Can WhatsApp benefit from debunked fact-checked stories to reduce misinformation?

WhatsApp was alleged to be widely used to spread misinformation and propaganda during elections in Brazil and India. Due to the private encrypted nature of the messages on WhatsApp, it is hard to track the dissemination of misinformation at scale. In this work, using public WhatsApp data, we observe that misinformation has been largely shared on WhatsApp public groups even after they were already fact-checked by popular fact-checking agencies. This represents a significant portion of misinformation spread in both Brazil and India in the groups analyzed. We posit that such misinformation content could be prevented if WhatsApp had a means to flag already fact-checked content. To this end, we propose an architecture that could be implemented by WhatsApp to counter such misinformation. Our proposal respects the current end-to-end encryption architecture on WhatsApp, thus protecting users' privacy while providing an approach to detect the misinformation that benefits from fact-checking efforts.

[1]  Fast hash table lookup using extended bloom filter: an aid to network processing , 2005, SIGCOMM.

[2]  Haoyu Song,et al.  Fast hash table lookup using extended bloom filter: an aid to network processing , 2005, SIGCOMM '05.

[3]  Min Wu,et al.  Robust and secure image hashing , 2006, IEEE Transactions on Information Forensics and Security.

[4]  B. Nyhan,et al.  When Corrections Fail: The Persistence of Political Misperceptions , 2010 .

[5]  Christoph Zauner,et al.  Implementation and Benchmarking of Perceptual Image Hash Functions , 2010 .

[6]  Peeter Jürviste.  Fast Hash Table Lookup Using Extended Bloom Filter , 2011 .

[7]  Bruria Adini,et al.  Kidnapping WhatsApp - Rumors during the search and rescue operation of three kidnapped youth , 2016, Comput. Hum. Behav..

[8]  M. Gentzkow,et al.  Social Media and Fake News in the 2016 Election , 2017 .

[9]  Karthik Bhat,et al.  WhatsApp for Monitoring and Response during Critical Events: Aggie in the Ghana 2016 Election , 2017, ISCRAM.

[10]  Venkata Rama Kiran Garimella,et al.  WhatsApp, Doc? A First Look at WhatsApp Public Group Data , 2018, ICWSM 2018.

[11]  Hany Farid,et al.  Reining in Online Abuses , 2018 .

[12]  Jonathan A. Busam,et al.  Real Solutions for Fake News? Measuring the Effectiveness of General Warnings and Fact-Check Tags in Reducing Belief in False Stories on Social Media , 2020, Political Behavior.

[13]  Venkata Rama Kiran Garimella,et al.  Can WhatsApp Counter Misinformation by Limiting Message Forwarding? , 2019, COMPLEX NETWORKS.

[14]  Chinmayi Arun,et al.  On WhatsApp, Rumours, Lynchings, and the Indian Government , 2019 .

[15]  Ethan Porter,et al.  The Elusive Backfire Effect: Mass Attitudes’ Steadfast Factual Adherence , 2019 .

[16]  Fabrício Benevenuto,et al.  (Mis)Information Dissemination in WhatsApp: Gathering, Analyzing and Countermeasures , 2019, WWW.

[17]  Joan Donovan,et al.  Deepfakes and cheap fakes , 2019 .

[18]  Jussara M. Almeida,et al.  WhatsApp Monitor: A Fact-Checking System for WhatsApp , 2019, ICWSM.

[19]  Emilio Ferrara,et al.  What types of COVID-19 conspiracies are populated by Twitter bots? , 2020, First Monday.

[20]  Dean Eckles,et al.  Images and Misinformation in Political Groups: Evidence from WhatsApp in India , 2020, ArXiv.

[21]  How fake news about coronavirus became a second pandemic. , 2020, Nature.

[22]  Dean Eckles,et al.  A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections , 2020, ICWSM.