Characterising User Content on a Multi-Lingual Social Network

Abstract

Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multicultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages. We investigate the cross lingual dynamics by clustering visually similar images together, and exploring how they move across language barriers. We find that Telugu, Malayalam, Tamil and Kannada languages tend to be dominant in soliciting political images (often referred to as memes), and posts from Hindi have the largest cross-lingual diffusion across ShareChat (as well as images containing text in English). In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility. That said, we find cases where the same image is associated with very different text (and therefore meanings). This initial characterisation paves the way for more advanced pipelines to understand the dynamics of fake and political content in a multi-lingual and non-textual setting.

Under the Spotlight: Web Tracking in Indian Partisan News Websites

“…Vernacular diversity: Our dataset was primarily focused on websites using English language (76/103 English, with 14/103 in Hindi and 13/103 in regional languages). Multilingual online users consist of a large portion in India (Agarwal et al 2020a). However, the diversity of languages in this country (apart from Hindi and English, India has 22 scheduled languages and several state-based official languages) raises the question: Are the patterns of tracking similar or different among regional Indian News websites?…”

Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

“…Several studies have explored messaging patterns within other community mediums, e.g., Reddit [45], 4chan [8,20] and IRC [42]. Although there also have been studies of junk sending on social media [15,46,49], email [12] and SMS [36], junk has not yet been studied on WhatsApp except for anecdotal observations [1,3,10,35,47]. These studies have largely focussed on qualitative methodologies, e.g., interviews, surveys, focus groups.…”

“…To understand the nature of such misbehaviour we focus on India, a country where over 400 of the 460 million people online are on WhatsApp. 1 Given the population we are studying (mostly first time Internet users from India), the susceptibility of believing the content in junk message (e.g., fake job offers) is high. As a common topic that attracts interest from across the population (all languages and states), we focus on national politics, and gather 2.6 million messages from 5,051 public political WhatsApp groups in India ( §2) which are posted before, during and after the elections in 2019.…”

Computational Analysis of Bot Activity in the Asia-Pacific: A Comparative Study of Four National Elections

“…Implemented empirical investigations, however, appear much more scant. During the 2019 Indian elections, extensive analysis is conducted into the online dissemination of political content, but bot activity does not explicitly feature in this analysis (Agarwal et al 2020). In Sri Lanka, some bot activity is linked to the 2015 elections, but studied only in relation to a small dataset of about 2000 users (Rathnayake and Buente 2017).…”