Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsApp
Not on the map. Why?
Abstract (arXiv)
There is currently no easy way to fact-check content on WhatsApp and other end-to-end encrypted platforms at scale. In this paper, we analyze the usefulness of a crowd-sourced "tipline" through which users can submit content ("tips") that they want fact-checked. We compare the tips sent to a WhatsApp tipline run during the 2019 Indian national elections with the messages circulating in large, public groups on WhatsApp and other social media platforms during the same period. We find that tiplines are a very useful lens into WhatsApp conversations: a significant fraction of messages and images sent to the tipline match with the content being shared on public WhatsApp groups and other social media. Our analysis also shows that tiplines cover the most popular content well, and a majority of such content is often shared to the tipline before appearing in large, public WhatsApp groups. Overall, our findings suggest tiplines can be an effective source for discovering content to fact-check.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 8 Jun 2021.
Builds on
14 of 40 references.
References · 14
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
- 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
- 2020That is a Known Lie: Detecting Previously Fact-Checked Claimscited by 82
- 2020FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19cited by 60
- 2018Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detectioncited by 55
- 2021Claim Matching Beyond English to Scale Global Fact-Checkingcited by 42
- 2020An Exploratory Study of COVID-19 Misinformation on Twittercited by 33
- 2020Images and Misinformation in Political Groups: Evidence from WhatsApp in Indiacited by 11
- 2020AMUSED: An Annotation Framework of Multi-modal Social Media Datacited by 7
- 2020Text Similarity Using Word Embeddings to Classify Misinformationcited by 2
2 more
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.