Research note: Tiplines to uncover misinformation on encrypted platforms: A case study of the 2019 Indian general election on WhatsApp

Abstract

There is currently no easy way to discover potentially problematic 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 compared the tips sent to a WhatsApp tipline run during the 2019 Indian general election with the messages circulating in large, public groups on WhatsApp and other social media platforms during the same period. We found 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 potentially misleading content.

Misinformation in India's 2019 National Election

“…New developments in crowdsourcing misinformation may mean citizens can help to stem the tide of misinformation in the future (Kazemi et al, 2021). For example, the enthusiastic responses from users to the WhatsApp tipline in 2019, with tens of thousands of requests by users to have messages fact-checked clearly reflects that not everyone believes the information they see on WhatsApp.…”

“…The project was organized by PROTO-a media research and training organization-and users could submit WhatsApp messages to be fact-checked during the 2019 campaign. Kazemi et al (2021) compare data from WhatsApp users using this tipline with messages circulating in the large public groups mentioned above by Garimella & Eckles (2020), during the 2019 election campaign, and concluded "tiplines can be an effective source for discovering content to fact-check" (Kazemi et al, 2021, p. 1). The study found a total of 1,945 unique messages related to the election, of which the largest cluster included 213 unique messages with misinformation telling voters to ask for a "challenge vote" or "tender vote" if they were not on the voter list or marked as already voting, which was circulated in many languages and reported by the fact-checking team at The Times of India (Kazemi et al, 2021, p. 7).…”

“…Although Panda et al (2020) examine Twitter activity during India's 2019 national election, they did not study misinformation. Scholars have creatively explored the characteristics of misinformation on WhatsApp in India, which is discussed further below (Garimella & Eckles, 2020;Kazemi et al, 2021). However, the dissemination and characteristics of misinformation on large social media platforms such as Facebook India and the use of affordances provided by the platform that signal engagement with misinformation in 2019, has not been studied to the best of my knowledge.…”

Social and Cognitive Aspects of the Vulnerability to Political Misinformation

“…Fact‐checking can reduce gullibility towards misinformation (Porter & Wood, 2021), and meta‐analyses reveal that corrections are partly, albeit significantly, effective in reducing people's misperceptions (Chan et al, 2017; Walter & Tukachinsky, 2020), despite the fact that corrections on political misinformation appear to have a weaker effect than correction on, for example, health issues (Walter & Murphy, 2018); they are clearly better than nothing. Given that the closer in time the misinformation and its correction are processed, the better the correction works (Walter & Tukachinsky, 2020), advances in “claim matching” to automatically match repeated instances of misinformation with already published fact‐checks (Kazemi, Garimella, Gaffney, et al, 2021; Shaar et al, 2020) are noteworthy, especially in the context of misinformation tip lines (Kazemi, Garimella, Shahi, et al, 2021). Warning tags against misinformation also appear to be a better‐than‐nothing solution (Walter et al, 2020).…”

Overview of the CLEF–2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection

“…The five editions of the lab have been held in 2018-2022, targeting various natural language processing and information retrieval tasks related to factuality [11,12,24,25,61,62,65,66]. The aim is to develop systems that can be useful as supportive technology for investigative journalism, as they could provide help and guidance, thus saving time [30,35,37,42,63,76,97]. For example, a system could automatically identify check-worthy claims, make sure they have not been fact-checked already by a reputable fact-checking organization, and then present them to a journalist for further analysis in a ranked list [83].…”