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Blockchain for Social Good: Combating Misinformation on the Web with AI and Blockchain

Published:26 June 2022Publication History

ABSTRACT

The spread of deceptive or misleading information, commonly referred to as misinformation, poses a social, economic, and political threat. Such deceptive information spreads quickly and inexpensively. For example, with the hype around blockchain technologies, misinformation on “get rich quick” scams on the Web is rampant, as evidenced by sophisticated Twitter hacks of celebrities and many social media posts that bait unsuspecting users to visit phishing websites. Unfortunately, AI technologies have contributed to the growing pains of misinformation on the Web, with the advances in technologies such as generative adversarial deep learning techniques that can generate “deep fakes” for nefarious purposes. At the same time, researchers are working on a different set of AI technologies to combat misinformation, akin to “fighting fire with fire.” As there is no clear way to win the online “cat-and-mouse” game against fake news generators and spreaders of misinformation, we believe social media platforms could be fortified with blockchain and AI technologies to mitigate the extent of misinformation propagation in various communities worldwide. Tamper-proof blockchain techniques can provide irrefutable evidence of what content is authentic, guaranteeing how the information has evolved with provenance trails. Various AI models that could be used for detecting fake news can be served on a blockchain for the effective and transparent utility of the model. Such a synergistic combination of AI and blockchain is a burgeoning area of research. This paper outlines a proposal for combining blockchain and AI techniques for handling misinformation on the Web and highlights some of the early ongoing work in this space.

References

  1. David Adler. 2018. Silk Road: The Dark Side of Cryptocurrency. Retrieved April 7, 2022 from https://news.law.fordham.edu/jcfl/2018/02/21/silk-road-the-dark-side-of-cryptocurrencyGoogle ScholarGoogle Scholar
  2. Muhammad Pervez Akhter, Jiangbin Zheng, Farkhanda Afzal, Hui Lin, Saleem Riaz, and Atif Mehmood. 2021. Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media. PeerJ Computer Science 7(2021), e425.Google ScholarGoogle ScholarCross RefCross Ref
  3. Hunt Allcott and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election. Journal of economic perspectives 31, 2 (2017), 211–36.Google ScholarGoogle ScholarCross RefCross Ref
  4. Albert-László Barabási and Eric Bonabeau. 2003. Scale-free networks. Scientific american 288, 5 (2003), 60–69.Google ScholarGoogle Scholar
  5. Juan Benet. 2014. Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561(2014).Google ScholarGoogle Scholar
  6. Abdeljalil Beniiche. 2020. A study of blockchain oracles. arXiv preprint arXiv:2004.07140(2020).Google ScholarGoogle Scholar
  7. Tim Berners-Lee, Robert Cailliau, Ari Luotonen, Henrik Frystyk Nielsen, and Arthur Secret. 1994. The world-wide web. Commun. ACM 37, 8 (1994), 76–82.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Ed Bracho-Polanco. 2019. How Jair Bolsonaro used ‘fake news’ to win power. Retrieved April 17, 2022 from https://theconversation.com/how-jair-bolsonaro-used-fake-news-to-win-power-109343Google ScholarGoogle Scholar
  9. Talha Burki. 2020. The online anti-vaccine movement in the age of COVID-19. The Lancet Digital Health 2, 10 (2020), e504–e505.Google ScholarGoogle ScholarCross RefCross Ref
  10. Priscilla Caplan. 2009. Understanding premis. Library of Congress Washington DC, USA.Google ScholarGoogle Scholar
  11. Carrie Khan. 2022. El Salvador’s leader wants to go in even bigger on bitcoin. Retrieved April 7, 2022 from https://www.npr.org/2022/03/27/1086851329/el-salvadors-leader-wants-to-go-in-even-bigger-on-bitcoinGoogle ScholarGoogle Scholar
  12. Evin Cheikosman, Nadia Hewett, and Karin Gabriel. 2021. Blockchain can help combat the threat of deepfakes. Here’s how. Retrieved April 17, 2022 from https://www.weforum.org/agenda/2021/10/how-blockchain-can-help-combat-threat-of-deepfakes/Google ScholarGoogle Scholar
  13. Qian Chen, Gautam Srivastava, Reza M Parizi, Moayad Aloqaily, and Ismaeel Al Ridhawi. 2020. An incentive-aware blockchain-based solution for internet of fake media things. Information Processing & Management 57, 6 (2020), 102370.Google ScholarGoogle ScholarCross RefCross Ref
  14. Shira Chess and Adrienne Shaw. 2015. A conspiracy of fishes, or, how we learned to stop worrying about# GamerGate and embrace hegemonic masculinity. Journal of Broadcasting & Electronic Media 59, 1 (2015), 208–220.Google ScholarGoogle ScholarCross RefCross Ref
  15. Elizabeth Culliford. 2020. Social media companies distrusted by most Americans on content decisions: Poll. Retrieved December 24, 2020 from https://www.reuters.com/article/us-usa-social-media-poll/social-media-companies-distrusted-by-most-americans-on-content-decisions-poll-idUSKBN23N12NGoogle ScholarGoogle Scholar
  16. Chris Dannen. 2017. Introducing Ethereum and solidity. Vol. 1. Springer.Google ScholarGoogle Scholar
  17. Ronald Denaux and Jose Manuel Gomez-Perez. 2020. Linked credibility reviews for explainable misinformation detection. In International Semantic Web Conference. Springer, 147–163.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Alison Durkee. 2020. Facebook Engineer Resigns, Says Company On ‘Wrong Side Of History’ As Internal Dissent Grows. Article. Retrieved April 21, 2022 from https://www.forbes.com/sites/alisondurkee/2020/09/08/facebook-engineer-resigns-company-on-wrong-side-of-history-internal-employee-dissent-growsGoogle ScholarGoogle Scholar
  19. Ashutosh Dhar Dwivedi, Rajani Singh, Sakshi Dhall, Gautam Srivastava, and Saibal K Pal. 2020. Tracing the source of fake news using a scalable blockchain distributed network. In 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 38–43.Google ScholarGoogle ScholarCross RefCross Ref
  20. Ullrich KH Ecker and Li Chang Ang. 2019. Political attitudes and the processing of misinformation corrections. Political Psychology 40, 2 (2019), 241–260.Google ScholarGoogle ScholarCross RefCross Ref
  21. Enrique Estellés-Arolas and Fernando González-Ladrón-de Guevara. 2012. Towards an integrated crowdsourcing definition. Journal of Information science 38, 2 (2012), 189–200.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. ET Online. 2022. Countries which have banned or restricted use of cryptocurrency. Retrieved April 7, 2022 from https://economictimes.indiatimes.com/news/web-stories/countries-which-have-banned-or-restricted-use-of-cryptocurrency/slideshow/89153960.cmsGoogle ScholarGoogle Scholar
  23. Shuhui Fan, Shaojing Fu, Haoran Xu, and Xiaochun Cheng. 2021. Al-SPSD: Anti-leakage smart Ponzi schemes detection in blockchain. Information Processing & Management 58, 4 (2021), 102587.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sean Foley, Jonathan R Karlsen, and Tālis J Putniņš. 2019. Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?The Review of Financial Studies 32, 5 (2019), 1798–1853.Google ScholarGoogle Scholar
  25. Paula Fraga-Lamas and Tiago M Fernandez-Carames. 2020. Fake news, disinformation, and deepfakes: Leveraging distributed ledger technologies and blockchain to combat digital deception and counterfeit reality. IT Professional 22, 2 (2020), 53–59.Google ScholarGoogle ScholarCross RefCross Ref
  26. Sheera Frenkel. 2018. Facebook to Remove Misinformation That Leads to Violence. Retrieved December 24, 2020 from https://www.nytimes.com/2018/07/18/technology/facebook-to-remove-misinformation-that-leads-to-violence.htmlGoogle ScholarGoogle Scholar
  27. Adrien Friggeri, Lada Adamic, Dean Eckles, and Justin Cheng. 2014. Rumor cascades. In proceedings of the international AAAI conference on web and social media, Vol. 8. 101–110.Google ScholarGoogle ScholarCross RefCross Ref
  28. Jeffrey Gottfried and Elisa Shearer. 2017. Americans’ online news use is closing in on TV news use. Pew Research Center 7(2017).Google ScholarGoogle Scholar
  29. D Graves. 2018. Understanding the promise and limits of automated fact-checking. (2018).Google ScholarGoogle Scholar
  30. Andrew Guess, Jonathan Nagler, and Joshua Tucker. 2019. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science advances 5, 1 (2019), eaau4586.Google ScholarGoogle Scholar
  31. Haya R Hasan and Khaled Salah. 2019. Combating deepfake videos using blockchain and smart contracts. Ieee Access 7(2019), 41596–41606.Google ScholarGoogle ScholarCross RefCross Ref
  32. Jeff Horwitz and Deepa Seetharaman. 2020. Facebook Executives Shut Down Efforts to Make the Site Less Divisive: The social-media giant internally studied how it polarizes users, then largely shelved the research. Retrieved December 24, 2020 from https://www.wsj.com/articles/facebook-knows-it-encourages-division-top-executives-nixed-solutions-11590507499Google ScholarGoogle Scholar
  33. Jon Huang, Claire O’Neill, and Hiroko Tabuchi. 2021. Bitcoin Uses More Electricity Than Many Countries. How Is That Possible?Retrieved April 7, 2022 from https://www.nytimes.com/interactive/2021/09/03/climate/bitcoin-carbon-footprint-electricity.htmlGoogle ScholarGoogle Scholar
  34. Steve Huckle and Martin White. 2017. Fake news: A technological approach to proving the origins of content, using blockchains. Big data 5, 4 (2017), 356–371.Google ScholarGoogle Scholar
  35. Daniel Kazenoff, Oshani Seneviratne, and Deborah L McGuinness. 2020. Semantic Graph Analysis to Combat Cryptocurrency Misinformation on the Web.. In ASLD@ ISWC. 168–176.Google ScholarGoogle Scholar
  36. Mary C Lacity. 2021. Fake news, technology and ethics: Can AI and blockchains restore integrity?Journal of Information Technology Teaching Cases (2021), 2043886921999065.Google ScholarGoogle Scholar
  37. Nicole Lapin. 2021. Explaining Crypto’s Volatility. Retrieved April 7, 2022 from https://www.forbes.com/sites/nicolelapin/2021/12/23/explaining-cryptos-volatilityGoogle ScholarGoogle Scholar
  38. Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jia-Nan Liu, Yang Xiang, and Robert H Deng. 2018. CrowdBC: A blockchain-based decentralized framework for crowdsourcing. IEEE Transactions on Parallel and Distributed Systems 30, 6 (2018), 1251–1266.Google ScholarGoogle ScholarCross RefCross Ref
  39. Yuan Lu, Qiang Tang, and Guiling Wang. 2018. Zebralancer: Private and anonymous crowdsourcing system atop open blockchain. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, 853–865.Google ScholarGoogle ScholarCross RefCross Ref
  40. MacKenzie Sigalos. 2022. Russia is considering selling its oil and gas for bitcoin as sanctions intensify from the West. Retrieved April 7, 2022 from https://www.cnbc.com/2022/03/24/russia-might-take-bitcoin-as-payment-for-oil-and-gas-as-sanctions-rise.htmlGoogle ScholarGoogle Scholar
  41. Yisroel Mirsky and Wenke Lee. 2021. The creation and detection of deepfakes: A survey. ACM Computing Surveys (CSUR) 54, 1 (2021), 1–41.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Global Policy Management) Monika Bickert (Vice President. 2020. Enforcing Against Manipulated Media. Retrieved December 24, 2020 from https://about.fb.com/news/2020/01/enforcing-against-manipulated-mediaGoogle ScholarGoogle Scholar
  43. Satoshi Nakamoto. 2008. Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review(2008), 21260.Google ScholarGoogle Scholar
  44. New York State Department of Financial Services. 2020. Twitter Investigation Report: Report on Investigation of Twitter’s July 15, 2020 Cybersecurity Incident and the Implications for Election Security. Article. Retrieved April 21, 2022 from https://www.dfs.ny.gov/Twitter_ReportGoogle ScholarGoogle Scholar
  45. Thanh Thi Nguyen, Quoc Viet Hung Nguyen, Cuong M Nguyen, Dung Nguyen, Duc Thanh Nguyen, and Saeid Nahavandi. 2019. Deep learning for deepfakes creation and detection: A survey. arXiv preprint arXiv:1909.11573(2019).Google ScholarGoogle Scholar
  46. Jack Nicas. 2020. YouTube Cut Down Misinformation. Then It Boosted Fox News. Retrieved December 24, 2020 from https://www.nytimes.com/2020/11/03/technology/youtube-misinformation-fox-news.htmGoogle ScholarGoogle Scholar
  47. Eli Pariser. 2011. The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Gordon Pennycook and David G Rand. 2019. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188(2019), 39–50.Google ScholarGoogle ScholarCross RefCross Ref
  49. Adnan Qayyum, Junaid Qadir, Muhammad Umar Janjua, and Falak Sher. 2019. Using blockchain to rein in the new post-truth world and check the spread of fake news. IT Professional 21, 4 (2019), 16–24.Google ScholarGoogle ScholarCross RefCross Ref
  50. Pooja Reddy. 2021. Could We Fight Misinformation With Blockchain Technology?Retrieved April 17, 2022 from https://www.nytimes.com/2020/07/06/insider/could-we-fight-misinformation-with-blockchain-technology.htmlGoogle ScholarGoogle Scholar
  51. Tony Romm, Rachel Lerman, Cat Zakrzewski, Heather Kelly, and Elizabeth Dwoskin. 2020. Facebook, Google, Twitter CEOs clash with Congress in pre-election showdown. Retrieved December 24, 2020 from https://www.washingtonpost.com/technology/2020/10/28/twitter-facebook-google-senate-hearing-live-updatesGoogle ScholarGoogle Scholar
  52. Nejc Rožman, Marko Corn, Gašper Škulj, Janez Diaci, and Lovro Šubelj. 2021. Emergence of a scale-free network topology in a blockchain-based Shared Manufacturing. In 2021 Third International Conference on Blockchain Computing and Applications (BCCA). IEEE, 172–178.Google ScholarGoogle ScholarCross RefCross Ref
  53. Adam Satariano and Milan Schreuer. 2018. Facebook’s Mark Zuckerberg Gets an Earful From the E.U.Retrieved April 7, 2022 from https://www.nytimes.com/2018/05/22/technology/facebook-eu-parliament-mark-zuckerberg.htmlGoogle ScholarGoogle Scholar
  54. Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter 19, 1 (2017), 22–36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Jack Stubbs and Christopher Bing. 2020. Facebook, Twitter dismantle global array of disinformation networks. Retrieved December 24, 2020 from https://www.reuters.com/article/cyber-disinformation-facebook-twitter/facebook-twitter-dismantle-global-array-of-disinformation-networks-idINKBN26T2XFGoogle ScholarGoogle Scholar
  56. Andon Tchechmedjiev, Pavlos Fafalios, Katarina Boland, Malo Gasquet, Matthäus Zloch, Benjamin Zapilko, Stefan Dietze, and Konstantin Todorov. 2019. ClaimsKG: a knowledge graph of fact-checked claims. In International Semantic Web Conference. Springer, 309–324.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Fabian Teichmann. 2020. Recent trends in money laundering. Crime, Law and Social Change 73, 2 (2020), 237–247.Google ScholarGoogle ScholarCross RefCross Ref
  58. Clive Thompson. 2020. YouTube’s Plot to Silence Conspiracy Theories: From flat-earthers to QAnon to Covid quackery, the video giant is awash in misinformation. Can AI keep the lunatic fringe from going viral?Retrieved December 24, 2020 from https://www.wired.com/story/youtube-algorithm-silence-conspiracy-theoriesGoogle ScholarGoogle Scholar
  59. Joe Tidy. 2022. Hackers helped me find my lost Bitcoin fortune. Retrieved April 7, 2022 from https://www.bbc.com/news/technology-60318946Google ScholarGoogle Scholar
  60. Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. 2020. Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion 64(2020), 131–148.Google ScholarGoogle ScholarCross RefCross Ref
  61. Andrew Urquhart and Brian Lucey. 2022. Crypto and digital currencies—nine research priorities.Google ScholarGoogle Scholar
  62. Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146–1151.Google ScholarGoogle Scholar
  63. Mika Westerlund. 2019. The emergence of deepfake technology: A review. Technology Innovation Management Review 9, 11 (2019).Google ScholarGoogle ScholarCross RefCross Ref
  64. Xiaolong Xu, Qingxiang Liu, Xuyun Zhang, Jie Zhang, Lianyong Qi, and Wanchun Dou. 2019. A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Transactions on Computational Social Systems 6, 6 (2019), 1407–1419.Google ScholarGoogle ScholarCross RefCross Ref
  65. Abbas Yazdinejad, Reza M Parizi, Gautam Srivastava, and Ali Dehghantanha. 2020. Making sense of blockchain for ai deepfakes technology. In 2020 IEEE Globecom Workshops (GC Wkshps. IEEE, 1–6.Google ScholarGoogle ScholarCross RefCross Ref
  66. Saide Zhu, Zhipeng Cai, Huafu Hu, Yingshu Li, and Wei Li. 2019. zkCrowd: a hybrid blockchain-based crowdsourcing platform. IEEE Transactions on Industrial Informatics 16, 6 (2019), 4196–4205.Google ScholarGoogle ScholarCross RefCross Ref
  67. Arkaitz Zubiaga, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Peter Tolmie. 2016. Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS one 11, 3 (2016), e0150989.Google ScholarGoogle ScholarCross RefCross Ref

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          cover image ACM Conferences
          WebSci '22: Proceedings of the 14th ACM Web Science Conference 2022
          June 2022
          479 pages
          ISBN:9781450391917
          DOI:10.1145/3501247

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          • Published: 26 June 2022

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