924–932. arXiv:1305.6339. Bibcode:2013arXiv1305.6339K. doi:10.1016/j.joi.2013.09.002. S2CID 7415777.
  88. ^ Van Noorden, Richard (November 6, 2013). "Who is the best scientist of them all?". Nature.
  89. ^ "Kinsey Reporter". Retrieved 18 March 2019.
  90. ^ "Kinsey Reporter". Scientific American. Retrieved May 4, 2014.
  91. ^ Healy, Melissa (February 14, 2014). "Want to dish about Valentine's Day sex? There's an app for that". Los Angeles Times.
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