WhatsApp Tiplines and Multilingual Claims in the 2021 Indian Assembly Elections
WhatsApp tiplines, first launched in 2019 to combat misinformation, enable users to interact with fact-checkers to verify misleading content. This study analyzes 580 unique claims (tips) from 451 users, covering both high-resource languages (English, Hindi) and a low-resource language (Telugu) during the 2021 Indian assembly elections using a mixed-method approach. We categorize the claims into three categories, election, COVID-19, and others, and observe variations across languages. We compare content similarity through frequent word analysis and clustering of neural sentence embeddings. We also investigate user overlap across languages and fact-checking organizations. We measure the average time required to debunk claims and inform tipline users. Results reveal similarities in claims across languages, with some users submitting tips in multiple languages to the same fact-checkers. Fact-checkers generally require a couple of days to debunk a new claim and share the results with users. Notably, no user submits claims to multiple fact-checking organizations, indicating that each organization maintains a unique audience. We provide practical recommendations for using tiplines during elections with ethical consideration of users' information.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsApp
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 con…
MisinformationSynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks
Diaspora communities are disproportionately impacted by off-the-radar misinformation and often neglected by mainstream fact-checking efforts, creating a critical need to scale-up efforts of nascent fact-checking initiati…
ChatbotDataset GenerationFact CheckingMisinformationRecurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text
This paper describes Centre for Development of Advanced Computing's (CDACM) submission to the shared task-'Tool Contest on POS tagging for Code-Mixed Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocate…
Language ModelingLanguage ModellingPOSPOS Tagging+1"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups
WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it …
PACO: Provocation Involving Action, Culture, and Oppression
In India, people identify with a particular group based on certain attributes such as religion. The same religious groups are often provoked against each other. Previous studies show the role of provocation in increasing…
Cultural Vocal Bursts Intensity Prediction